Shaping EU agriculture, rural areas and wealth for the next decades

Common Agricultural Policy

Shaping EU agriculture, rural areas and wealth for the next decades

Main outcomes of the 2019 Global Food Forum

The Common Agricultural Policy was founded to respond to the challenge of food sovereignty in Europe, on the basis of the fact that a pooling of financial means and the definition of common political guidelines are more effective than the sum of potentially divergent national initiatives.

Food security remains today the prerequisite for an area to have a strong and credible political strategy in today’s multipolar world.

In a globalized world where food security for all should be guaranteed, the European Union is also responsible for ensuring the sustainability of a stable presence in global food markets.

At the same time, the CAP has had to evolve to respond more to the challenges of vitality in Europe’s rural areas and to the preservation of the environment and the fight against climate change.

As a basis for developing rural areas, managing 75% of Europe and our natural resources, European agriculture is a key factor and farmers are the only reliable relays for effective action.

Today, the CAP must aim to meet the objectives of:

Ensuring the food security of Europeans and enabling European citizens to have access to quality food at affordable prices
Preserving the environment, soil, air and water while contributing to the fight against climate change.
Ensuring a correct level of income for farmers even as the volatility of agricultural markets have increased considerably since the mid-2000s under the combined effect of climate change and globalization
Maintaining a strong agricultural network, a basis of territorial development of agro-food activities and local economic development in all European regions

Keeping the European Union as the world’s largest exporter – the EU is also the world’s largest importer of agricultural and food goods,

The challenge for those who have to decide on future European policies having an impact on agriculture and rural areas of the European Union, is not only to reconcile societal expectations and economic challenges but to put them in synergy by focusing the CAP on the challenge of the dual performance of our agriculture: no economic benefit without more environmental protection, no environmental protection without economic benefit.

For this, the CAP must become again an investment policy for the future of the European Union and center its actions on:

the investments and innovations in farms and the food chain; agricultural sectors need to seize digital opportunities quickly both for their relationships with consumers and their economic and environmental performance.
the incentive for a transition of European agriculture towards dual performance agricultural systems,
securing European farmers against risks and crisis by combining basic direct aid, support for climate risk insurance tools and mutual funds for income stabilization and an effective European crisis management reserve
the promotion of a quality European food model, diversified for all European citizens; and fight against the risk of a two tiers’ nutrition in Europe stratified on social classes.CAP reform must as well be the ground of the “farm to fork” strategy the President of the Commission is willing to develop within a new green deal for Europe. Europe’s agriculture is not only a question of food for European citizens, but it is as well a provider of bio-energy, biochemistry, textile…. and farmers are the only ones able to manage properly our environment in rural areas.Thus, the CAP reform should be designed as to develop such a strategy, to implement the core of an efficient green deal when it comes to farming, managing rural areas, shaping a balanced food chain and putting nutrition and good food at the very heart of Europeans’ habits.

CAP reform’s negotiations

8 ACTIONS TO BE TAKEN

Given the societal, economic and environmental challenges to be met, the European Union needs a CAP that is strong, effective, common and adapted to the realities of a European Union rich in its diversity.

More specifically, it is most needed to:

put in place relevant key parameters in basic acts at a common level to preserve the C of the CAP and ensure fairness for farmers wherever they are in the EU and especially concerning support they will receive under the 1st pillar.
give a European environmental dimension with a clear definition and an effective European baseline of the Eco-scheme, concentrating the measures to be proposed by the Member States on the promotion of transition to virtuous agri-systems and to incentivize innovative tools and practices able to encourage the environmental and economic transition of European agriculture. The definition of the Eco-scheme needs to be further specified as a tool to incentive transition of EU agriculture towards carbon neutrality. Per hectare financial incentive should be paid to farmers already implementing virtous agri-systems or engaging in a transition within the next 7 years by implementing systems such as precision agriculture or conservation.
promote the economic dimension of the CAP altogether with its societal and environmental dimension. Comagri’s position of a minimum financial objective (30 % of 2nd pillar funding) for economic and environmental performance investments and for risk management tools in the 2nd pillar should be kept.
provide environmental basis for the new conditionality for a European reference, with the possibility for Member States and farmers to propose equivalent measures where it is more appropriate. The common nature of the CAP does not preclude useful flexibilities. On the contrary, it allows them and makes them easily manageable by farmers and Member States. Without a precise common basis, the Commission power to judge the relevance of the ambition of national strategic plans would be unenforceable as it is not legally based. Without a common reference, the least environmentally ambitious would strengthen the economic competitiveness to the detriment of those working really for the success of the new European Green Deal.

build an effective and well-financed crisis reserve with guarantees that the European Commission will react without delay in the event of serious market disruption. To build on the progress made to support income stabilization tools and climatic insurance (Financial omnibus 2017), the new CAP should integrate a new effective crisis reserve with a two-fold mission: to quickly finance, in the event of a crisis, exceptional market measures and intervention measures, as well as to automatically take over risk management measures of the income stabilization tools (IST), as soon as indicators have reached pre-defined thresholds, Failure to do so would risk checking the progress achieved through the Financial omnibus, and render it ineffective as the intensity of extreme crisis would jeopardize the efforts of the sectors having implemented volontary IST to better protect themselves. In that context; the current reserve fund has to be adapted, building on its 400 million euros allocation to reach 1.5 billion euros.
set up a European scheme for €20 Billion Green Investments in Agriculture. The EU has pledged to reduce its greenhouse gas emissions by 40% by 2030. Also reducing water pollution, soil erosion and preserving biodiversity are non-negotiable objectives. However, the EU agriculture sector is facing severe headwinds. Farmers’ incomes have stagnated, and according to the European Commission forecasts they are bound to drop by a staggering 14% (in real terms) in the next decade, taking into account the European Commission initial Common Agriculture Policy reform proposals. Moreover, the sector faces increased international competition and without significant productivity gains exports of agri-food products will suffer. Better farming systems that efficiently use nutrient resources exist, enhancing not only soil carbon but also biodiversity and improving resilience of farming to climate change itself. These systems typically increase productivity, reduce input needs, and other environmental pressures such as eutrophication and air pollution. One of the key EU problems is the lack of investment, in particular on investment that reduces the environmental footprint whilst increasing productivity and incomes. The adoption of new technologies is slow, and the EU is lagging behind its main competitors in smart and precision farming.The EU needs a “Marshall Plan” to encourage its farmers to shift to more virtuous agricultural systems as mentioned above through an ambitious program for investing in a double performance (environmental and economic) farming system. In that context, in parallel of a recrafted new Eco-scheme and a more balanced Pillar II as proposed by Comagri, it would be highly relevant to set up a European scheme for green investments in Agriculture to co-finance (50%) investments of double performance in farms which bring at least 15% reduction of use of inputs. This new European scheme for green investments in Agriculture would generate and support 20 billion euros of investments of transition to double performance within the period, allowing massive positive impact on environment and increase of EU agriculture competitiveness. To finance this scheme a small share of CAP budget should be mobilized in each Member state (3 %).
design a truly simple and well-managed CAP: Keeping the C of the CAP while simplifying its management. The CAP reform proposed by the former Commission is centered on a new governance that is quite debatable. It shouldn’t confuse people. Whatever the administrative management of the CAP will be, the main goal is to define a new CAP which will bring more sustainability and more performance to our European agriculture and to rural areas.

With its proposal of CAP reform, the Commission wants to encourage the Member States, when drafting their national agricultural (political) strategies, to make more consistent use of the support of the first pillar and the second pillar (rural development) of the CAP, and move from a ‘compliance policy’ to a ‘performance policy’ based on achievements and outcome indicators.

As underlined by Comagri in its report, while these two objectives are laudable however, the present proposal does not make it possible to achieve any of them: the first will be subject to the good will and priorities of various governments, the second is limited to only counting the number of hectares or farmers concerned by such or such a measure without their impact being otherwise assessed. We should be in a position to measure both the economic and the environmental impact of the implemented measures within the framework of the CAP and not – as proposed by the Commission – to only collect statistics.

Without clear baselines and requirements defined by the co-legislators at EU level, the Commission would not be in a position to ensure respect of an equivalent level of ambition between the 27 different national strategies, having no legal ground to not approve the strategies proposed even if their ambition is far from what should be expected.
Moreover, the Commission’s proposal foresees that Member states would define their own rules on controls, the Commission auditing only that the MS have reached the targets they have defined in their national strategic plans in terms of uptake of foreseen measures. Whereas the current CAP is recognized as very well managed with less than 3% error, EU taxpayers wouldn’t be able to know in the future whether the CAP spending is managed efficiently or not, whether its management reaches high EU standards or instead only various national or regional ones.

Instead, it is necessary to rebase the management of the CAP on:

a compliance with the European rules for which each actor fully assumes its prerogatives and responsibilities: the Member States in the implementation with the farmers, the national agencies of certification in the assurance of the good management and the respect of the European rules, the European Commission in the control of the agencies of certification and in taking into account their evaluation, without double controls at the level of the farmers or the payment agencies.
an effective assessment of the impact of the CAP at EU level and in each Member state, notably on environment, using few efficient impact indicators able to measure the achievements on carbon sequestration, methane and nitrite emissions and on biodiversity.

8. contribute to better tackle the challenge on nutrition and food habits. Facing challenges on nutrition and increase of obesity in the EU, we need to better understand the behaviour of European consumers and their expectations. Member States have tested different legislative tools or sectors’ guides of good pratices. Actually, if none has made a difference, some have put at risk the common dimension of the single market.

The CAP, through its CMOs regulation, proposes to support some limited initiatives where agriculture and nutrition are interlinked, namely the Schools’ fruits and vegetables scheme and the Schools milk scheme.

Building on the acquired experience, it would be relevant to go one step further and deploy a more integrated approach of nutrition and agriculture by co-funding public and private programs of nutrition in schools, developing children’ taste in food (unprocessed products), children’s skill and pleasure in cooking and increasing their understanding and attraction to better nutrition and by developing science based communication actions on that issue. These action have to be developed as long term ones all across the EU, mobilizing all relevant EU tools.

Indicators assessing the economic and environmental performance of EU agriculture in the framework of the CAP

December 2019

Introduction

For 30 years, the environment has been an integrated part of the Common Agricultural Policy (CAP) and today it has become one of the main dimensions of this policy.

A “greening” measure was introduced during the 2013 reform to strengthen the environmental and climate performance of the CAP. The conclusions on the results of this measure are mixed, pointing to the lack of clarity of its objectives and the need to establish quantified objectives.

In this context, the post-2020 CAP reform proposes a new “Ecoscheme” tool to replace this greening measure. This tool is intended to drive a transition to more sustainable agriculture, ensuring both economic and environmental performance. In response to the criticisms made about the greening measure, it is needed a solid and quantified monitoring of the impact of this plan at farm level, using simple tools.

Many indicators attempt to assess the performance of the CAP at European level. However, the latter give an administrative image of the implementation of the CAP by monitoring the number of hectares or farms involved in different measures, without being able to assess the environmental or economic benefits of these measures.

Thus, what indicators should be put in place to quantitatively assess the impact of the Ecoscheme plan on the economic and environmental performance of farms, in a simple and rapid way?

In response to this problem, and in order to enrich the European Commission’s reflections, the think tank Farm Europe proposes a set of indicators. This structure is described in the first part.

The need to put in place indicators to measure the achievement of economic and environmental objectives is also explained.

In order to set up such indicators, the concepts of economic and environmental performance are defined followed by a presentation of the methodology applied to identify the indicators.

Finally, for each environmental or economic element to be assessed, the construction of the corresponding indicator is detailed. The elements measured are justified, the adaptation of existing indicators is explained. The equations to measure these elements are presented, as well as the data needed to feed them.

Lastly, the management, limitations and perspectives of this set of indicators assessing the impact of the CAP and future ecoschemes are discussed.

full study available on Farm Europe Members Area (in french)

Economic and environmental performance of digital agriculture

December 2019

Introduction

In response to environmental and climate challenges and societal expectations, the post-2020 CAP project proposes to raise its environmental ambitions to facilitate and encourage the consideration of these issues in agricultural practices. A balance between the economic and environmental dimensions is essential to ensure stability and profitability for farmers and to establish a sustainable CAP (Jereb et al., 2017; COMAGRI, 2019).

The Ecoscheme plan is a new tool proposed in the post-2020 CAP reform that is intended to be a driving force in the transition to a more sustainable agriculture (COMAGRI, 2019). It replaces the greening of income support introduced in the 2013 reform. Like the greening measures, the Ecoscheme plan makes it possible to devote a share of direct payments to environmental and climate protection (COMAGRI, 2019).

The content of the Ecoscheme plan has the dual objective of ensuring unity of action and ambition while being flexible enough to respond to local issues. One idea to achieve this goal would be to stimulate the transition to more sustainable agricultural systems.

Faced with recurrent reproaches concerning the lack of quantification of the effects of the CAP, simple policy tools offering a solid follow-up of results across Europe are desired, both by actors in the agricultural world and by European decision-makers.

The link between agriculture and the environment should be maintained and strengthened, in continuity with previous CAPs. The new tools available to agriculture claim to ensure economic and environmental results. If this is the case, they would make it possible to achieve this dual economic and environmental performance. More widespread, they could ensure an accurate assessment of the effect of the policies put in place.

Thus, faced with the challenge of defining a European framework for Ecoscheme and of implementing it with concrete results, the aim of this study is to assess the economic and environmental performance of the different agricultural systems with and without recourse to precision farming.

1. BACKGROUND

1.1. The Common Agricultural Policy, CAP

1.1.1. An environmentally friendly CAP

1.1.2. Current CAP

1.1.3. Greening objectives

1.1.4. The results of this measure

1.2. Issues to be addressed in the reform of the future CAP 2021-2027

1.1.5. Environmental issues

1.1.6. Societal expectations

1.1.7. Economic issues of the agricultural sector at different scales

1.1.8. Balance sheet: the need for dual performance

1.3. Ecoscheme Plan

1.1.9. Purpose

1.1.10. Structure

1.1.11. Content

1.1.12. Stimulating the transition to certain farming systems

1.1.13. Monitoring the impact of this plan at farm level

1.1.14. The value of precision farming in improving the environmental performance of farms

1.4. Issues

2. STATE OF THE ART

2.1. Definition

2.1.1. Digital agriculture and smart farming

2.1.2. Automation of agriculture

2.1.3. Precision agriculture

2.2. Tools by type of production

2.2.1. Precision breeding

2.2.2. Tools related to the management of agricultural land

2.2.3. The cost of these tools

2.3. The different levels of input management tools in crop production

2.4. The development of digital agriculture in Europe

2.4.1. General data

2.4.2. Goals sought by farmers

2.4.3. The relevance of digital farming to European agricultural policies

2.4.4. Sustainability of European digital agriculture on a global scale

2.5. Factors hindering the development of digital agriculture

2.5.1. Farmers’ barriers to adopting these technologies

2.5.2. The complexity of technologies

2.5.3. Lack of infrastructure

2.5.4. Standards issues

2.5.5. Knowledge and expertise issues

2.5.6. Challenges in managing data from digital agriculture

2.6. Role of the European Union in relation to these challenges

2.7. The challenge of a quantitative analysis of the performance of these tools

3. METHODOLOGY

3.1. Data collection

3.1.1. Data providers

3.1.2. Crops concerned

3.1.3. Type of data collected

3.1.4. Cost of ODAs by input

3.2. Data processing

3.3. Results calculated from the data

3.3.1. Results to be calculated for a complete analysis

3.3.2. Results based on available data

4. EVALUATION OF ECONOMIC AND ENVIRONMENTAL PERFORMANCE

4.1. Irrigation management

4.1.1. Almond cultivation

4.1.2. Cotton cultivation

4.1.3. Olive growing

4.1.4. Peach cultivation

4.1.5. Pistachio cultivation

4.1.6. Potato cultivation

4.1.7. Grape growing

4.1.8. Maize cultivation

4.1.9. Performance assessment of DMOs related to irrigation management

4.2. Pesticide management

4.2.1. Almond cultivation

4.2.2. Beet growing

4.2.3. Wheat cultivation

4.2.4. Soft wheat

4.2.5. Cotton cultivation

4.2.6. Bean cultivation

4.2.7. Cultivation of kiwifruit

4.2.8. Olive growing

4.2.9. Barley cultivation

4.2.10. Peach cultivation

4.2.11. Pistachio cultivation

4.2.12. Chickpea cultivation

4.2.13. Potato cultivation

4.2.14. Grape growing

4.2.15. Stevia cultivation

4.2.16. Tomato cultivation

4.2.17. Assessment of the performance of DMOs related to pesticide management

4.3. Fertilisation management

4.3.1. Almond cultivation

4.3.2. Wheat cultivation

4.3.3. Rapeseed cultivation

4.3.4. Cotton cultivation

4.3.5. Growing lettuce

4.3.6. Olive growing

4.3.7. Barley cultivation

4.3.8. Peach cultivation

4.3.9. Potato cultivation

4.3.10. Grape growing

4.3.11. Tomato cultivation

4.3.12. Performance assessment of DMOs related to fertiliser management

4.4. Summary of ADO performance by crop

4.4.1. Almond cultivation

4.4.2. Wheat cultivation

4.4.3. Cotton cultivation

4.4.4. Olive growing

4.4.5. Barley crops

4.4.6. Peach cultivation

4.4.7. Pistachio cultivation

4.4.8. Potato cultivation

4.4.9. Grape growing

4.4.10. Tomato cultivation

5. DISCUSSION

5.1. Performance of ADOs

5.1.1. Environmental performance

5.1.2. Economic performance

5.1.3. Limitations and perspectives of the analysis

5.1.4. Assessment of ADO performance

5.2. Consequences of the implementation of digital agriculture

5.2.1. Impact on jobs

5.2.2. Digital agriculture in relation to farming systems

5.2.3. Digital agriculture and the diversity of European farms

5.3. Stimulating the transition to digital agriculture

CONCLUSION

BIBLIOGRAPHY

1. Context

1.1. The Common Agricultural Policy

1.1.1. An environmentally friendly CAP

The Common Agricultural Policy (CAP), created in 1962, aimed at providing affordable food in Europe and a decent living for farmers. In addition to this economic orientation, supporting agricultural markets and producers, an environmental dimension has been added since the 1990s (European Commission, 2019). In 1985, the Council introduced for the first time – in Article 19 of Regulation 795/85 – the notion of protection of agricultural habitats and landscapes. In 1991 the Commission stressed the importance of encouraging environmentally friendly production. The integration of environmental requirements into the CAP during the Mac Sharry reform in 1992 followed this orientation. Environmental protection has become increasingly important in the course of agricultural policy reforms, and has become one of the three main pillars of the 2013 Ciolos reform (European Commission, 2019).

Farmers, encouraged to minimise their environmental footprint by changing their practices, have achieved a 21% reduction in GHG emissions through more efficient use of fertilisers between 1990 and 2014 (COMAGRI, 2019). Today, European society seems to be expressing strong expectations regarding the link between agriculture and the environment. The sector is expected to provide answers, particularly with regard to climate change.

1.1.2. Current CAP

In order to improve the environmental and climatic performance of the CAP, the 2013 reform introduced a ‘greening’ measure, also known as a ‘green payment’. This measure represents 30% of direct payments. Put into practice in 2015, this measure remunerates farmers for carrying out practices that benefit the environment, providing basic public goods (Jereb et al., 2017).

1.1.3. Greening objectives

Greening aims to encourage simple and environmentally beneficial agronomic practices across the EU. These practices are implemented by millions of farmers, over large parts of the European territory. Greening measures have to meet three environmental objectives. The first one is to improve soil quality, in particular by increasing the level of organic matter through mandatory minimum crop diversification. The second one is to preserve and improve biodiversity by including a certain percentage of areas of ecological interest, EIS, on farms. Fallow land, catch crops and nitrogen-fixing plants, and certain topographical features are part of these EIS. The last environmental objective is the sequestration of carbon by permanent grasslands, by ensuring the protection of the latter (Jereb et al., 2017).

1.1.4. The results of the greening measures

The European Court of Auditors’ report questions the effectiveness of the greening measures put in place. The Court estimates that these measures have only led to positive changes for 5% of farmers. 65% of them would not have had to change their practices to be eligible for these aids (Jereb et al., 2017). However, in its assessment, the Court of Auditors only notes the positive changes in practices made by farmers. It does not take into account the maintenance of environmentally beneficial practices already implemented by farmers. According to the Commission, 77% of the European Union’s agricultural land is affected by greening.

These observations show the lack of clarity of the environmental objectives and raise the ambivalence of these measures. Should they encourage farms to become more environmentally friendly or should they reward farmers for environmentally beneficial practices?

However, it is stressed that the 2013 CAP has built coherence, based on common European objectives, between these greening measures, cross-compliance standards and requirements, and rural development commitments (Jereb et al., 2017).

1.2. Issues addressed in the 2021-20227 CAP reform

1.1.5. Environmental issues

The Court of Auditors raises among its recommendations the importance of establishing quantified environmental objectives. It also stresses the need to develop models that will monitor the impact of future CAP environmental measures on the environment and climate (Jereb et al., 2017).

In addition, many environmental commitments have been made by the European Union at international level since the 2013 reform. These include the target of a 40% reduction in GHG emissions by 2030, agreed at the Paris agreements in 2015 and the United Nations Sustainable Development Goals (SDGs) (European Commission, 2018). The themes of combating and adapting to climate change, sustainable management of natural resources (water, soil and air) and the preservation of biodiversity and ecosystems are among these objectives. The SDGs state that the achievement of these goals requires policies and strategies at the national level (United Nations, 2019). In the context of these commitments, three of the nine objectives of the 2023-2027 CAP are related to the environment and take up the themes of the SDGs.

1.1.6. Societal expectations

European citizens expect safe, nutritious and fairly priced food from the agricultural sector. At the same time, there is a growing desire for a more sustainable agriculture, including a greater focus on the origin and on quality of food. The of sustainability includes protecting the environment, combating climate change and defend/increase biodiversity and animal welfare (COMAGRI, 2019). The impact of agriculture on the health of producers is also a frequently raised issue. A European survey also highlighted the desire to strengthen the role of farmers in the food chain and to support rural communities and family farming (DGCOMM, 2016).

Many actors, including the agricultural unions, want farmers to be the driving force behind these changes and are calling for support in the agricultural transition, and for the CAP to be simplified in terms of administration, its use, and sanctions. Policies with concrete objectives, clear and rigorous indicators are expected.

1.1.7. Economic issues of the agricultural sector at different scales

Agricultural policies must enable farmers to produce quality food, public goods and services in response to consumer expectations while ensuring a decent income (European Commission, 2019). The imperative of food security is not negotiable both when it comes to EU consumers or answering to expectations of the world markets notably of our Mediterranean and African neighbours.

The agricultural sector must be supported in the form of remuneration for the non-market goods it produces, in view of the cost distortions linked to the societal choices that the European Union has made, as well as in view of the markets and their increasing volatility.

Besides the fair remuneration of non-market goods by the CAP, the challenge is to strengthen the position of farmers within supply chains -that tend to reduce the cost of raw materials (European Commission, 2019)-. It is also a matter of encouraging farmers to plan ahead by investing while giving them the tools to manage price and market volatility, besides protecting them against the impacts of deep crises of essentially exogenous origin.

On an international scale, Europe’s competitiveness must be strengthened so that the European Union can consolidate its position on the world markets for the various market segments – assuming its responsibility for the supply and stability of food markets – and reinforce its degree of autonomy, particularly in terms of protein.

1.1.8. Assessment: the need for dual performance

In response to environmental and climate challenges and societal expectations, the post-2020 CAP proposes to raise its environmental ambitions to facilitate and encourage the consideration of these issues in agricultural practices. A balance between the economic and environmental dimensions is essential to ensure stability and profitability of work for farmers and to establish a sustainable CAP (Jereb et al., 2017; COMAGRI, 2019).

1.3. Eco-schemes

1.1.9. Goal

The Eco-schemes are a new tool proposed in the post-2022 CAP that aims to be a driving force for a transition towards more sustainable agriculture. This measure replaces the greening of income support introduced in the 2013 reform. Like the greening, the Eco-schemes to earmark a share of the direct payments pillar to be devoted to environmental and climate protection (COMAGRI, 2019).

1.1.10. Structure

Eco-schemes involve per-hectare aid that, however, varies from one state to another (or even from one region to another) and from one measure to another, which farmers would receive annually depending on the environmentally and climatically beneficial practices they implement. It is mandatory for Member States to foresee eco-schemes in their national strategic plans. Nevertheless, they are free to choose the measures to be included and the environmental requirements of these measures, which are optional for farmers.

In order not to create overlaps with other CAP measures, the practices and schemes promoted by the eco-schemes should ensure higher environmental benefits than those required for environmental cross compliance[1] . This environmental cross-compliance should be implemented by Member States under the CAP. However, the Commission concedes a very high degree of subsidiarity in the actual requirements made on their farmers in this respect. The practices and benefits should also be different from the commitments of the GAEC[2] , Good Agri-Environmental and Climatic Measures, of the second pillar.

The European Parliament’s Agriculture Committee, COMAGRI, fought to include in the CAP deal the principles of benefit the environment as well as the economic side so to ensure stability and resilience for farmers. By combining these two performances, they aim at ensuring that a sustainable tool is built in the long term.

1.1.11. Content

25% of the direct payment budget should be dedicated to eco-schemes by Member States in their Strategic Plans. However, members of the European Parliament’s COMAGRI have defended the position according to which the aid should cover more than just compensation for the additional costs and loss of income associated with the implementation of such practices and devices (Farm Europe, 2019). In the agreed CAP deal, member states can decide to support annual or multi-annual commitments and premia can be set as ‘compensation’ payments (for the additional costs an income loss stemming from the practices concerned) or as payments going beyond compensation -provided that the relevant Green Box rules of the World Trade Organisations are respected-.

Co-legislators agreed also on the need of a ‘learning period’, going from 2023 to 2024, when an EU country may spend less than 25% in case of a lower take-up by farmers than planned.

Member States, when designing their strategic plans, can choose from a list of practices that will be considered part of their Eco-schemes (practices such as organic farming, agro-ecological practices, precision farming, agro-forestry, carbon farming, etc.). Each member state has to draw up a CAP strategic plan based on their national situation and needs of their territory. These plans -now submitted to the Commission by Member states within their proposition for the National Strategic Plan on the CAP, are under scrutiny[3] . For the Commission, adapting these measures at national or even regional level according to soil types, climates, land use and farm structures would make it possible to better respond to local environmental issues.

1.1.12. Stimulating the transition to certain farming systems

In order to respond to local issues without compromising the unity of Europe, one solution discussed during negotiations was to set the content of the Eco-schemes with broad objectives that can be achieved by different means. It would then be possible to adapt the means to achieve specific objectives to local conditions. Eventually, it was opted for a list of set measures that member states can choose from and that best adapt the national needs and farming system.

The aim would be to promote the transition to farming systems that are environmentally friendly and economically viable in the long term. The practices linked to these systems, once identified, correspond to the list of practices that can be promoted within Eco-schemes.

1.1.13. Monitoring the impact of this plan at farm level

Considering that the approved CAP is intended to be outcome-based (Etats membres de l’UE, 2019) it is necessary to measure the impact of eco-schemes in how effectively they favour the achievement the environmental objectives.

The European Commission has developed 178 indicators and over 900 sub-indicators to assess the performance of the CAP. This large number of indicators is intended to compare the effects of the CAP country by country. As recommended by the Court of Auditors, evaluating the Eco-schemes separately would allow a better measurement of their impact on the economic and environmental performance of farms (Jereb et al., 2017).

1.1.14. The value of precision agriculture in improving the environmental performance of farms

The Commission and co-legislators see knowledge, innovation and digitalisation as key tools to improve the environmental performance of the CAP and the European agricultural sector. Precision farming uses sensors to measure the needs of crops, animals and weather conditions. They make it possible to adjust inputs such as water, nutrients and plant protection products to the measured needs.

In such a system, farms generate data on the quantities of inputs used and the yields obtained. These data can eventually be used to generate indicators that will show the environmental performance of farms with much greater precision. However, in order to do this, the use of such tools must be sufficiently widespread on European farms to cover most of the areas and production carried out. This will only happen if precision farming ensures environmental performance and it brings economic benefit to farmers.

1.4. Issue

The tool of the Eco-scheme has the dual aim of ensuring unity of action and ambition while being flexible enough to respond to local issues, all while stimulating the transition to more sustainable agricultural systems.

In the face of recurrent criticism concerning the lack of quantification of the effects of the CAP, simple policy tools offering solid monitoring of results across Europe are desired, both by actors in the agricultural world and by European decision makers.

The link between agriculture and the environment must be maintained and strengthened, in line with previous CAPs. The new tools available to agriculture claim to ensure economic and environmental results. If this is the case, they would make it possible to achieve this dual economic and environmental performance. If they are more widespread, they could ensure a precise evaluation of the effect of the policies put in place.

In this context, this study has three objectives. The first one aims to qualitatively analyse different farming systems in order to discern which ones can promote an effective transition. The second one is to develop a set of indicators to quantitatively measure the impact of the plan on farms. The last objective evaluates the economic and environmental performance of different farming systems with and without the use of precision agriculture.

2. State of the art

2.1. Definition

2.1.1. Digital and smart agriculture

Digital agriculture integrates information and communication technologies (ICT) in agriculture. Global Positioning Systems (GPS), Geographic Information Systems (GIS), data clouds, Internet of Things (IOT), Radio Frequency Identification (FRID) techniques, data collection and management systems, drones and activators, attached to highly accurate sensors and cameras are technologies used in digital agriculture. For several decades, these digital tools have been used in animal husbandry, field crops, arboriculture, viticulture or market gardening (Zarco-Tejada, Hubbard and Loudjani, 2014; Schrijver, 2016; Souza et al., 2019)

Smart farming is the use of these technologies to collect data and create algorithms to process it. Smart farming provides both better traceability of agricultural products, guaranteeing their value, and a set of tools that farmers can rely on during production (Zarco-Tejada, Hubbard and Loudjani, 2014; Schrijver, 2016). The production tools provided by smart farming are linked either to precision agriculture or to the automation of agriculture.

Thus, digital agriculture could correspond to a set of digital tools that, when used for data collection and processing by smart farming, help with production or ensure product traceability. Figure 1 summarises this vision of digital agriculture.

Digital agriculture

Smart farming

Production-related tools

Precision agriculture

Automation of agriculture

Traceability

Figure 1 – Diagram showing the links between digital agriculture, smart farming, precision agriculture

Regarding agricultural production, digital agriculture is seen as a way to succeed in producing more while protecting nature by optimising inputs (seeds, pesticides, fertilisers, feed and livestock care) and ensuring well-being at work (Zarco-Tejada, Hubbard and Loudjani, 2014). This study focuses on the part of digital farming related to agricultural production.

2.1.2. Automation of agriculture

The emergence of robots, automatic controls and artificial intelligence within agricultural production is leading to the automation of agriculture. This leads to labour optimisation, and more efficient use of certain inputs such as feed rations. The use of pesticides can also be reduced (Zarco-Tejada, Hubbard and Loudjani, 2014; Brown et al., 2018).

Robotic scarecrows limit the nuisance of wildlife on crops. Others provide mechanical weeding or cleaning of stables. Some milking robots equipped with sensors and cameras operate automatically. The presence of cameras ensures the opening of the doors of a farm. Sensors can automatically trigger crop irrigation based on measured data. Ventilation and heating can be adapted to the needs of the animals. Feeding efficiency can be improved by automating the delivery of feed tailored to the animal’s physiological needs. Certain diseases can be detected in advance by sensors, thus enabling the anticipation of care in the farm. The same applies to the spraying of pesticides and fertilisers in crop production. Tractor equipment such as power steering facilitates operations such as sowing, treatment and harvesting (Zarco-Tejada, Hubbard and Loudjani, 2014; Brown et al., 2018).

2.1.3. Precision agriculture

Precision agriculture enables decisions to be made through decision algorithms and decision support tools (DSTs). Some of these relate to agronomic choices. They help with the implementation of rotations, crop associations, sowing and grazing management. Others advise on input management. They propose suitable products with low environmental impact. They target treatment dates and modulate the doses to be sprayed (Zarco-Tejada, Hubbard and Loudjani, 2014). Precision agriculture uses sensors, cameras and satellite images that report on the needs and presence of pathogens and diseases in both livestock and crop production (Zarco-Tejada, Hubbard and Loudjani, 2014; Brown et al., 2018).

2.2. Tools by type of production

2.2.1. Precision breeding

In meat, egg or milk production, the health status of the flock is monitored by collecting information at the individual animal level. The data obtained is used to improve the efficiency of rations and to monitor the behaviour of each animal. Together with data on their environment, this ensures their welfare. The time saved and the simplification of work tasks that this monitoring allows provide comfort to the farmer. As in crop production, environmental benefits are generated. These include the reduction of inputs and energy consumed, waste produced and emissions of polluting gases. The use of precision farming in livestock production is in its infancy. It is often coupled with tools that automate farming tasks. The cost of such devices and the lack of awareness among farmers seem to be the main reasons for their slow development (Zarco-Tejada, Hubbard and Loudjani, 2014; Brown et al., 2018).

2.2.2. Tools related to the management of agricultural areas

Digital farming is more widely used and publicised for agricultural land management than for livestock production, and is mainly developed for crops. In recent years, it has been deployed in market gardening, arboriculture and viticulture. DSTs can also help with the management of pastures and meadows (Zarco-Tejada, Hubbard and Loudjani, 2014; Brown et al., 2018).

Precision agriculture, through DSTs, proposes an adjustment of agricultural practices according to the measured conditions (soils, climatic conditions, type of crop, etc.). It can be coupled with robotic and automated tools (Brown et al., 2018). This combination of agricultural automation and precision agriculture in crop production mainly concerns input management.

Variable input rate application methods and tractor power steering are the two main types of tools that smart farming offers for input management in crop production (Zarco-Tejada, Hubbard and Loudjani, 2014). Variable input application methods adapt the quantities of water, pesticides and fertilisers to the needs of the plants or to infestation thresholds. In combination with power steering, these quantities can be sprayed locally, while without the use of digital technology, they are usually applied uniformly (Zarco-Tejada, Hubbard and Loudjani, 2014). This is called modulation rather than precision. The risk of human error can be reduced by controlling the deviation of the tractor’s trajectory when spraying with the power steering (Brown et al., 2018).

By preserving water resources and limiting the risk of leaching of fertilisers and pesticides, the impact of agriculture on the environment is reduced. The economic performance of farms can also be improved through more efficient use of inputs and possible yield increases (Zarco-Tejada, Hubbard and Loudjani, 2014).

This study therefore focuses on tools related to input management on agricultural land.

2.2.3. The cost of these tools

The cost of different digital tools related to production varies. Some DSTs are free, notably those concerning crop management, choice of cover and varieties (Arvalis, 2019). Others are not free but can offer a rapid return on investment. These are DSTs such as Farmstar®, GAIASENS® or weather stations. Other tools, related to agricultural automation, such as robots or digital tools may require larger financial investments. The degree of precision can be correlated with the amount of tools needed (GPS, cameras, guidance systems, nozzle cut-off systems…) and thus with the investment cost (Brown et al., 2018).

2.3. The different levels of input management tools in crop production

It is possible to classify digital tools into different levels according to their ability to limit the impact of inputs on the environment. Their affordability and the scale at which the adjustment of input quantities is assessed are taken into account. This classification, presented Tableau 1, applies to water, pesticides and fertilisers.

Table 1 – Ranking of tools according to their ability to reduce the environmental impact of inputs

Level	Objective	Tools needed
1	Adjustment to the scale of a group of plots with the same crops, soil and climatic conditions and phytosanitary risks	Precision farming tools*
2	Adjustment at the plot level	Precision farming tools*
3	Intra-cellular scale adjustment	Precision farming tools* Dose modulation
4	Intra-plot adjustment and zero detectable residues for fertilisation and pesticides	Precision farming tools* Dose modulation Robotisation for pesticide management Automation of irrigation
5	Adjustment at the plant level in the plot and zero detectable residues for fertilisation and pesticides	Precision farming tools* Dose modulation Robotisation for pesticide management Automation of irrigation
Sensors, weather stations, satellite images, cameras, input management DSTs. * Power steering and local spraying of pesticides and fertilisers.

2.4. The development of digital agriculture in Europe

2.4.1. General data

Europe is one of the regions of the world where the supply of digital agriculture is growing the most. Since the beginning of the 21st century, between 70% and 80% of agricultural equipment is equipped with components that can be linked to digital tools (Zarco-Tejada, Hubbard and Loudjani, 2014; Say et al., 2018). More than 450 different types of products are marketed by 4500 suppliers. The manufacture, distribution and services associated with these technologies generate more than 160,000 jobs, mainly in the private sector (Zarco-Tejada, Hubbard and Loudjani, 2014).

The adoption of digital farming is taking place on a wide range of agricultural surfaces, and the supply of technologies is constantly increasing. Nevertheless, some consider that the evolution of digital agriculture is lagging behind the development initially envisaged and compared to that observed in other major global agricultural regions (Zarco-Tejada, Hubbard and Loudjani, 2014). A 2016 survey of 287 European farmers shows that 50% of European farmers are in favour of adopting such tools (Kernecker et al., 2019). However, this type of technology is only used on 25% of farms (Say et al., 2018).

2.4.2. Goals sought by farmers

Increased yields, improved working conditions and comfort, and reduced workload are the main decision factors for its adoption by farmers, according to this survey (Kernecker et al., 2019). Reducing input costs, pollution and environmental impact are other interests mentioned in second place (Zarco-Tejada, Hubbard and Loudjani, 2014; Kernecker et al., 2019).

2.4.3. Interest of digital agriculture in European agricultural policies

In parallel to reducing the environmental impact of agriculture, deploying the use of digital tools in Europe would corollary quantify this impact at the farm level. Digital agriculture could make explicit the adequacy of farms with the standards of good practice. It would make it possible to better materialise the role of farmers in the production of public goods, and to better legitimise the public remuneration of this production (Kritikos, 2017).

Deployed on a large scale in Europe, the administrative burden could be simplified, especially in the implementation and enforcement of CAP measures. Data entry for each farm could be simplified, as could control procedures. The latter mainly concerns the identification of agricultural parcels, and their monitoring over time (Kritikos, 2017). Digital agriculture could also provide precise data that could be used for indicators to evaluate the policies implemented, as well as statistical data.

The advent of digital agriculture in Europe cannot take place without the support of the European Union. In view of the economic and environmental performance promoted by these tools, digital agriculture must be put forward in the future CAP. But to do so, it is essential to better understand the current and future potential of these tools. The challenges linked to their adoption and use need to be identified and the real economic and environmental benefits obtained by farmers need to be measured (Panagos et al., 2019).

2.4.4. Sustainability of European digital agriculture on a global scale

While digital farming is of economic and environmental interest at the European level, the sustainability of the solutions offered by such farming method can only be assessed at the global level. The environmental, economic and social impacts related to the extraction and processing of certain sensor components, such as rare earths, must be taken into consideration at the international level. These impacts include the high water and energy consumption of the mines, water shortages for the surrounding villages, and soil and crop pollution. Social considerations related to living and working conditions are also put forward.

The life cycle analysis of the solutions proposed by digital agriculture could make it possible to measure the environmental impact of these tools, from the extraction of the raw materials to their end of life. The social impact of these solutions outside Europe needs to be analysed and alternatives to be found.

2.5. Factors hindering the development of digital agriculture

2.5.1. Farmers’ barriers to adopt these technologies

2.5.1.1. The cost

The cost of the tools is only one part of the total expenses farmers have to make when they want to invest in digital tools. In addition to the cost of the software and hardware, the cost of information on these techniques and training has to be considered as well (Zarco-Tejada, Hubbard and Loudjani, 2014; Kernecker et al., 2019). There may also be expenses related to data processing. Investment in these technologies may be limited by some farmers’ access to credit (Kritikos, 2017).

Added to this is the fear of investing in the long term in tools that may quickly become obsolete due to the speed of technological progress.

2.5.1.2. Debated profitability

Return on investment and profitability are key criteria for the adoption of digital tools. The results of different studies contradict each other on the profitability of digital farming. Some claim that there are benefits, and others show that there are no statistically significant economic benefits over a 10-year period (Zarco-Tejada, Hubbard and Loudjani, 2014).

Some studies conclude that digital farming is only profitable for areas larger than 250 ha (Zarco-Tejada, Hubbard and Loudjani, 2014). This finding is supported by the 2016 survey showing that larger farms are more likely to adopt such technologies (Kernecker et al., 2019). However, it only concerns farms that pay for the investment on their own. Examples of organisations and groups of small farms show that it is possible to invest and make a profit from such tools collectively. This is the case of GAIA, for example, where the size of the plots where precision farming tools are used varies between 0.51 hectares and 37.23 hectares. In other words, the lack of visibility of the added value of digital agriculture works against its adoption (Kernecker et al., 2019).

2.5.2. The complexity of technologies

The time needed to format and enter data into software is another barrier for farmers hoping to save time in production. This data management step can be a barrier to the adoption of such technologies (Zarco-Tejada, Hubbard and Loudjani, 2014). The 2016 survey by Kernecker et al. (2019) shows that farmers who do not use these tools feel that they are too complicated to use. Farmers who are ready to adopt these technologies fear that they will have difficulties interpreting the data (Kernecker et al., 2019).

2.5.3. Lack of infrastructure

Digital agriculture generates large amounts of data. Its proper functioning depends on the presence of high-speed infrastructure (Zarco-Tejada, Hubbard and Loudjani, 2014; Kritikos, 2017). However, broadband coverage (30mbps) is covered in less than 50% of the European rural areas. This is particularly the case for 14 Member States (Ivanova et al., 2018).

2.5.4. Standards issue

Tools from different vendors are not always compatible, preventing data sharing between different systems. The creation of standards, both in terms of interfaces and data codes, would allow interoperability between the different tools, thus facilitating their accessibility to farmers (Zarco-Tejada, Hubbard and Loudjani, 2014; Kritikos, 2017; Kernecker et al., 2019).

2.5.5. Knowledge and expertise issues

There is a lot of research work related to digital agriculture. The lack of communication and knowledge transfer between researchers, institutes, cooperatives and farmers is one of the causes of the slow development of digital agriculture in Europe (Zarco-Tejada, Hubbard and Loudjani, 2014; Kritikos, 2017; Kernecker et al., 2019).

This transfer of knowledge could take place in the form of training, enabling farmers to learn the skills needed to use these techniques (Schrijver, 2016). Demonstrations and demonstration plots are other ways of introducing these tools to agricultural stakeholders.

Farmers also state the need for expertise and unbiased advice, from entities independent of the manufacturers of digital farming tools (Kernecker et al., 2019).

2.5.6. Challenges in managing data from digital agriculture

New players are entering the agricultural sector. These companies from agribusiness, finance, chemicals or even distributors and agri-food industries focus mainly on data collection, analysis and management. The coded data is not primarily aimed at the farmers who produce it or at farmers’ organisations, but rather at those companies that base their work on it. This raises questions about data ownership, confidentiality, protection and use (Zampti, 2019).

Protecting farmers’ rights to data from their farms against the risk of abuse of data management and use is paramount (Kritikos, 2017; Paraforos et al., 2019). To this end, charters, certifications and codes of conduct are set up by different agricultural unions and cooperatives at national or European level (COGECA, 2018; data agri, 2018). European policies have an important role to play in order to protect the centrality of farmers in the production and management of these data (Kritikos, 2017).

2.6. Role of the European Union in relation to these challenges

Data management should take place at European level, following on from what the private sector has already put in place (Zarco-Tejada, Hubbard and Loudjani, 2014). A European legislative framework dealing with data ownership, confidentiality and protection needs to be organised. Issues of liability, human security, technical controls must also be addressed (Kritikos, 2017). To enable the development of digital agriculture, the identified obstacles related to the lack of infrastructure, standards and knowledge transfer must be addressed.

In this context, the European Union should define a framework for data sharing between the actors of the agri-food chain and strengthen the place of farmers in the production chain. It should be able to inform farmers about the costs, benefits and economic returns of digital agriculture. Training and courses to learn how to use these technologies could be supported under the CAP (Kritikos, 2017).

Given the diversity in farm size, farm type, farming practices, farmer training and yields, the opportunities and concerns of digital farming may vary from state to state. Agricultural policies must take this differences into account (Schrijver, 2016).

2.7. The challenge of a quantitative analysis of the performance of these tools

Digital farming is being promoted as a way of using inputs more effectively. However, data on digital farming is scattered and held by manufacturers and traders who are not necessarily inclined to share it.

Farmers’ fears about the cost and lack of return on investment of these technologies may be correlated with the lack of quantification of the benefits of such way of farming. The tools offered by digital agriculture span a wide range of cost and precision, as illustrated by Table 1. The tools related to precision agriculture correspond to the first level, and they are, usually, relatively cheap. If they meet the economic and sustainability objectives of digital agriculture, their development could be supported on a European scale.

Finally, it should be noted that an estimate of the profitability and benefits of digital tools in agricultural production must be made in relation to reference systems without the use of such tools

3. Methodology

3.1. Data collection

3.1.1. Data providers

To assess the economic and environmental performance of DSTs on the amounts of fertiliser, pesticides and water applied, data were collected from distributors offering various DSTs.

Farmstar is a tool designed by Airbus Defence and Space and two French technical institutes, Arvalis – Institut du végétal – and Terre Inovia. This combination of agronomy and remote sensing provides information on the condition of crops, their stand, their nutrient status and on disease risks. The precision of this tool, down to the intra-plot level, makes it possible to modulate the application of inputs according to needs. Farmstar is marketed to service distributors, mainly cooperatives and chambers of agriculture, which then offer it to farmers.

Terrena is a French agricultural cooperative located in Pays de Loire, Poitou-Charentes and Brittany. The cooperative offers its members in the cereal sector the possibility of using Farmstar coupled with monitoring by an advisor. This nitrogen management service is called FertiloSat. For pesticide management, it is called Fongipro.

GAIA EPICHEIREIN was created in 2014 from a broad coalition of farmers and agricultural cooperatives (71 agricultural cooperatives and associations, 150,000 farmers) working throughout Greece in all sectors of plant and animal production. These agricultural actors have joined forces with partners from the banking and IT sectors sharing a common vision for a more sustainable and competitive Greek agricultural model. GAIA EPICHEIREIN is the entity that coordinates the cooperation and marketing networks of the GAIASENSE smart farming system developed and operated by NEUROPUBLIC SA.

The BTI, the Beet Technical Institute, offers an interactive map informing on the global fungal risk and on the number of treatments carried out concerning cercosporiosis, powdery mildew, rust and ramularia. This DST is fed from weekly data produced by the BTI, from the technical services of the sugar factories and from the observers of the biological monitoring network of the territory.

3.1.2. Crops concerned

Data on almonds, beetroot, soft and durum wheat, rape, cotton, beans, kiwifruit, lettuce, maize, olives, barley, peaches, pistachios, chickpeas, potatoes, grapes, stevia and tomatoes were collected.

Table 2 summarises the crops studied according to the type of input.

Table 2 – Crops studied by type of input

Culture	Type of input
Culture	Water	Pesticides	Fertilizer
Almonds	x	x	x
Beets		x
Wheat (soft and/or hard)		x	x
Rape			x
Cotton	x	x	x
Beans		x
Kiwis		x
Lettuce			x
Maize	x
Olives	x	x	x
Barley		x	x
Peaches	x	x	x
Pistachios	x	x
Chickpeas		x
Potatoes	x	x	x
Grapes	x	x	x
Stevia		x
Tomatoes		x	x

3.1.3. Type of data collected

For each input, the data requested, for plots monitored by DSTs and for plots not monitored by DSTs are:

The quantities of input used
- In m³ .ha^-1 for water
- In U.ha^-1 for fertilisers
- In kg.ha^-1 for pesticides
The cost of this use, in €.ha^-1
The yields obtained, in q.ha^-1 .

The plots of land that are not monitored by DSTs are the control ones. The input quantities of the plots monitored by digital tool correspond to the quantities recommended by them. This study is based on the assumption that the quantities recommended by the DSTs are applied.

In order to know the sample size, the number of land plots from which the data are agglomerated is indicated for the modalities with/without DST. The number of years over which the data was gather is detailed. Although it is necessary to have more than 3 years of data, in order to smooth out the variability linked to climatic conditions and the presence of pests, data are only available over two years for some inputs and some crops. The cost of the DST is also reported because it is taken into account in the calculations of the difference in costs and the gross margin linked to the use of the tool.

3.1.4. Cost of DST by input

Depending on the provider, DSTs can be free of charge or have a price tag ranging from €3 to €20 per hectare. Some tools offer a range of costs that adjust according to the additional services provided, such as additional advice. Other DSTs have a price that is adapted to the type of crop, the area and number of plots covered by its services and, of course, the number of producers sharing the tool.

For each input, the highest price offered by the DST provider is used for the cost and gross margin calculations.

3.2. Data processing

The data collected is then aggregated by crop type. Each crop has its own nitrogen, potassium, magnesium and water requirements. Their susceptibility to certain diseases varies according to their type and the order of magnitude of yields can vary greatly from one crop to another.

As the aim is to evaluate the performance of these DSTs on a European scale, no comparison between different locations is made.

3.3. Results calculated from the data

3.3.1. Results to be calculated for a complete analysis

In order to have a complete evaluation of the economic and environmental performance of DSTs on input management, a set of elements to be analysed has been identified. These elements are, for each crop and each type of input:

The difference between the average amounts of inputs that are applied per hectare with or without DST, in m³. ha^-1 for water, in U.ha^-1 for fertilisers, in kg.ha^-1 for pesticides, as shown in Equation 1.

Equation 1 – Calculation of the difference between the average quantities of inputs applied per hectare with and without DST

The percentage of input saved on average per hectare through the use of DSTs.
The difference in average costs per hectare induced by the difference between the quantities of inputs that are applied with and without DST, in €.ha^-1 , as shown in Equation 2.

Equation 2 – Calculation of the average load difference per hectare

The average percentage of savings per hectare achieved through the use of DSTs.
The difference in average yield with and without DST, in q.ha^-1 , as shown in Equation 3.

Equation 3 – Calculation of the difference in average yield with and without ADO

The difference in gross product from the yield obtained with or without DST, in €.ha^-1 , as shown in Equation 4.

Equation 4 – Calculation of the difference in gross product from

The difference in gross margin between plots with DST and plots without DST in €.ha^-1 , as shown in Equation 5.

Equation 5 – Calculation of the gross margin difference

The gross margin here is the difference between the gross product and the expenses. For plots with DST, their cost is added to the expenses, as shown in Equation 6.

Equation 6 – Calculation of the gross margin obtained with DST

3.3.2. Results based on available data

The type of data obtained varies according to the type of input and the crop. For each input, the available data are detailed, as well as the results they allow to be calculated.

3.3.2.1. Results obtained for DSTs related to irrigation management

Data on the average cost of irrigation and the average amount of water consumed with and without DST are available. From these data it can be calculated:

The difference in the average amount of water consumed
The percentage of water saved on average
The difference in average irrigation costs per hectare
The average percentage of financial savings that DSTs provide.

No information on the difference in yield with and without DST is available. The difference in gross product, gross margin and irrigation efficiency could not be calculated.

3.3.2.2. Results obtained for DSTs related to pesticide management

Data on the average cost of pesticide use and the average amounts of pesticides used with and without DST are available for almonds, beetroot, cotton, beans, kiwifruit, olives, peaches, pistachios, chickpeas, potatoes, grapes, stevia, and tomatoes. From these crops, data can be calculated:

The difference in the average amount of pesticides applied
The average percentage of pesticide savings
The difference in average costs related to pesticide management
The average percentage of financial savings that DSTs provide.

No information on average yields with or without DST is available for these crops. Therefore, gross revenues and gross margins cannot be calculated.

Average differences in costs and yields, as well as average percentages of financial savings are given for durum wheat, common wheat and barley. From these data, it can be calculated:

The difference in average costs related to pesticide management
The average percentage of financial savings that DSTs provide
The difference in average gross product
The difference in average gross margins.

For these crops, the calculation of efficiency is not possible because no information on the quantities of pesticides applied is provided.

3.3.2.3. Results obtained for DSTs related to fertilisation management

Data on the average cost of fertilisation with and without DST are available for all crops. From these data, it can be calculated:

The difference in average fertilisation costs
The average percentage of financial savings achieved by DSTs.

The average amounts of nitrogen, phosphorus and potassium applied with and without DST are given for almonds, cotton, lettuce, olives, peaches, potatoes, grapes and tomatoes. Only the average amounts of nitrogen applied with and without DST are available for wheat, oilseed rape and barley. From these data, it can be calculated:

The difference in the average amount of nutrients applied
The average percentage of nutrient savings for each crop.

The average yields obtained with and without DSTs are given for wheat, barley and oilseed rape. From these data, it can be calculated:

The difference in average gross product
The difference in average gross margin.

4. Evaluation of economic and environmental performance

4.1. Irrigation management

The performance of a DST related to irrigation management was measured for almonds, cotton, olives, peaches, pistachios, potatoes and grapes. The economic performance for each crop is analysed based on the difference in irrigation costs with and without DST. These costs include the maximum cost of the DST, which is €20. The environmental performance is evaluated from the difference in the amount of water consumed with or without the use of a DST.

A reduction in the average volume of water consumed when using DSTs can be seen in the Figure 2. A reduction in the average volume of water consumed when using DSTs is noted. Table 3 details the percentages of water saved on average per crop.

Figure 2 – Average amount of water consumed for the different crops with and without DST support

Table 3 – Average percentage of water saved per crop

	Almonds	Cotton	Olives	Peaches	Pistachios	Potatoes	Grapes
Percentage of water saved by using a DST	31,70 %	42,97%	32,50%	18,54%	24,61%	32,56%	42,71%

4.1.1. Almond cultivation

A DST related to irrigation management for almond cultivation was evaluated in 2017 and 2018. The use of this DST allows for an average reduction of 22.48% in irrigation-related expenses, i.e. an average saving of €56.40 per hectare, as shown in Figure 3.

Figure 3 – Average irrigation costs with and without DST for almond cultivation

4.1.2. Cotton growing

The use of a DST was evaluated in 16 cotton land plots in 2017 and 2018. This specific DST allows for an average reduction of 44.03% in irrigation-related costs, i.e. an average saving of €697.72 per hectare, as shown in Figure 4.

Figure 4 – Average irrigation loads with and without DST for cotton

4.1.3. Olive growing

Ten olive plots were evaluated with and without DST during 2017 and 2018. The use of this DST allows to reduce, on average, 25.62% of the expenses related to irrigation, i.e., to achieve a saving of 182.32€ per hectare. Findings are shown in Figure 5.

Figure 5 – Average irrigation loads with and without DST in olive cultivation

4.1.4. Peach growing

The use of a DST was evaluated in 26 peach orchards in 2017 and 2018. This DST allows for an average reduction of 12.74% in irrigation costs, i.e., an average saving of €96.04 per hectare, as shown in Figure 6.

Figure 6 – Average irrigation loads with and without DST for peaches

4.1.5. Pistachio cultivation

The use of a DST was evaluated in 4 pistachio plots in 2017 and 2018. This DST allows for an average reduction of 17.61% in irrigation-related expenses, i.e., a saving of €49.60 per hectare on average, as shown in Figure 7.

Figure 7 – Average irrigation loads with and without DST for pistachio cultivation

4.1.6. Potato cultivation

The use of a DST was evaluated in 5 potato plots in Mediterranean areas in 2017 and 2018. This DST allows for an average reduction of 27.44% in irrigation costs, i.e., an average saving of 107.50€ per hectare, as shown in Figure 8.

Figure 8 – Average irrigation loads with and without DST for potato cultivation

4.1.7. Grape growing

The use of a DST was evaluated in 12 vineyard plots in Mediterranean regions (Greece) in 2017 and 2018. This DST allows for an average reduction of 41.97% in irrigation-related expenses, i.e. a saving of €1155.34 per hectare on average, as shown in Figure 9.

Figure 9 – Average irrigation loads with and without DST for grapes

4.1.8. Maize crop

Since 2019, a DST has been implemented for maize management. According to the experimental data, this DST allows to save 40€ per hectare on average.

4.1.9. Performance review of DSTs related to irrigation management

The DSTs linked to irrigation management make it possible to reduce the average volume of water consumed for all the analysed crops. Similarly, for all the crops studied, the amounts saved are higher than the maximum cost of the DSTs, thus ensuring a return on investment.

In order to have a better analysis of the performance of DSTs concerning irrigation management, it would be interesting to complete these gains linked to the reduction of production costs with the gains in gross product. It would then be possible to evaluate the difference in gross margin obtained with or without DST. Such elements are also useful in order to remove the common fear of producers that less irrigation could lead to a decrease in yield, even though more finely managed irrigation could, on the contrary, lead to an increase.

4.2. Pesticide management

The environmental performance of DSTs related to pesticide management is assessed from the difference in the quantity of pesticides applied with or without a DST. When this data is not available, this evaluation is based on the difference in costs related to pesticide management. The cost of the tool is included in these costs. The economic performance is also evaluated from the difference in expenses related to pesticide management as well as from the differences in gross products and gross margins, depending on the data available.

4.2.1. Almond cultivation

The use of a DST was evaluated in six almond plots in 2017 and 2018. This DST reduces pesticide management costs by an average of 13.88%, resulting in an average saving of €34.18 per hectare, as shown in Figure 10.

Figure 10 – Average pesticide application costs with and without DST for almond cultivation

The use of DSTs reduces the amount of pesticides applied by 11.25% on average compared to not using DSTs, saving 1.07 kg of pesticides per hectare, as shown in Figure 11.

Figure 11 – Average amount of pesticides applied with and without DST for almond cultivation

4.2.2. Beet growing

The use of a DST was evaluated in 300 plots of beet cultivation between 2006 and 2015. This DST allows for an average reduction of 8.49% in pesticide management costs, i.e., an average saving of €4.44 per hectare, as shown in Figure 12.

Figure 12 – Average pesticide application costs with and without DST for beetroot

4.2.3. Wheat crop

The use of DSTs was evaluated for wheat cultivation between 2007 and 2018. On average, these DSTs increase the yield by an additional 2 quintals, increasing the gross product by €15 per hectare and the gross margin by €21 per hectare on average, as shown in Table 4. The cost of the tool is taken into account in the gross product and gross margin for wheat and in the expenses and gross margin for durum wheat and soft wheat.

Table 4 – Economic performance of DSTs related to pesticide management in wheat

	Wheat	Durum wheat	Soft wheat
Investment in pesticides saved through the use of DST (€/ha)		10,92	7,01
Percentage of costs saved through the use of DST (%)		26	16,67
Additional production (q/ha)	2	0,2	4,7
Difference in gross product (€/ha)	15*	3	65,8
Difference in gross margin (€/ha)	21*	1,92*	60,01*
* the cost of the tool is included in the gross margin

4.2.3.1. Durum wheat

The use of DSTs was evaluated in 457 durum wheat plots between 2007 and 2018. These DSTs reduce pesticide management costs by an average of 26%, or €10.92 per hectare on average. An additional production of 0.2 quintals per hectare takes place on average, increasing the gross product by €3 per hectare and the gross margin by €1.92 per hectare on average, as illustrated in Table 4.

4.2.4. Soft wheat

The use of DSTs was evaluated on 2912 soft wheat plots between 2007 and 2018. These DSTs reduce pesticide management costs by an average of 16.67%, or €7.01 per hectare on average. An additional production of 4.7 quintals per hectare takes place on average, increasing the gross product by €65.80 per hectare and the gross margin by €60.01 per hectare on average, as illustrated in Table 4.

4.2.5. Cotton growing

The use of a DST was evaluated in 14 cotton plots in 2017 and 2018. This DST allows for an average reduction of 31.81% in pesticide management costs, i.e., an average saving of €97.27 per hectare, as shown in Figure 13.

Figure 13 – Average pesticide application costs with and without DST for cotton

The use of DSTs reduces the amount of pesticides applied by 50.61% on average compared to not using DSTs, saving 2.07 kg of pesticides per hectare, as shown in Figure 14.

Figure 14 – Average amount of pesticides applied with and without DST for cotton

4.2.6. Bean cultivation

The use of an DST was evaluated in five bean plots in 2017. This DST recommends a lower number of pesticides, compared to a standard application without a DST. Without adding the cost of the tool to the expenses related to pesticide management, a margin of 2€ per hectare is observed on average. The cost of the DST used to calculate the average costs of pesticide application is €20 per hectare. This is the highest cost of the DSTs studied. Pesticide management by a DST increases the costs related to pesticide management by 37.88% on average, i.e., by €17.88 per hectare, as shown in Figure 15.

4.2.7.

Figure 15 – Average pesticide application costs with and without DST for the bean crop

Cultivation of kiwifruit

The use of an DST was evaluated in two kiwifruit plots in 2018. This DST recommends the same amount of pesticides as a standard application without DST. Thus, the cost of the tool increases the expenses related to pesticide management with DST compared to the expenses related to standard pesticide management.

4.2.8. Olive growing

The use of a DMO was evaluated in ten olive plots in 2018. This DMO allows for an average reduction of 64.46% in pesticide management costs, i.e., an average saving of €212.62 per hectare, as shown in Figure 16.

Figure 16 – Average costs related to the application of pesticides with or without ADO in olive growing

The use of DSTs reduces the amount of pesticides applied by 37.66% on average compared to not using DSTs, saving 4.77 kg of pesticides per hectare, as shown in Figure 17.

Figure 17 – Average amount of pesticides applied with and without DST in olive cultivation

4.2.9. Barley cultivation

The use of DSTs was evaluated in 694 barley plots between 2007 and 2018. These DSTs reduce pesticide management costs by an average of 32.5%, or €1.44 per hectare on average. An additional production of 1.3 quintals per hectare takes place on average, increasing the gross product by €19.5 per hectare on average and the gross margin by €8.94 per hectare on average.

4.2.10. Peach growing

Figure 18 – Average pesticide application costs with and without DST for peach cultivation

The use of a DST was evaluated in 28 peach plots in 2017 and 2018. This DST allows for an average reduction of 12.05% in pesticide management costs, i.e., an average saving of €133.40 per hectare, as shown in the Figure 18.

The use of DSTs reduces the amount of pesticides applied by 12.44% on average compared to not using DSTs, saving 2.95 kg of pesticides per hectare, as shown in Figure 19.

Figure 19 – Average amount of pesticides applied with and without DST for peach cultivation

4.2.11. Pistachio cultivation

The use of a DST was evaluated in four pistachio plots in 2017 and 2018. This DST allows to reduce on average by 36.46% the expenses related to pesticide management, i.e., to save 29€ per hectare on average, as shown in the Figure 20.

Figure 20 – Average pesticide application costs with and without DST for pistachio cultivation

4.2.12. Chickpea cultivation

The use of a DST was evaluated in two chickpea plots in 2018. This DST allows for an average reduction of 58.97% in pesticide management costs, i.e., an average saving of €28.75 per hectare, as shown in Figure 21.

Figure 21 – Average pesticide application costs with and without DST for chickpea

4.2.13. Potato cultivation

The use of a DST was evaluated in five potato plots in 2017 and 2018. This DST reduces pesticide management costs by an average of 6.47%, resulting in an average saving of €35.52 per hectare, as shown in Figure 22. However, the amount of pesticides recommended is on average the same as that applied without the use of a DST.

Figure 22 – Average pesticide application costs with and without DST for potato cultivation

4.2.14. Grape growing

Figure 23 – Average pesticide application costs with and without DST for grapes

The use of an DST was evaluated in 12 grape plots in 2017 and 2018. This DST reduces pesticide management costs by an average of 27.02%, resulting in an average saving of €263.44 per hectare, as shown in Figure 23.

The use of DSTs reduces the amount of pesticides applied by 12.19% on average compared to not using DSTs, saving 1.97kg of pesticides per hectare, as shown in Figure 24.

4.2.15. Stevia cultivation

The use of an DST was evaluated in two stevia plots in 2018. This DST recommends the same amount of pesticides as a standard application without an DST. Thus, the cost of the tool included in the expenses related to pesticide management with DST increases the latter compared to the expenses related to standard pesticide management.

Figure 24 – Average amount of pesticides applied with and without DST for grapes

4.2.16. Tomato cultivation

The use of a DST was evaluated in a tomato plot in 2018. This DST allows for an average reduction of 93.76% in pesticide management costs, i.e., an average saving of €311.3 per hectare, as shown in Figure 25.

Figure 25 – Average pesticide application costs with and without DST for tomato cultivation

The DST recommends no pesticide application compared to not using the DST, saving 0.65kg of pesticides per hectare, as shown in Figure 26.

Figure 26 – Average amount of pesticides applied with and without DST for tomato cultivation

4.2.17. Performance review of DST related to pesticide management

4.2.17.1. Environmental performance

The amount of pesticides applied with an DST is, on average, lower for almonds, beets, wheat, cotton, beans, olives, barley, peaches, pistachios, chickpeas, grapes and tomatoes, leading to a substantial decrease in costs. However, these amounts do not change for kiwi and stevia crops, leading to an increase in costs (related to the use of the Digital Support Tool).

The average amounts of pesticides recommended for potato cultivation with and without DST are the same, yet the average pesticide loads are lower with DST. These contradictory results reveal the need for further data agglomeration in order to have a more realistic representation of the average water consumption by these crops.

4.2.17.2. Economic performance

For almonds, beetroot, cotton, olives, peaches, pistachios, chickpeas, potatoes, grapes and tomatoes, the savings from pesticide use are greater than the cost of the DST. This is not the case for bean crop. As the same amount of pesticides are applied with or without DST for the stevia and kiwi crops, the cost of the tool is not reimbursed by the reduction in expenses.

For all these crops, no information is given on the yields obtained with or without DST. Such data would allow to measure the difference in gross product and the difference in gross margin.

In contrast to the first crops, the load difference, gross product difference and gross margin difference are given for wheat and barley. The DSTs used for these crops allow, on average, to increase the gross product and the gross margin compared to a standard production.

No conclusions can be drawn about the economic performance of crops other than wheat and barley without adding up the margin from reduced input costs and the gain from any additional production. This is especially true for the data obtained for beans, kiwis and stevia. Indeed, the cost of the tool can eventually be reimbursed by a higher gross product, obtained with the help of DST.

4.3. Fertilisation management

The environmental performance of DST related to fertilisation management is assessed from the difference in the amount of nutrient applied with or without a DST. When this data is not available, this evaluation is made from the difference in fertilisation costs. The cost of the tool is included in these costs. The economic performance is also assessed from the difference in fertilisation costs as well as from the gross products and gross margins, depending on the data available.

4.3.1. Almond cultivation

The use of a DST was evaluated in 24 almond cultivation plots in 2017 and 2018. This DST allows for an average reduction of 53.62% in fertilisation costs, i.e., an average saving of €261.25 per hectare, as shown in Figure 27.

Figure 27 – Average fertilisation costs with and without DST for almond cultivation

The use of DST reduces the amount of nutrients applied by 44.11% on average compared to not using DST, saving 131.90 kg of nutrients per hectare, as shown in Figure 28.

These observations are made on a small number of plots and years. The drastic reduction in nutrients obtained with the help of DSTs can only be assessed over a longer period of time, of at least three years.

Figure 28 – Average amount of nutrients applied with and without DST for almond cultivation

4.3.2. Wheat crop

The use of several DSTs evaluated on 3749 plots was compared to 11831 control plots for wheat cultivation over 17 years. These DSTs allow a saving of 7.65% of nutrients compared to a standard application. Without adding the cost of the tool to the fertilisation costs, a margin of 9€ per hectare is observed on average. Nutrient management with a DST reduces fertilisation costs by an average of 5.88%. Yet, the total cost per hectare of production, including the cost of the DST, increases by an average of €2.13 per hectare, as shown in the table below Figure 29.

Figure 29 – Average fertilisation costs with and without DST for wheat

But at the same time, an additional production of 3.93 quintals per hectare takes place on average thanks to the use of DST, increasing the gross product by €41.23 per hectare. An increase in gross margin of €31.97 per hectare on average is thus achieved.

4.3.3. Rapeseed crop

The use of several DST evaluated on 1383 plots was compared to 2732 control plots for oilseed rape cultivation between 2008 and 2010 and between 2017 and 2018. These DSTs reduce fertilisation costs by an average of 8.38%, i.e., an average saving of €18.26 per hectare, as shown in Figure 30. These DSTs allow for a 16% saving in nutrients, or €34.08 compared to a standard application without DST.

An additional production of 1.43 quintals per hectare takes place on average, increasing the gross product by €58.90 per hectare. An increase in gross margin of €51.20 per hectare on average is achieved.

Figure 30 – Average fertilisation costs with and without DST for oilseed rape

4.3.4. Cotton growing

The use of a DST was evaluated in 48 cotton plots in 2017 and 2018. This DST allows for an average reduction of 7.19% in fertilisation costs, i.e. an average saving of €152.97 per hectare, as shown in Figure 31.

Figure 31 – Average fertilisation costs with and without DST for cotton

The use of DST reduces the amount of nutrients applied by 41.32% on average compared to not using DST, saving 92.42 kg of nutrients per hectare, as shown in Figure 32.

Figure 32 – Average amount of nutrients applied with and without DST for cotton crop

4.3.5. Growing lettuce

The use of a DST was evaluated in two lettuce plots in 2018. This DST reduces fertilisation costs by an average of 70.46%, i.e., an average saving of €803.20 per hectare, as shown in Figure 33.

Figure 33 – Average fertilisation costs with and without DST for lettuce

The use of DST reduces the amount of nutrients applied by 77.50% on average compared to not using DST, saving 327.20 kg of nutrients per hectare, as shown in Figure 34.

Figure 34 – Average amount of nutrients applied with and without DST for lettuce cultivation

4.3.6. Olive growing

The use of a DDT was evaluated in 16 olive plots in 2017 and 2018. This DST allows for an average reduction of 6.10% in fertilisation costs, i.e. an average saving of €21.50 per hectare, as shown in Figure 35.

Figure 35 – Average fertilisation costs with and without DST in olive cultivation

The use of DST reduces the amount of nutrients applied by an average of 38.87% compared to not using DST, saving 162.75 kg of nutrients per hectare, as shown in Figure 36.

Figure 36 – Average amount of nutrients applied with and without DST in olive cultivation

4.3.7. Barley cultivation

The use of several DSTs evaluated on 202 plots was compared to 2211 control plots for the barley crop between 2008 and 2010. This DST recommends on average the same amount of nutrients as a standard application without DST. Thus, the cost of the tool included in the fertilisation costs per hectare with DST increases the latter compared to standard fertilisation costs.

An additional production of 3.5 quintals per hectare takes place on average due to the use of DST, increasing the gross product by €52.50 per hectare. An increase in gross margin of €43.50 per hectare on average is thus achieved.

4.3.8. Peach growing

The use of a DST was evaluated in 24 peach orchard plots in 2017 and 2018. This DST allows for an average reduction of 54.92% in fertilisation costs, i.e., an average saving of €538.70 per hectare, as shown in Figure 37.

Figure 37 – Average fertilisation costs with and without DST for peach cultivation

The use of DST reduces the amount of nutrients applied by 65.05% on average compared to not using them, saving 239.88 kg of nutrients per hectare, as shown in Figure 38.

Figure 38 – Average amount of nutrients applied with and without DST for peach cultivation

4.3.9. Potato cultivation

Figure 39 – Average fertilisation costs with and without DST for potato crops

The use of a DST was evaluated in 12 potato plots in 2017. This DST reduces fertilisation costs by an average of 19.58%, resulting in an average saving of €152.63 per hectare, as shown in Figure 39.

The use of DST reduces the amount of nutrients applied by 65.45% on average compared to not using DST, saving 433.36 kg of nutrients per hectare, as shown in Figure 40.

Figure 40 -Average amount of nutrients applied with and without DST for potato crop

4.3.10. Grape growing

The use of an ADO was evaluated in 24 grape growing plots in 2018. This DMO allows for an average reduction of 40.13% in fertilisation costs, i.e. an average saving of €243.30 per hectare, as shown in Figure 41.

Figure 41 – Average fertilisation costs with and without DMO for grapes

The use of DMO reduces the amount of nutrients applied by 33.56% on average compared to not using DMO, saving 74.77 kg of nutrients per hectare, as shown in Figure 42.

These observations are made on a small number of plots and years. The analysis of the difference in economic gain and inputs consumed with or without DMO can only be assessed over a longer period of at least three years.

Figure 42 – Average amount of nutrients applied with and without ADO for grapes

4.3.11. Tomato cultivation

The use of an DST was evaluated in four tomato plots in 2018. An increase of 43.60% in fertilisation costs was found on average with the use of the DST, increasing costs by an average of €217.70, as shown in Figure 43. The amount of these expenses is higher than the cost of the DST, meaning an increase in the recommended quantities of nutrients.

Figure 43 – Average fertilisation costs with and without DST for tomatoes

However, the use of DST reduces the application of nitrogen, phosphorus and potassium by 9.73%, resulting in a saving of 41.30 kg per hectare, compared to not using DST, as shown in Figure 44. The increase in fertilisation costs corresponds to the application of other nutrients, as this DST also recommends application amounts for nutrients such as iron and magnesium.

These observations are made on a small number of plots and years. The analysis of the difference in economic gain and inputs consumed with or without DST can only be assessed over a longer period of at least three years.

Figure 44 – Average amount of nutrients applied with and without DST for tomato cultivation

4.3.12. Performance review of DSTs related to fertiliser management

4.3.12.1. Environmental performance

The amount of nutrients applied with an DST is on average lower for almonds, wheat, rape, cotton, lettuce, olives, peaches, potatoes, grapes and tomatoes. These amounts are equal for the barley crop.

4.3.12.2. Economic performance

On average, the use of DSTs results in a higher gross margin, compared against not using them for wheat, barley and oilseed rape.

DSTs can reduce fertilisation costs for almonds, cotton, lettuce, olives, peaches, potatoes and grapes. This is not the case for tomato crops. For these crops the study could not be conducted by analysing the yields per hectare with or without DSTs, unlike for wheat, barley and rape. Such data would allow to measure the real difference in gross product and the difference in gross margin.

Conclusions on economic performance cannot be drawn without adding up the margin from reduced input costs and the gain from any additional production. This observation is all the more valid for the data obtained for the tomato crop. Indeed, the cost of the tool can possibly be reimbursed by a higher gross product, obtained with the help of DSTs.

4.4. Summary of DST performance by crop

In order to assess the overall performance of the DSTs for each crop, the performance related to the management of the different inputs studied is summarised by crop for almonds, wheat, cotton, olives, barley, peaches, pistachios, potatoes, grapes and tomatoes.

The performance of the DSTs was evaluated on single input management for oilseed rape, beetroot, lettuce, maize, beans, kiwi, pistachio, chickpea and stevia. Figure 5 describes the performance assessed for each crop and links to the section detailing that performance.

Table 5 referring to the sections detailing the performance of oilseed rape, beetroot, lettuce, maize, beans, kiwifruit, pistachios, chickpeas and stevia

Culture	Type of input	Part detailing the performance of ADOs
Rape	Fertilisers	4.3.2 Colza
Beets	Pesticides	4.2.2 Beet
Lettuce	Fertilizers	4.3.4 Lettuce
Maize	Irrigation	4.1.8 Maize
Beans	Pesticides	4.2.6 Beans
Kiwi	Pesticides	4.2.7 Kiwi
Chickpeas	Pesticides	4.2.12 Chickpeas
Stevia	Pesticides	4.2.15 Stevia

4.4.1. Almond cultivation

Figure 6 summarises the economic and environmental performance of DSTs for almond cultivation.

Table 6 – Economic and environmental performance of DSTs for almond cultivation

	Environmental performance		Economic performance
	Average input reduction	Average amount of inputs saved	Average input costs saved by DSTs	Average input costs saved (€.ha^-1 )
Irrigation management	31,70 %	1091.35 m³ .ha^-1	22,48%	56,40
Pesticide management	11,25%	1.07 kg.ha^-1	13,88%	34,18
Fertilisation management	42,11 %	131.9 kg.ha^-1	53,62%	261,25

4.4.2. Wheat crop

Table 7 summarises the economic performance of DSTs for wheat cultivation. Table 8 shows the maximum nitrogen requirements and the gains obtained by these requirements, according to high vegetative development, over-fertilisation, low vegetative development and under-fertilisation. In this table, the cost of nitrogen is 1.kg^-1 and the selling price of wheat is 15€.q^-1and the maximum tool cost is 15€.ha^-1.

Table 7 – Economic performance of DSTs for wheat cultivation

	Average reduction in input costs		Average input costs saved (€.ha^-1 )		Average additional yield (q.ha^-1 )	Average additional gross product (€.ha^-1 )	Additional average gross margin (€.ha^-1 )
	Durum wheat	Soft wheat	Durum wheat	Soft wheat
Pesticide management	78%	50%	32,76	21,00	2	15	21
Fertilisation management	0,51%		-7,27		3,93	54,23	44,97

Table 8 – Gains obtained for the maximum doses prescribed by the DSTs according to the condition of the wheat crop

Crop condition	Maximum recommendation (kg. ha^-1 )	Yield (q. ha^-1)	Economic gain (€. ha^-1 )*
Strong vegetative development	-20	same	5 €
Over-fertilisation	-40	same	25
Weak vegetative development	20	2	-5
Under-fertilisation	21,5	4,6	32,5
*The maximum cost of the tool is taken into account.

4.4.3. Cotton growing

Table 9 summarises the economic and environmental performance of DSTs for cotton cultivation.

Table 9 – Economic and environmental performance of DSTs for cotton cultivation

	Environmental performance		Economic performance
	Average input reduction	Average amount of inputs saved	Average input costs saved by DSTs	Average input costs saved (€.ha^-1 )
Irrigation management	42,97%	933.94 m³ .ha^-1	44,03%	677,72
Pesticide management	50,61%	2.07 kg.ha^-1	30,81%	97,27
Fertilisation management	41,32 %	92.42 kg.ha^-1	7,19%	152,97

4.4.4. Olive growing

Table 10 summarises the economic and environmental performance of DSTs in olive cultivation.

Table 10 – Economic and environmental performance of DSTs in olive growing

	Environmental performance		Economic performance
	Average input reduction	Average amount of inputs saved	Average input costs saved by DSTs	Average input costs saved (€.ha^-1 )
Irrigation management	32,50%	285.86 m³ .ha^-1	25,62%	182,32
Pesticide management	37,66%	4.77 kg.ha^-1	64,46%	212,62
Fertilisation management	38,87%	162.75 kg.ha^-1	6,10%	21,50

4.4.5. Barley crops

Table 11 summarises the economic and environmental performance of DSTs for barley cultivation. Table 12 shows the maximum nitrogen requirements and the gains obtained by these requirements, depending on high and low vegetative development. In this table, the cost of nitrogen is 1.kg^-1 and the selling price of barley is 15€.q^-1 and the maximum tool cost is 15€.ha^-1 .

Table 11 – Economic performance of DSTs for barley cultivation

	Average input reduction	Average input costs saved (€.ha^-1 )	Average additional yield (q.ha^-1 )	Average additional gross product (€.ha^-1 )	Additional average gross margin (€.ha^-1 )
Pesticide management	65%	15,3*	1,3	19,5	12
Fertilisation management	0%	0	3,50	52,50	43,50
*The price of the DST is included in the charges

Table 12 – Gains obtained for the maximum rates prescribed by the DSTs according to the condition of the barley crops

Crop condition	Maximum recommendation (kg. ha^-1 )	Yield (q. ha^-1)	Economic gain (€. ha^-1 )*
Strong vegetative development	-20	same	5
Weak vegetative development	20	3,5	17,5
*The maximum cost of the tool is taken into account.

4.4.6. Peach growing

Table 13 summarises the economic and environmental performance of DSTs for peach cultivation.

Table 13 – Economic and environmental performance of DMOs for peach cultivation

	Environmental performance		Economic performance
	Average input reduction	Average amount of inputs saved	Average input costs saved by DSTs	Average input costs saved (€.ha^-1 )
Irrigation management	18,54%	422.58 m³ .ha^-1	12,74%	96,04
Pesticide management	12,44%	2.95 kg.ha^-1	12,05%	133,41
Fertilisation management	65,05%	239.88 kg.ha^-1	54,92%	538,70

4.4.7. Pistachio cultivation

Table 14 summarises the economic and environmental performance of DSTs for pistachio cultivation.

Table 14 – Economic and environmental performance of DSTs for pistachio cultivation

	Environmental performance		Economic performance
	Average input reduction	Average amount of inputs saved	Average input costs saved by DSTs	Average input costs saved (€.ha^-1 )
Irrigation management	24,61%	60 m³ .ha^-1	17,61%	49,60
Pesticide management	No data		36,46%	29

4.4.8. Potato cultivation

Table 15 summarises the economic and environmental performance of DSTs for potato cultivation.

Table 15 – Economic and environmental performance of DSTs for potato cultivation

	Environmental performance		Economic performance
	Average input reduction	Average amount of inputs saved	Average input costs saved by DSTs	Average input costs saved (€.ha^-1 )
Irrigation management	32,56%	1220.5 m³ .ha^-1	27,44%	107,50
Pesticide management	0%	0 kg.ha^-1	6,47%	35,52
Fertilisation management	65,45%	433.36 kg.ha^-1	19,58%	152,63

4.4.9. Grape growing

Table 16 summarises the economic and environmental performance of DSTs for grape growing.

Table 16 – Economic and environmental performance of DSTs for grape growing

	Environmental performance		Economic performance
	Average input reduction	Average amount of inputs saved	Average input costs saved by DSTs	Average input costs saved (€.ha^-1 )
Irrigation management	42,71%	782.8 m³ .ha^-1	41,97%	1155,34
Pesticide management	12,19%	1.97 kg.ha^-1	27,02%	263,44
Fertilisation management	33,56%	74.77 kg.ha^-1	40,13%	243,30

4.4.10. Tomato cultivation

Table 17 summarises the economic and environmental performance of DSTs for tomato cultivation.

Table 17 – Economic and environmental performance of DSTs for tomato cultivation

	Environmental performance		Economic performance
	Average input reduction	Average amount of inputs saved	Average input costs saved by DSTs	Average input costs saved (€.ha^-1 )
Pesticide management	100%	0.65 kg.ha^-1	93,96%	311,3
Fertilisation management	9,73%	41.30 kg.ha^-1	-43,60%	-217,70

5. Discussion

5.1. Performance of DSTs

5.1.1. Environmental performance

The quantities of water, pesticides and the main nutrients (nitrogen, phosphorus and potassium) recommended by the DSTs are on average less than or equal to the quantities applied with traditional methods.

5.1.2. Economic performance

The economic performance of a DST can be due to a reduction in input costs and an increase in yield, and, therefore, gross product. Only gross products are given for wheat, rape and barley. The gross margins for these crops are positive for all three types of inputs studied.

The economic performance of DSTs for almonds, beetroot, cotton, beans, kiwifruit, lettuce, maize, olives, peaches, pistachios, chickpeas, potatoes, grapes, stevia and tomatoes was assessed using the cost of production, in the absence of the raw products for these crops.

For all the inputs studied, the quantities recommended by the DSTs for these crops generally allow a reduction in the cost of production. As the cost of the DSTs is included in the cost of production, the input savings achieved with the help of the technology ensure a return on investment.

The return on investment of the tool through the cost of production does not occur when the recommended quantities are equal to the quantities usually applied. This is notably the case for the quantities of pesticides recommended on average for kiwi and stevia crops. It does not occur either when the amount of expenses saved by the use of a DST is lower than the cost of the DST. This is the case for the bean crop for pesticide management.

5.1.3. Limitations and perspectives of the analysis

The measurement of environmental performance is based on the analysis of the quantities of inputs used. For pesticide management, quantitative data are not available for all crops. It would be useful to obtain data for all crops in order to be able to assess the environmental performance of each crop in a consistent manner.

Access to the raw product would allow full quantification of economic performance for each crop based on gross margin. It would then be possible to know whether investments in DSTs are cost-effective for pesticide management of beans, kiwis and stevia crops, through the additional gross revenue generated.

The yields obtained are influenced by the interaction of many factors, including the inputs applied. Inputs not considered in the analysis may come into play, as in the case of fertilisation management for the tomato crop.

Data must be aggregated over at least three years to take into account interannual variability (Lebacq, Baret and Stilmant, 2013). This is the case for wheat, barley and beet crops, whereas data for other crops are only aggregated over one or two years. Similarly, the number of land plots studied should be as large as possible. The economic and environmental performance of DSTs studied on less than 30 different plots is not very representative. This is the case for almonds, cotton, beans, kiwis, olives, peaches, pistachios, chickpeas, potatoes, grapes, stevia, and tomatoes.

The averages for each crop are obtained from data from France and Greece. The evaluation of the performance of precision agriculture is intended to be valid on a European scale, but the quantities of inputs applied are highly dependent on climatic conditions. For this reason, it would be useful to supplement the data obtained for each crop with data from other European regions or other countries.

This study does not cover all the components of digital agriculture. It illustrates the concrete results of precision agriculture on farms. These tools have, a priori, an economic and environmental interest that confirms the performances observed during the experiments. The orders of magnitude obtained for the percentages of savings need to be refined. This is why it would be interesting to continue this work by aggregating the data that will be obtained in the years to come and by expanding the number of crops analysed, the number of regions and the number of plots studied.

5.1.4. Assessment of the performance of DSTs

Adjusting input amounts at the inter- or intra-plot scale limits leaching risks. The use of pesticides adapted to pressures reduces the presence of chemical residues while ensuring production. Automated precision farming or farming coupled with robotics would be more precise and could reduce traces of input residues to zero. However, these tools are expensive and do not yet provide a return on investment (Zarco-Tejada, Hubbard and Loudjani, 2014) unlike precision farming tools, such as sensors, weather stations, satellite images, cameras, and input management DSTs.

The reduction in the amount of inputs often achieved shows that agriculture can produce more with less. With the help of precision tools, used in levels 1, 2 and 3 of precision agriculture, agriculture can contribute to food safety and food security.

Other benefits on soil quality are obtained for higher levels, using automated driving and robotisation. These include a reduction in soil compaction due to less frequent use of lighter farm machinery (Zarco-Tejada, Hubbard and Loudjani, 2014).

5.2. Consequences of the implementation of digital agriculture

Digital farming will affect many aspects, first and foremost working conditions and farming practices. Farms may gain in sustainability, but in return they may become more dependent on consultancies. On a larger scale, food security can be enhanced, as can social cohesion. However, traditional knowledge and the use of local genetic resources will need to be preserved. These effects depend in part on the accessibility of techniques, the transparency of algorithms and the good governance of the data shared and held (Kritikos, 2017).

5.2.1. Impact on jobs

There are mixed views on the impact of the advent of digital farming on agricultural jobs. Some fear that the fall in the number of farmers will get worse (Kritikos, 2017). Others argue that digital farming would increase employment in rural areas and in the agricultural sector, particularly through the attraction generated by the use of digital technology (Schrijver, 2016).

Precision farming tools – sensors, weather stations, satellite images, cameras, and input management DSTs – do not replace farmers’ work in the way that robots might. Implemented in levels 1, 2 and 3 of digital farming, they do not have such an impact on farming jobs.

Jobs are also being created in other sectors. Digital agriculture offers the agricultural supply sector new avenues of conversion, in line with societal expectations related to the reduction of input use.

5.2.2. Digital agriculture in relation to farming systems

Digital farming is seen as cutting across agricultural systems. The tools offered by digital farming can be implemented in all farming systems.

Although digital farming can increase input efficiency, it can be implemented in agricultural systems whose practices can sometimes be harmful to the environment. This is particularly the case in conventional agriculture, where monoculture remains a widespread practice (Kritikos, 2017).

The development of digital agriculture would therefore be done in parallel with the promotion of farming systems that are both economically and environmentally efficient. The latter will help to strengthen the environmental performance of farms, as will the implementation of measures to protect the environment and increase biodiversity.

5.2.3. Digital agriculture and the diversity of European farms

The size and structure of farms in Europe are very diverse. At present, digital tools are mainly used on large farms that can make the investment profitable on their own (Kritikos, 2017).

Alternatives exist to facilitate access to these tools for small farms. Such investments can be made jointly through cooperatives or farmer groups. These investments can also be supported by a third party organisation, as it is the case for GAIA. The size of the farm is therefore not the limiting factor for the development of digital farming as such. Rather, it is the management and organisation mode.

5.3. Stimulating the transition to digital agriculture

Digital farming can boost the economic and environmental performance of farms. It also has the potential to simplify the administration of the CAP and to more accurately measure the effects of policies such as the Eco-schemes.

In view of the potential of digital agriculture at farm level and at EU political one, it seems worthwhile for the CAP and for a specific European plan of investment in Agriculture to support investment in the transition to these tools. The Commission proposed a recovery plan of €20 billions for Agriculture. European council reduced it to €8 billions to be shared between MS which used them to finance some priorities not only innovation and investments. Thus, an investments plan of at least €15 billions is still needed to support precision and digital transition of EU farms and training for farmers.

Conclusion

Among the digital tools, the ones related to precision agriculture make it possible to combine productivity and sustainability in Europe. These sensors, weather stations, satellite images, cameras, and input management DSTs correspond to levels 1, 2 and 3 of digital agriculture.

They seem to ensure, at the European farm level, a more efficient management of inputs and an increase in yields. These elements guarantee a return on investment, and confirm the performances observed during the experiments. The measurement of these performances should be continued in the coming years, by extending the number of crops, the number of regions and the number of plots studied, in order to refine the orders of magnitude.

These digital tools are transversal to agricultural systems.

The generalisation of digital tools on a European scale would also make it possible to reduce the administrative burden of the CAP. The data generated by such tools could be used to feed indicators that would automatically assess the environmental performance of farms with much greater accuracy.

Digital farming can therefore boost the economic and environmental performance of farms. It also has the potential to simplify the administration of the CAP and to more accurately measure the effects of policies such as the Eco-schemes.

In view of the potential of digital agriculture at farm level and at the level of the CAP, it seems worthwhile for the CAP to support investment and the transition to these tools.

This support can be envisaged in both pillars of the CAP. It could take place through flat-rate transition incentives in the framework of the Eco-schemes, and through the creation of a priority support for green double performance investments in agriculture allowing to support a European plan of 20 billion € of such investments over the next 7 years (investments financed at 50% by the CAP by mobilising 3% of the CAP envelope in each Member State).

Certifications ensuring that these tools are used in good manners and conditions could complement this support.

Digital agriculture also presents question marks in Europe. Threats related to the management of farm data, autonomy and the position of farmers in relation to other actors, are risks to be considered in the context of digital development. In order to benefit from these tools and minimise the threats to individual farms, it is necessary to put in place, in parallel, a European legislative framework concerning its use. The promotion of quantified benefits and training for farmers should be developed.

Bibliography

Arvalis (2019) OAD – Crop management. Available at: https://www.arvalis-infos.fr/tous-les-outils-d-aide-a-la-decision-conduite-des-cultures-@/view-1457-arvstatiques.html (Accessed: 23 August 2019).

Bourguignon, D. (2017) EU Pesticide Policy and Legislation: Plant Protection Products and Biocides. Brussels. Available at: http://www.europarl.europa.eu/thinktank.

Brown, A. et al (2018) Threats to Precision Agriculture. Available at: https://www.dhs.gov/sites/default/files/publications/2018 AEP_Threats_to_Precision_Agriculture.pdf.

COGECA, C. (2018) ‘EU Code of conduct on agricultural data sharing by contractual agreement’, Copa Cogeca, pp. 1-11. Available at: http://cema-agri.org/sites/default/files/publications/EU_Code_2018_web_version.pdf.

COMAGRI (2019) The Common Agricultural Policy After 2020: Environmental Ambition and Simplification. Available at: https://ec.europa.eu/info/sites/info/files/food-farming-fisheries/key_policies/documents/cap-post-2020-environ-benefits-simplification_fr.pdf.

data agri (2018) Charter on the use of agricultural data.

DGCOMM (2016) Europeans, Agriculture and the Common Agricultural Policy (CAP). Brussels.

EU Member States (2019) A smart and sustainable digital future for European agriculture and rural areas.

European Commission (2018) Regulation of the European Parliament and of the Council laying down rules governing support for strategic plans to be drawn up by Member States under the common agricultural policy (‘CAP strategic plans’). Brussels.

European Commission (2019) Common Agricultural Policy. Available at: https://ec.europa.eu/info/food-farming-fisheries/key-policies/common-agricultural-policy_fr (Accessed: 25 July 2019).

Farm Europe (2019) Adopted positions by the Agriculture Committee of the European Parliament Next steps.

Ivanova, I. et al. (2018) Broadband in the EU Member States: despite progress, not all the Europe 2020 targets will be met.

Jereb, S. et al. (2017) Greening: increased complexity of the income support scheme and still no environmental benefit.

Kernecker, M. et al. (2019) ‘Experience versus expectation: farmers’ perceptions of smart farming technologies for cropping systems across Europe’, Precision Agriculture. Springer US, pp. 1-17. Available at: https://doi.org/10.1007/s11119-019-09651-z.

Kritikos, M. (2017) Precision agriculture in Europe: Legal, social and ethical considerations. Brussels. Available at: http://www.europarl.europa.eu/RegData/etudes/STUD/2017/603207/EPRS_STU(2017)603207_EN.pdf.

Lebacq, T., Baret, P. V. and Stilmant, D. (2013) ‘Sustainability indicators for livestock farming. A review’, Agronomy for Sustainable Development, 33(2), pp. 311-327. doi: 10.1007/s13593-012-0121-x.

United Nations (2019) Sustainable Development Goals. Available at: https://www.un.org/sustainabledevelopment/fr/ (Accessed: 1 July 2018).

Panagos, P. et al (2019) Cap Specific Objectives, brief 5 – EFFICIENT SOIL MANAGEMENT.

Paraforos, D. S. et al. (2019) ‘Agricultural data ownership and use: digital farming perspective’, in Montpellier, S. (ed.) Poster Proceedings of the 12th European Conference on Precision Agriculture, July 8-11, Montpellier, France. Montpellier, pp. 144-145.

Say, S. M. et al (2018) ‘The Online Journal of Science and Technology’, TOJSAT, 8(1), pp. 7-15.

Schrijver, R. (2016) Precision agriculture and the future of farming in Europe. Brussels.

Souza, E. et al. (2019) ‘AGDATABOX: Web platform of data integration, software and methodologies for digital agriculture’, in SupAgro Montpellier (ed.) Poster Proceedings of the 12th European Conference on Precision Agriculture, July 8-11, Montpellier, France. Montpellier.

Zampti, F. (2019) ‘Ethical and legal aspects of open data in agriculture and nutrition’, in SupAgro Montpellier (ed.) Poster Proceedings of the 12th European Conference on Precision Agriculture, July 8-11, Montpellier, France. Montpellier, pp. 198-199.

Zarco-Tejada, P. J., Hubbard, N. and Loudjani, P. (2014) Precision agriculture: an opportunity for Eu farmers-potential support with the CAP. Brussels.

[1] The latter corresponds to a set of basic standards, GAEC, Good Agricultural Environmental Conditions, and SMR, Regulatory Management Requirements, which farmers receiving CAP support must respect (COMAGRI, 2019).

[2] Agri-environmental and climate commitments are payments per hectare or per head, based on the implementation of environmentalpractices over several years (COMAGRI, 2019).

[3] Term encompassing plant protection products (mainly herbicides, fungicides and insecticides) and biocides (Bourguignon, 2017).

Characteristics of the main agricultural systems

December 2019

Introduction

The CAP reform project currently under discussion introduces a new “Ecoscheme”. This measure, financed by the first pillar of the CAP, aims to stimulate the transition to a more environmentally friendly and economically viable agriculture in Europe. In view of the diversity within European agriculture, to be effective this tool will have to be adapted to national or regional challenges.

To this end, it will have to ensure a strong common framework both for the concept to be promoted and for the objectives to be achieved.

Therefore, a preferred approach seems to be to conceive this Ecoscheme tool as an incentive for the transition to certain agricultural systems that bring explicit environmental benefits and integrate the need for an European agriculture that must return to a fairly blunt profitability.

Each agricultural system promotes a set of practices to achieve a specific objective, which distinguishes it from other systems.

By supporting the transition to environmentally friendly and economically viable farming systems, the Ecoscheme tool will provide concrete support for practices to be implemented. These practices can then be adapted according to local needs and constraints as soon as they provide at least directly or on an equivalent basis the benefits expected from all farmers as defined at European level.

A comparison of the economic and environmental performance of different systems will help to identify those most likely to participate in this transition. In this context, it is essential to understand the characteristics of agricultural systems, as well as the practices they support.

The systems studied in this document correspond to those recognized internationally. These are conventional agriculture, sustainable agriculture, agro-ecology, integrated agriculture, soil conservation agriculture, and organic agriculture. Digital agriculture, which includes a set of tools applicable in all systems, is not detailed in this section. It will be the subject of a specific analysis with regard to its horizontality.

This qualitative study makes it possible to understand the aims and practices implemented by agricultural systems. The strengths and weaknesses of the latter are also detailed. The practices listed for each system are based on a literature search. Except those mentioned in the specifications, they do not have to be applied simultaneously. The reasons that may hinder the implementation of the latter are then detailed, followed by the economic and environmental benefits they allow.

full study available on Farm Europe Members Area (in french)

DO NECPs FROM THE 28 MEMBER STATES MEET EU TRANSPORT DECARBONISATION TARGETS?

According to the Governance of the energy union and climate action rules, Member States are required to establish a 10-year Integrated National Energy and Climate Plan (NECP) for the period from 2021 to 2030 in order to meet the EU’s new energy and climate targets for 2030. These Plans shall cover the five dimensions of the energy union on a common template so that the European Commission can be able monitor EU wide progress (as a whole) towards achieving these targets.

The draft NECPs of the Member States were already assessed and analysed by the Commission and the Member States have until the end of 2019 to submit their final, modified NECPs accordingly.

This study aims to contribute to this discussion on the state of the draft NECPs from the perspective of the transport sector. It examines all the 28 draft NECPs from this point of view, underlining the importance of the need to make vital efforts in this area for Europe to succeed and reach its energy and climate targets. It focuses on the question whether or not the proposed measures, ambitions and tools in these draft Plans are truly able to effectively contribute to the decarbonisation of the European transport sector.

Results of these study will be discuss in the Euractiv event on November, 27th

THE US RETALIATION LIST ON THE AIRBUS SUBSIDIES CASE

THE HEAVILY IMPACTED AGRICULTURE SECTOR SHOULD GET MUCH NEEDED HELP

The US retaliation list has been published, and as unfortunately I had anticipated it is loaded against EU agriculture exports to the US.

Although the dispute is on aircraft subsidies, agriculture leads the list of the products targeted, with roughly the double of the trade impact than all the others products added together, including aircraft.

The retaliation list is also heavier on additional tariffs for the agriculture products than for aircraft – 25% as compared to 10%.

The US has been careful to minimize the impact on its economy by exempting some items that are imported bulk and bottled in the US. It has also left room for expansion and/or deepening of the sanctions, either by expanding the product coverage, or by increasing the amount of the additional tariffs. That might be a tactical move to prevent or anticipate any EU retaliation prior to the expected decision on the “sister” case against Boeing, and to increase its negotiation leverage then after.

Dairy is heavily impacted, as are wine and whiskey, olive oil and olives, pork, hams, biscuits, jams and some fruits.

The EU dairy sector, including many cheese GIs, bears most of the blunt. The impact of a 25% additional tariff on EU exports is not easy to anticipate. It will be a mix of lower exports as products become more expensive in the US, and a reduction of margins for EU exporters, eager to minimize loss of volumes and markets even if that means giving up on profits.

In the end there is little doubt that the negative impact will be passed on to the producers, negatively impacting their incomes.

What also strikes me as well is the lack of support to cushion the sector from this blow. Whereas in the US the negative effects of the US-China trade war on US farmers were compensated twice by sizeable subsidies, in the EU there is no such talk, no initiative on sight from the European Commission. Could the fact that the current Commissioner for Agriculture will be the next Trade Commissioner help in proposing a package of support to those affected, in particular to the farmers? Let us not forget that they had no part on the subsidies found illegal by the WTO, but they are the ones who will lose the most.

The European Commission should calculate the actual loss of agriculture exports targeted by the retaliation list to the US, in the months to come, and devise a support package that would compensate the farmers affected. It should not just monitor the EU domestic markets and intervene only when drastic price drops are on the record, as that will miss the actual losses of those affected by the trade retaliation.

By Joao Pacheco

BREXIT – A LAST CHANCE FOR AN EXIT DEAL?

We are now barely 3 weeks before the (latest) deadline for Brexit, which should concentrate minds, in particular of those with political responsibilities, on how best to unlock a reasonable solution.

The new element is the latest UK proposal, which departs from the previous signed but never approved withdrawal agreement.

The new proposal accepts an all-Irish alignment with the EU norms and standards, in particular on animal and plant health, but extricates the whole of the UK territory from the Customs Union. That entails the need for customs controls between the Republic and Northern Ireland, but according to the new proposal that could be met to a large extent by electronic means and controls outside the border, in order to prevent the unacceptable (to all parties) re-imposition of a hard border.

In my view the right way to approach this new UK proposal is to access its own merits, and then compare it with the appropriate benchmark. This is an important point, as the choice of the appropriate benchmark can go a long way towards accepting the new proposal as a basis for negotiation, or on the contrary to clearly refuse it.

If the benchmark is the signed withdrawal agreement that was not adopted by the British Parliament, this new proposal falls short in terms of guarantees it brings to the EU agriculture sector, as it acknowledges a number of holes in the customs controls, and puts the faith of the whole agreement on the hands of the Northern Ireland’s Assembly.

Before moving further let me be clear: the preferred solution would be … no Brexit. But now with the finishing line at sight, I believe we need less wishful thinking and more realistic and pragmatic solutions.

But if the benchmark is a no-deal Brexit, as the UK Government says, the new proposal should be weighed in its own merits against the effects of a no-deal.

I am aware that the political process in the UK is quite messy, and that political events might change the scenario, namely by the provoking the fall of the current UK Government. But rather than engaging in political speculation, I prefer to keep my comments on what is on the table right now.

On the merits of the new UK proposal, or better on its main flaws, what comes first is the design for customs controls between the EU 27 and the UK in Ireland.

The UK proposes that small firms be exempted, or to put it differently proposes that low-scale smuggling be de facto accepted. As regards more significant trade flows the new proposal bets on a mix of electronic, trusted operators and outside the border controls.

It would become a test for a radically different customs control system. It might control the most significant flows, but it hasn’t been tested.

In the new proposal the obligatory customs declaration would stay, but the means to enforce it would be by electronic tagging of transport and a number of physical controls outside the border. Thus the new system would be less tight.

However I would put that into perspective. Currently our customs controls are done by an obligatory declaration of the goods, and payment of customs duties and VAT as appropriate, but only a small fraction is actually physically controlled.

The other major flaw of the new proposal is that it gives the Assembly of Northern Ireland the right to refuse the agreement, when it would have been agreed by the states – the EU 27 and the UK. That is a highly political issue, but I believe that would have to go if the proposal is to be accepted.

On the positive side, the new proposal gives guarantees on the respect of the norms and standards of our single market, and paves the way to negotiating a Free Trade deal between the EU 27 and the UK.

Now, coming back to the new proposal and the benchmark of a no-deal, what is at stake in my view is accepting a less tight customs controls, and therefore facing to a certain extent duty evasion (on both directions) or bracing for the shock of a no-deal Brexit and a high tariff wall across the Channel. The duty evasion problem, which is real, would however be rendered irrelevant by a large Free Trade deal that would eliminate all tariffs.

I will not come back to the disaster for the EU 27 agriculture sector of a no-deal – Farm Europe has dwelt extensively on that.

To conclude I would hope that the time has come for a last ditch negotiating effort from both sides, eliminating as far as possible the flaws in the latest UK proposal. Rather than betting that a political U-turn in the UK means an end to Brexit, or brings back the previous withdrawal agreement back.

How can the EU avoid actually importing deforestation?

Deforestation and EU imports of agri-food products

How can the EU avoid actually importing deforestation?

September 2019

The recent forest fires in the Amazon have put a renewed focus on the loss of forests at a global level, and on its connection to the EU’s international trading policy on importing various agri-food products that are linked to deforestation.

Since mid-July thousands of fires have been burning in the Amazon, destroying the habitat of the world’s largest rainforest. According to the Brazilian National Institute for Space Research (INPE), there were over 80 percent more forest fires than in the same period of the previous year. Images of the burning forests, smoke and a black sky over Sao Paulo have circulated the Internet resulting in increased public concern, culminating in an outcry from the international community. Several world leaders, such as UN Secretary-General António Guterres, the Pope and the G7 Summit, have joined NGOs in calling for a global commitment to more effectively and efficiently fight the fires.

The current fires in the Amazon renew attention to the loss of forests at global level.

If the loss of forests is not new, it has to be acknowledged that it now happens at an alarming rate. INPE’s latest preliminary data on the loss of trees in the Amazon show that an area of 1145 km2 – almost as big as Greater London – was cleared just in August, making it the highest level in the past five years. 1

In 1990 the world had 4 128 million ha of forest; in 2015 this area had decreased to 3 999 million ha2, and the latest FAO Report on the State of the World’s forests (2018) states that the “total area of the world’s forests is shrinking day by day”. During this period the largest forest area losses occurred in the tropics, particularly in South America, Africa and Indonesia.

	Forest/land area	Forest area
UN Forest Cover	2015	1990	2000	2005	2010	2015	Change 1990-2015
Country	%	(1000 ha)	(1000 ha)	(1000 ha)	(1000 ha)	(1000 ha)	(1000 ha)
Angola	46.4	60976	59728	59104	58480	57856	-3120
Brazil	59	546705	521274	506734	498458	493538	-53167
Cameroon	39.8	24316	22116	21016	19916	18816	-5500
Colombia	52.7	64417	61798	60201	58635	58502	-5915
Congo	65.4	22726	22556	22471	22411	22334	-392
Côte d’Ivoire	32.7	10222	10328	10405	10403	10401	179
DRC	67.3	160363	157249	155692	154135	152578	– 7785
Ecuador	50.5	14631	13729	13335	12942	12548	-2083
Honduras	41	8136	6392	5792	5192	4592	-3544
Indonesia	53	118545	99409	97857	94432	91010	-27535
Malaysia	67.6	22376	21591	20890	22124	22195	-181
Nigeria	7.7	17234	13137	11089	9041	6993	-10241
Paraguay	38.6	21157	19368	18475	16950	15323	–5834

(Data from the UN FAO 2015 Forest Resources Assessment)

According to FAOSTAT, while in 1990, Brazil was 65,41% covered by forests, this dropped to 59,05% in 2015. The same dramatic trend can be seen in Indonesia as well: 65,44% in 1990 and 50,24% in 2015.

FAO’s 2015 Global Forest Resources Assessment shows that agriculture is expanding at the expense of forests in countries located in South America (e.g. Argentina, Brazil), South-East Asia (e.g. Indonesia, Malaysia, Thailand) and West & Central Africa.

The State of the World’s Forests 2018 concludes that “one of the great challenges of our times is the question on how to increase agricultural production and improve food security without reducing forest area.“ 3

Besides wildfires and illegal logging, the causes of deforestation – the conversion of forest to other land use or the long term reduction of the tree canopy cover below a minimum 10 percent threshold – are numerous and include the conversion of forests mainly for agricultural purposes, mining, infrastructure development and urban growth. Some of these conversions can even occur with the support of national authorities as for example most recently Brazilian President Jair Bolsonaro has defended the opening of indigenous lands for mining and threatened to pull Brazil out of the Paris Agreement.

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These practices are global and can also be observed in Central Africa, which due to the Amazon fire coverage has gained more attention recently as satellite images show intense fires near the Congo Basin. 4

According to UNEP5, besides illegal logging and fires, widespread investments in palm oil plantations are the leading cause of rainforest destruction in South-East Asia. Indonesia and Malaysia are the world’s leading exporters of palm oil, with production skyrocketing. Based on FAOSTAT, the area under palm cultivation expanding by a factor of 7 times in Indonesia (from 1190000 ha to 8630000 ha) and almost doubled in Malaysia (from 2540087 ha to 4859397 ha) between 1995 and 2015. Greenpeace and Forum for Environment (Walhi) argue that due to its loopholes, Indonesia’s legal moratorium on converting primary natural forests and peatlands to palm and logging concessions has been ineffective. The European Commission’s Report on the status of production expansion of relevant food and feed crops worldwide (2019) states that palm oil is the highest ILUC-risk biofuel feedstock.6

Economic losses from weather and climate-related events already average EUR 12 billion per year in the EU (EUR 426 billion – at 2017 values – between 1980 and 2017)7, which is only set to increase in the future if no action is taken.

The EU is well aware of the situation and is committed to act against it. Just recently the European Commission has published its Communication “on stepping up EU Action to Protect and Restore the World’s Forests”, in which it calls for “a variety of regulatory and non-regulatory actions” and proposes a list of initial actions to reach its two-fold objective of protecting existing forests and increasing global forest coverage. In doing so, the Commission sets out five priorities, including reducing the EU consumption footprint on land and encouraging the consumption of products from deforestation-free supply chains.

Through its trade and consumption of various agri-food products, the EU causes deforestation. In its resolution on palm oil and deforestation of rainforests, the European Parliament noted that a little under one quarter (by value) of all agricultural commodities in international trade obtained from illegal deforestation is destined for the EU. 8 Such previously identified products coming from agriculture include palm oil, soy, rubber, beef, maize, cocoa and coffee. 9

The origin of the goods and services consumed in the EU27 that were associated with deforestation (between 1990-2008) point towards South America and South- East Asia. For Southeast Asia, palm oil is the main source of deforestation related to EU imports. For South America, this is primarily beef and soy.

This trade could be expanded under the proposed EU-Mercosur Trade Agreement. Experts have well described the vicious circle of the Amazonian forests being deforested due to illegal logging of a few high value trees and the burning of other low-value trees into charcoal sold to the iron and steel industries, with the land cleared being then used as pasture for beef production. In this disaster in the making for the climate and our planet, the share of deforestation attributable to farmers, including small farmers, whose production consolidates the overall supply of meat from Brazil and its ability to export, can’t be denied.

In the context of the proposed EU-Mercosur Trade Agreement the export of beef meat is planned to increase and additional beef production in the Amazon – even if only consumed in the domestic market – frees up more beef produced in Center and South Brazil for export too.

During the Commission’s publication of the Forest Communication, Commissioner Jyrki Katainen defended the Mercosur deal by stating that it requires Paris Agreement compliance and contains a “strong, robust sustainable development chapter, which gives the EU a stronger hand to have a political dialogue on sustainability related issues”. Hence he insisted that the deal gives the EU more influence in preventing deforestation in Brazil.

The Trade and sustainable development (TSD) chapter 10 of the agreement includes indeed Articles on biodiversity, environment and climate, which state that each Party shall effectively implement the Paris Agreement, which both blocs signed. Nevertheless there are today no further concrete instructions or control/reporting mechanisms on the ‘how’ part within the Agreement. The Paris Agreement includes a pledge to stop illegal deforestation in the Amazon by 2030, but the opposite is happening.

Due to their importance to the Earth’s ecosystem, rainforests like the Amazon, Borneo or the Congo Basin are a universal common good and concern of all humanity and should be preserved accordingly. 11

Accordingly, if the EU wishes to be a world leader in fighting climate change it must do more.

One way is to explicitly declare and follow-up its ‘zero-deforestation’ policy and commit to a ‘zero-deforestation’ supply chain. For that, it needs to find a way to:

value the preservation of rainforests more than the products that originate from its destruction.
stop imports of goods linked with deforestation, and put efficient safeguard mechanisms to be activated at any time by the EU on the basis of objective data and reports that the EU should revisit every 6 months.

Both for its imports of biofuels and feedstocks used for biofuel production in the EU and for imports of agricultural and food products from areas at risk of deforestation, the European Union should either build a robust system of deforestation free import certificationor an efficient system of verification of deforestation free export certificates made by exporting countries. Such certifications would be the first sine qua non for authorization of entry of these products into the territory of the European Union.

On top of that publicly accessible data on the matter at hand would make sense and put Europe in a perspective of co-construction and search for solutions as it could help to prevent the start of deforestation of intact forests and contribute to work together in order to develop “local” regional strategies.

Proposal for specific conditions for an efficient and trustworthy EU deforestation certification scheme (safeguard clause):

Every 6 months, the European Commission should present a report covering the trends related to deforestation and the expansion of deforestation risk related products into high carbon stock areas including forests and peatlands. The European Commission should be empowered to trigger a safeguard clause allowing the European Union to suspend the deforestation free certificates in regions or countries, where deforestation is observed. The safeguard clause should be applied at an appropriate geographic level in order to cover indirect effects and potential market transfers.

This would mean that products in zones that have proven on-going deforestation (‘red zones’) should then be blacklisted and EU customs should block the imports from those regions/products. In order to be consistent with the principle that the EU shall not make any compromises on the issue of deforestation no exemptions shall be given to such products. This would require an improved system of information and transparency.

For proving such practices field inspections would be almost impossible to carry out. Therefore the unbiased monitoring of forest cover change through satellite imagery seems to be the most appropriate methodology to follow deforestation, degradation and the state of the forests. Such technologies have been developed by European enterprises such as Copernicus or Starling, used in particular by companies as part of their Zero Deforestation commitments.

The EU could accept, and even support, equivalent systems to monitor deforestation implemented by the concerned countries, if those are also based on objective and verifiable satellite imagery and open to auditing. This would represent a welcome step towards empowering countries where deforestation has been a plague to take the matter on their hands and implement the appropriate mix of control, economic, social and environmental policies to halt deforestation and forest degradation. In this context the EU could also support measures that aim at increasing agriculture productivity, which would ultimately reduce the economic and social pressure to deforestation and use of peatlands.

The EU needs to step up its game to turn words into concrete actions with effectively working measures – starting by stopping the importation of deforestation sourced products – if it wishes to honour its pledge to halt deforestation by 2020 as stated in the New York Declaration on Forests12 and the UN Sustainable Development Goals (Goal Number 15.2).

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1 http://terrabrasilis.dpi.inpe.br/app/dashboard/alerts/legal/amazon/aggregated/

2 FAO Global Forest Resources Assessment 2015

3The State of the World’s Forests 2018 – Forest pathways to sustainable development. FAO (2018)

http://www.fao.org/3/I9535EN/i9535en.pdf

4https://firms.modaps.eosdis.nasa.gov/map/
5 “The Last Stand of the Orangutan- State of Emergency: Illegal Logging, Fire and Palm Oil in Indonesia’s National Parks http://wedocs.unep.org/xmlui/bitstream/handle/20.500.11822/7524/- The%20Last%20Stand%20of%20the%20Orangutan- %20%20State%20of%20Emergency_%20Illegal%20Logging%2c%20Fire%20and%20Palm%20Oil%2 0in%20Indonesia%27s%20National%20Parks-2007756.pdf?sequence=2&isAllowed=y
6European Commission: Report on the on the status of production expansion of relevant food and feed crops worldwide (2019) https://ec.europa.eu/energy/sites/ener/files/documents/report.pdf

7https://www.eea.europa.eu/data-and-maps/indicators/direct-losses-from-weather-disasters- 3/assessment-2
8 http://www.europarl.europa.eu/doceo/document/TA-8-2017-0098_EN.pdf
9 Feasibility study on options to step up EU action against deforestationhttps://ec.europa.eu/environment/forests/pdf/KH0418199ENN2.pdf

10 EU-Mercosur Agreement – Trade and sustainable development chapter

https://trade.ec.europa.eu/doclib/docs/2019/july/tradoc_158166.%20Trade%20and%20Sustainable%2 0Development.pdf

11http://www.fao.org/3/I9535EN/i9535en.pdf

12 https://www.undp.org/content/dam/undp/library/Environment%20and%20Energy/Forests/New%20Yo rk%20Declaration%20on%20Forests_DAA.pdf

Improve the link between Science, innovation, agriculture and food

FICHE

Improve the link between Science, innovation, agriculture and food

Today, the agri-food sectors are more than ever confronted with 3 major demands from our society:

To provide safe and quality food not only to European citizens but as well to world markets,
To keep rural areas lively and viable. This means, first and foremost to maintain and develop a profitable farming activity in all rural areas across the EU.
To optimize the good management of the environment and to fight more effectively against climate change and risks linked to wider and wider spread diseases.

Being able to answer jointly to these three challenges is for sure a challenge itself, but a feasible one, if we accept to make effective use of science and concentrate our efforts on double performance: economic performance and environmental performance.

This is the very basic condition of any success of the EU and the EU agriculture to ensure both growth and jobs and better environment.

For more than a decade, the global productivity growth of the EU farming sector has halved. During this decade, the capital productivity of this sector has become negative. According to the EU Commission, this trend would result in a new decrease by 14% of the EU agri incomes in the next 10 years.

It is time now to reinvest in innovation and research, to reinvest in genetics and develop a concrete science-based approach in that respect.

In this framework, objectivity and transparency will be key.

To reach this objective, we need to change our attitude, to live in our time and consider what science tells us, and not what some say that science could tell.

This is true when it comes to precision and smart farming and how policies (and notably the CAP) can incentivize the move of the EU agriculture to a modern, a more eco-environmentally efficient agriculture.

This is true as well when it comes to genetics.New breeding techniquesare promising as modern and faster extension of usual traditional breeding techniques delivering both in environment, nutrition and economy.

These New Plant-Breeding Techniques, which have emerged as the result of advances in scientific research, enable more precise and faster changes in the plant’s genome than conventional plant breeding techniques, which use chemical and radiation processes to alter the genetic characteristics of plants.

New Plant Breeding Techniques are currently in an uncertain situation regarding their legal classification, as there is urgent to decide on how these practices should be regulated and whether they (or some of them) should or shouldn’t fall within the scope of the EU GMO legislation.

As it is scientifically demonstrated that NBTs such as CRISPR-Cas9 are not GMOs, the new EP should strive to ensure its classification as a non-GMO technique.

FOOD CHAIN, NUTRITION AND WELLBEING

FICHE

FOOD CHAIN, NUTRITION AND WELLBEING

The European food chain is facing a big challenge, which is to find a harmonious and positive relationship between diet and health. This is mainly due to the fact that, the EU agro food model is constantly evolving and improving, and its relationship with health issues is becoming growingly important. This is a key challenge for the future of both agriculture and food industry.

The relationship between science and innovation on one hand and agriculture and food on the other and is perceived mostly in negative terms, or at least in a quite unidirectional way – nutritionissues –, putting aside other elements as important as culture and traditions, sociology, employment and economics, internal market principles, environment, genetics, lifestyles.

Much of the discussions around the issue are quite polarized, sterile and more based in opinions than in science, which doesn’t help any progress on what can be considered the common goal: how to better integrate food and health for the benefit of consumers and society as a whole.

In order to prevent the negative effect of NCD ́s public authorities try to promote different policies and actions with a dubious impact.

If we put it into a European perspective, we have to realize that we participate in a sort of “collage” of measures, mixing European and national initiatives, in different areas, with different aims and ways, that need to be reconsidered. Examples are: national taxes, prohibition of sales, limits to advertising, traffic lights systems (U.K.) as well as French nutriscore model.

Internal market principles – the basis of European integration– have been side-linedwith direct impact on business, free circulation, competition and consumer welfare.

On the prominent role of science: improvements made in terms of food safety in Europe in the last 15 years have to be acknowledged. Both EFSA and the Commission have delivered a sound set of criteria that have created a strong consensus around the best science to inform food safety – and a comprehensive system to evaluate and manage risks.

Science is critical not only because it leads the way for improving human wellbeing, but also because evidence-based science orients political action– or at least should-.

Science is far more than a single discipline. The present “nutrimania” simply puts aside other fields of knowledge like sociology, genetics, physical activity or environment, while multifactorial problems need multidisciplinary approaches. Even when it comes to nutrition focus is more on products (“good” or “bad”) than in diets itself. And as a matter of fact “we eat food, not nutritiens”.

Having said that, the question would be how can Europe improve its decision making in order to take any political action on evidence based science? How can we avoid science to be substituted by opinion when informing political and legal decisions?

– First of all, we need more confidence in our institutions. EFSA must continue to be seen as the reference for excellence in science and food and at the same time, it must be able to better coordinate national Agencies in his effort to align criteria and inform action.

– Another issue: misinterpretation of complex information and a great deal of news and sources makes really difficult to avoid confusion and distress in the public opinion.

– Finally, the way in which debate takes place is not the most constructive one. It seems that each stakeholder sticks to its own position being more interested in counter-arguments and defending specific interests that in finding ways to progress against NCDs through co- operation.
Today, it is urgent to open a debate with all stakeholders and get a broad agreement oncommon objectives: a better and sound healthy lifestyle in Europe, and a firm contribution from the food chain – it is crucial to understand that this is a common issue in which all the members of the chain (consumers, farmers, industry and trade) must work together and united.

At the same, it would be useful to enlarge the role of EFSA to informing the public on where the science stands on key issues. If the objective is to build confidence and a trusty and efficient European legislative framework, one of the challenges is indeed to fight relentlessly baseless « opinions ».

Common Agricultural Policy