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To ensure regular and high yields, current agriculture is based on intensive use of pesticides and fertilizers, which are detrimental to the environment and human health. Moreover, as pest resistance to pesticides increases, and more and more pesticides are taken off the market, national and European policies are becoming powerful drivers to deliver pesticide-free farming systems. Whereas numerous studies have compared organic versus conventional systems, our study assessed, for the first time, the performances of a pesticide-free arable cropping system (No-Pesticide), using synthetic fertilizers, specifically designed to produce high yields and meet environmental goals. This system was compared with an input-based cropping system designed with the same environmental targets (PHEP: productive with high environmental performances) in an 11-year field trial in France (Paris Basin). Banning pesticides did not result in a significant average yield gap (in GJ.ha−1.year−1 or in kg N.ha−1.year−1) calculated over the crop sequence. Yet, some crops' yields significantly decreased, due either to pest damages, or to limited nitrogen nutrition. In the No-Pesticide system, the mycotoxin content of cereal grains was lower than the regulatory threshold, and the average wheat protein content was higher than the required standard for baking. Indirect energy consumption, total greenhouse gas emissions, number of technical operations, nitrogen fertilizer amounts, and treatment frequency indexes were significantly lower compared to the PHEP system. Conversely, results showed significantly higher direct energy consumption, direct greenhouse gas emissions, and number of work hours for weed control. We identify highly effective agricultural strategies to avoid pesticide use (e.g., widely diverse and long crop sequence; introduction of hemp) and pinpoint several technical lock-ins hampering steady production in pesticide-free systems. We argue that more experiments should be undertaken to deliver technical knowledge for managing major or orphan species within pesticide-free systems, and to provide supplementary results, including economic and social performances. [ABSTRACT FROM AUTHOR]

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Key message: Crop simulation helps to analyze environmental impacts on crops and provides year-independent context information. This information is of major importance when deciding which cultivar to choose at sowing time. Plant breeding programs design new crop cultivars which, while developed for distinct populations of environments, are nevertheless grown over large areas during their time in the market. Over its cultivation area, the crop is exposed to highly diverse stress patterns caused by climatic uncertainty and multiple management options, which often leads to decreased expected crop performance. In this study, we aim to assess how finer spatial management of genetic resources could reduce the yield variance explained by genotype × environment interactions in a set of cropping environments and ultimately improve the efficiency and stability of crop production. We used modeling and simulation to predict the crop performance resulting from the interaction between cultivar growth and development, climate and soil conditions, and management practices. We designed a computational experiment that evaluated the performance of a collection of commercial sunflower cultivars in a realistic population of cropping conditions in France, built from extensive agricultural surveys. Distinct farming locations sharing similar simulated abiotic stress patterns were clustered together to specify environment types. We then used optimization methods to search for cultivars × environments combinations leading to increased yield expectations. Results showed that a single cultivar choice adapted to the most frequent environment-type in the population is a robust strategy. However, the relevance of cultivar recommendations to specific locations was gradually increasing with the knowledge of pedo-climatic conditions. We argue that this approach while being operational on current genetic material could act synergistically with plant breeding as more diverse material could enable access to cultivars with distinctive traits, more adapted to specific conditions. [ABSTRACT FROM AUTHOR]

Mixed farming systems are gaining interest both as a risk management strategy and to apply agroecological principles. This study set out to assess the sustainability of mixed systems compared to their specialized counterparts. The Orfee bioeconomic farm model was used to simulate three mixed farms: beef and dairy, beef and sheep, and cash crop and beef, under 1990–2017 prices and policies in France. Several sustainability criteria were computed at two scales (i) aggregated farm level, to assess whether mixed farms with several integrated enterprises performed better than if these enterprises were managed in specialized farms, and (ii) unit of product or labour to assess whether a product was produced more sustainably and a worker obtained higher and more stable income in a mixed farm. We found that mixed farms had less work peaks, lower global warming potentials and nitrogen balances, lower total production costs, higher and more stable net incomes than if their enterprises were managed in two specialized farms. However, profitability and income variability of the mixed farms were not better than the best performing specialized farm. The consumption of concentrate feed decreased in the mixed livestock farms but increased in the beef-crop farm that degraded feed-food competition. [ABSTRACT FROM AUTHOR]

Estimating flood damage, although crucial for assessing flood risk and for designing mitigation policies, continues to face numerous challenges, notably the assessment of indirect damage. It is widely accepted that damage other than direct damage can account for a significant proportion of total damage. Yet due to scarcer data sources and lack of knowledge on links within and between economic activities, indirect impacts have received less attention than direct impacts. Furthermore, attempts to grasp indirect damage through economic models have not gone below regional levels. Even though local communities can be devastated by flood events without this being reflected in regional accounts, few studies have been conducted from a microeconomic perspective at local level. What is more, the standard practices applied at this level of analysis tackle entities but ignore how they may be linked. This paper addresses these two challenges by building a novel agent-based model of a local agricultural production chain (a French cooperative wine-making system), utilized as a virtual laboratory for the ex ante estimation of flood impacts. We show how overlooking existing interactions between economic entities in production chains can result in either overestimation (double counting) or underestimation (wrong estimation of the consequences for the activity) of flood damage. Our results also reveal that considering interactions requires thorough characterization of their spatial configuration. Based on both the application of our method and the results obtained, we propose balanced recommendations for flood damage estimation at local level. [ABSTRACT FROM AUTHOR]

Key Message: Crop simulation helps to analyze environmental impacts on crops and provides year-independent context information. This information is of major importance when deciding which cultivar to choose at sowing time. Plant breeding programs design new crop cultivars which, while developed for distinct populations of environments, are nevertheless grown over large areas during their time in the market. Over its cultivation area, the crop is exposed to highly diverse stress patterns caused by climatic uncertainty and multiple management options, which often leads to decreased expected crop performance. In this study, we aim to assess how finer spatial management of genetic resources could reduce the yield variance explained by genotype × environment interactions in a set of cropping environments and ultimately improve the efficiency and stability of crop production. We used modeling and simulation to predict the crop performance resulting from the interaction between cultivar growth and development, climate and soil conditions, and management practices. We designed a computational experiment that evaluated the performance of a collection of commercial sunflower cultivars in a realistic population of cropping conditions in France, built from extensive agricultural surveys. Distinct farming locations sharing similar simulated abiotic stress patterns were clustered together to specify environment types. We then used optimization methods to search for cultivars × environments combinations leading to increased yield expectations. Results showed that a single cultivar choice adapted to the most frequent environment-type in the population is a robust strategy. However, the relevance of cultivar recommendations to specific locations was gradually increasing with the knowledge of pedo-climatic conditions. We argue that this approach while being operational on current genetic material could act synergistically with plant breeding as more diverse material could enable access to cultivars with distinctive traits, more adapted to specific conditions., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models., (© 2020 John Wiley & Sons Ltd.)

Nitrate pollution from agriculture is an important environmental externality, caused by the excessive use of fertilizers. The internalization of this problem, via a tax on mineral nitrogen, could lead to a second best solution, reducing nitrate emissions. Several authors suggest that a reduction in agricultural support could produce similar results. In this paper, we examine the effects of different levels of a uniformly implemented nitrogen tax in France under two policy scenarios, corresponding to post Agenda 2000 and 2003 Luxembourg reforms of European Union's Common Agricultural Policy, in order to reveal the synergies and conflicts between the tax and the policy scenarios in terms of nitrate emissions abatement. The analysis is performed at different geographical scales, from the national to the regional and is based on a bioeconomic approach that involves the coupling of the economic model AROPAj with the crop model STICS. Results show that the efficiency of the N-tax varies according to the geographical scale of the analysis and the type of farming. Furthermore, we prove that a uniform implementation may lead to perverse effects that should always be taken into account when introducing second-best instruments. [ABSTRACT FROM AUTHOR]

Background: Microalgae are 10 to 20 times more productive than the current agricultural biodiesel producing oleaginous crops. However, they require larger energy supplies, so that their environmental impacts remain uncertain, as illustrated by the contradictory results in the literature. Besides, solar radiation is often too high relative to the photosynthetic capacity of microalgae. This leads to photosaturation, photoinhibition, overheating and eventually induces mortality. Shadowing microalgae with solar panels would, therefore, be a promising solution for both increasing productivity during hotter periods and producing local electricity for the process. The main objective of this study is to measure, via LCA framework, the energy performance and environmental impact of microalgae biodiesel produced in a solar greenhouse, alternating optimal microalgae species and photovoltaic panel (PV) coverage. A mathematical model is simulated to investigate the microalgae productivity in raceways under meteorological conditions in Sophia Antipolis (south of France) at variable coverture percentages (0% to 90%) of CIGS solar panels on greenhouses constructed with low-emissivity (low-E) glass. Results: A trade-off must be met between electricity and biomass production, as a larger photovoltaic coverture would limit microalgae production. From an energetic point of view, the optimal configuration lies between 10 and 20% of PV coverage. Nevertheless, from an environmental point of view, the best option is 50% PV coverage. However, the difference between impact assessments obtained for 20% and 50% PV is negligible, while the NER is 48% higher for 20% PV than for 50% PV coverage. Hence, a 20% coverture of photovoltaic panels is the best scenario from an energetic and environmental point of view. Conclusions: In comparison with the cultivation of microalgae without PV, the use of photovoltaic panels triggers a synergetic effect, sourcing local electricity and reducing climate change impacts. Considering an economic approach, low photovoltaic panel coverage would probably be more attractive. However, even with a 10% area of photovoltaic panels, the environmental footprint would already significantly decrease. It is expected that significant improvements in microalgae productivity or more advanced production processes should rapidly enhance these performances. [ABSTRACT FROM AUTHOR]