Models of bee responses to land use and land cover changes in agricultural landscapes - a review and research agenda.

Predictive modelling tools can be used to support the design of agricultural landscapes to promote pollinator biodiversity and pollination services. Despite the proliferation of such modelling tools in recent decades, there remains a gap in synthesising their main characteristics and representation capacities. Here, we reviewed 42 studies that developed non-correlative models to explore the impact of land use and land cover changes on bee populations, and synthesised information about the modelled systems, modelling approaches, and key model characteristics like spatiotemporal extent and resolution. Various modelling approaches are employed to predict the biodiversity of bees and the pollination services they provide, with a prevalence of models focusing on wild populations compared to managed ones. Of these models, landscape indicators and distance decay models are relatively simple, with few parameters. They allow mapping bee visitation probabilities using basic land cover data and considering bee foraging ranges. Conversely, mechanistic or agent-based models delineate, with varying degrees of complexity, a multitude of processes that characterise, among others, the foraging behaviour and population dynamics of bees. The reviewed models collectively encompass 38 ecological, agronomic, and economic processes, producing various outputs including bee abundance, habitat visitation rate, and crop yield. To advance the development of predictive modelling tools aimed at fostering pollinator biodiversity and pollination services in agricultural landscapes, we highlight future avenues for increasing biophysical realism in models predicting the impact of land use and land cover changes on bees. Additionally, we address the challenges associated with balancing model complexity and practical usability.

From a local descriptive to a generic predictive model of cereal aphid regulation by predators.

The temporal dynamics of insect populations in agroecosystems are influenced by numerous biotic and abiotic interactions, including trophic interactions in complex food webs. Predicting the regulation of herbivorous insect pests by arthropod predators and parasitoids would allow for rendering crop production less dependent on chemical pesticides. Curtsdotter et al. (2019) developed a food-web model simulating the influences of naturally occurring arthropod predators on aphid population dynamics in cereal crop fields. The use of an allometric hypothesis based on the relative body masses of the prey and various predator guilds reduced the number of estimated parameters to just five, albeit field-specific. Here, we extend this model and test its applicability and predictive capacity. We first parameterized the original model with a dataset with the dynamic arthropod community compositions in 54 fields in six regions in France. We then integrated three additional biological functions to the model: parasitism, aphid carrying capacity and suboptimal high temperatures that reduce aphid growth rates. We developed a multi-field calibration approach to estimate a single set of generic allometric parameters for a given group of fields, which would increase model generality needed for predictions. The original and revised models, when using field-specific parameterization, achieved quantitatively good fits to observed aphid population dynamics for 59% and 53% of the fields, respectively, with pseudo-R2 up to 0.99. But the multi-field calibration showed that increased model generality came at the cost of reduced model reliability (goodness-of-fit). Our study highlights the need to further improve our understanding of how body size and other traits affect trophic interactions in food webs. It also points up the need to acquire high-resolution data to use this type of modelling approach. We propose that a hypothesis-driven strategy of model improvement based on the integration of additional biological functions and additional functional traits beyond body size (e.g., predator space search or prey defences) into the food-web matrix can improve model reliability.

Relationships between soil ecosystem services in temperate annual field crops: A systematic review.

Agricultural soils provide multiple ecosystem services (ES) that can replace chemical inputs to support agricultural production. However, most arable cropping systems are managed with little concern for preserving ecological functions, which could reduce their ability to provide these ES. An increasing number of studies assess ES from agroecosystems, but analysis of multiple ES distinguishing relationships that may exist between them and between these ES and their drivers is lacking. Thus, we performed a systematic literature review of soil-based ES relationships, with a focus on temperate annual field crops. Forty relevant studies out of 870 were selected for the analysis. We created an original ontology of soil-based ES, based on the indicators used to assess them, to which we added soil-based negative impacts and biomass production (defined as a good) to combine the ES approach and the impact approach. We summarized each positive (synergy), negative (trade-off) or non-significant relationship in these studies, which were either quantitative or qualitative. We highlighted key relationships that have never been investigated in the corpus selected, such as relationships between C sequestration and physical soil quality regulation, soil erosion regulation or soil biodiversity. Relationships between biomass production and soil-based ES or impacts were investigated the most and were mainly non-significant. This suggests there are agroecological practices for which maximizing bundles of ES does not always decrease agricultural production. Relationships between soil biodiversity and soil-based ES were exclusively synergistic or non-significant. Summarizing effects of drivers of these relationships revealed that the three pillars of conservation agriculture - rotation diversification (with ley or legumes), soil coverage with cover crops and reduced tillage - and organic fertilization seem promising practices to help provide balanced bundles of ES and potentially reduce negative agronomic impacts. We highlighted potential trade-offs that should be consciously considered when adapting management strategies.

Farmer-led agroecology for biodiversity with climate change.

The increasing pace of climate change is an existential threat to farming continuity and biodiversity. Agricultural innovation is running too slowly but could be accelerated by a change in the agroecological narrative. A farmer-led agroecology prioritising farming continuity for biodiversity would speed up innovation and better serve science and society.

Towards Predictions of Interaction Dynamics between Cereal Aphids and Their Natural Enemies: A Review.

(1) Although most past studies are based on static analyses of the pest regulation drivers, evidence shows that a greater focus on the temporal dynamics of these interactions is urgently required to develop more efficient strategies. (2) Focusing on aphids, we systematically reviewed (i) empirical knowledge on the drivers influencing the dynamics of aphid-natural enemy interactions and (ii) models developed to simulate temporal or spatio-temporal aphid dynamics. (3) Reviewed studies mainly focus on the abundance dynamics of aphids and their natural enemies, and on aphid population growth rates. The dynamics of parasitism and predation are rarely measured empirically, although it is often represented in models. Temperature is mostly positively correlated with aphid population growth rates. Plant phenology and landscape effects are poorly represented in models. (4) We propose a research agenda to progress towards models and empirical knowledge usable to design effective CBC strategies. We claim that crossover works between empirical and modeling community will help design new empirical settings based on simulation results and build more accurate and robust models integrating more key drivers of aphid dynamics. Such models, turned into decision support systems, are urgently needed by farmers and advisors in order to design effective integrated pest management.

Inconsistent responses of conservation biocontrol to landscape structure: new insights from a network-based review.

Conservation biological control (CBC) has been an active research topic for the last two decades and is now one of the key ways being explored to develop agroecological production systems. Using broad concepts and indicators, recent reviews and meta-analyses have highlighted major inconsistencies in the responses of CBC to landscape structure, revealing their context-dependent nature. To decipher these relations, we reviewed the scientific literature (50 articles) using (1) an original ontology allowing us to navigate across the different terms and concepts used in this literature and (2) a network-based methodology to describe the scattering, completeness, and generalizability of scientific knowledge on CBC. An interactive version of this network is available online. Our results highlight the strong information scattering caused by the variety of indicators used to describe both landscape structure and CBC. We observe trade-offs between the use of coarse concepts classically used in meta-analysis (e.g., landscape complexity) and the non-convergence of results (ambiguity). The network analysis points out consistently less information ambiguity when considering sub-networks focused on trophic chains than in the full information network, without losing connectance. We suggest that effects of landscape structure may be different between trophic chains because of specific selection pressures associated with cropping systems. Our novel review procedure offers a relatively simple but powerful complementary approach to classical meta-analysis to explore ecological patterns. It highlights that crop trophic chain probably represents the adequate ecological unit to investigate the landscape-CBC relationship. Designing pest suppressive landscapes while favoring farmland biodiversity will imply considering multiple crop trophic chains responding differently to landscape structure. Therefore, we recommend assessing the level of CBC at both crop field and landscape scales to inform decisions on the best individual or collective strategy to adopt.

A spatiotemporal dataset for integrated assessment and modelling of crop-livestock integration with the MAELIA simulation platform.

The general purpose of the primary and secondary data available in this article is to support an integrated assessment of scenarios of crop-livestock integration at the territorial level i.e. of exchanges between arable and livestock farms. The data is a result of a research collaboration between the scientist from INRAE, agricultural advisers from Chamber of Agriculture of Pays de la Loire (CRAPL) and a collective of five arable and two livestock farmers located in the district of Pays de Pouzauges (Vendée department, western France). All participants formed part of the DiverIMPACTS project (https://www.diverimpacts.net/) that aims to achieve the full potential of diversification of cropping systems for improved productivity, delivery of ecosystem services and resource-efficient and sustainable value chains in Europe. The first dataset corresponds to the inputs of MAELIA (http://maelia-platform.inra.fr/), a spatial agent-based simulation platform that was used to support an iterative design and assessment of scenarios to redesign cropping systems. The second dataset corresponds to the outputs of MAELIA simulations and the associated indicators at the farm, group and territory level. The data comprise multiple shape and csv files characterizing the edaphic-climatic heterogeneity of the territory and cropping systems, farmers' crop management rules (IF-THEN rules) and general information about the farms (e.g. crops, agricultural equipment, average crop yields). Data is reported for the baseline situation and three exchange scenarios containing different innovative cropping systems co-designed by scientists, agricultural advisers and the farmers. The data presented here can be found in the Portail Data INRA repository (https://doi.org/10.15454/3ZTCF5) and were used in the research article "Fostering local crop-livestock integration via legume exchanges using an innovative integrated assessment and modelling approach: MAELIA" [1].

Estimating the carbon storage potential and greenhouse gas emissions of French arable cropland using high-resolution modeling.

Many studies have assessed the potential of agricultural practices to sequester carbon (C). A comprehensive evaluation of impacts of agricultural practices requires not only considering C storage but also direct and indirect emissions of greenhouse gases (GHG) and their side effects (e.g., on the water cycle or agricultural production). We used a high-resolution modeling approach with the Simulateur mulTIdisciplinaire pour les Cultures Standard soil-crop model to quantify soil organic C (SOC) storage potential, GHG balance, biomass production and nitrogen- and water-related impacts for all arable land in France for current cropping systems (baseline scenario) and three mitigation scenarios: (i) spatial and temporal expansion of cover crops, (ii) spatial insertion and temporal extension of temporary grasslands (two sub-scenarios) and (iii) improved recycling of organic resources as fertilizer. In the baseline scenario, SOC decreased slightly over 30 years in crop-only rotations but increased significantly in crop/temporary grassland rotations. Results highlighted a strong trade-off between the storage rate per unit area (kg C ha-1  year-1 ) of mitigation scenarios and the areas to which they could be applied. As a result, while the most promising scenario at the field scale was the insertion of temporary grassland (+466 kg C ha-1  year-1 stored to a depth of 0.3 m compared to the baseline, on 0.68 Mha), at the national scale, it was by far the expansion of cover crops (+131 kg C ha-1  year-1 , on 17.62 Mha). Side effects on crop production, water irrigation and nitrogen emissions varied greatly depending on the scenario and production situation. At the national scale, combining the three mitigation scenarios could mitigate GHG emissions of current cropping systems by 54% (-11.2 from the current 20.5 Mt CO2 e year-1 ), but the remaining emissions would still lie far from the objective of C-neutral agriculture.

Using a multivariate regression tree to analyze trade-offs between ecosystem services: Application to the main cropping area in France.

Analysis of trade-offs and synergies between ecosystem services (ES) and their underlying drivers is a main issue in ES research. The analysis is complex and requires innovative analytical approaches. To address this complexity, we used an original approach that combines a multivariate regression tree (MRT), data analysis, and spatial mapping. We applied this approach to the main cropping region in France (mainly the Paris basin of production) using an existing dataset (i.e. soil, climate, crop sequences and management) from the French National Ecosystem Assessment to determine relationships between agricultural production, two services to farmers - nitrogen provision to crops and water provision to crops - and three services to society - blue water provision, water quality regulation, and climate regulation. To support land managers and decision-makers, we also analyzed the extent to which manageable soil properties and agricultural practices (crop rotation and management) are major drivers of trade-offs or synergies. We demonstrated that water quality regulation, nitrogen provision to crops, and climate regulation have synergistic relationships in production situations in the northeastern region of the study area due to the types of crop rotation, frequency of cover crops in the crop rotation, the soil pH, and the soil available water capacity. We also identified that cover crops, while promoting these three ES, can drive a trade-off between two key water-related services: water provision to crops and blue water provision (i.e. between a service to farmers and one to society). By capturing non-linear relationships and threshold effects, our MRT-based approach overcomes the main limitations of classic statistical approaches. The approach is also spatially explicit and simple and intuitive to interpret, especially for non-scientists; our results thus provide researchers and ecosystem managers (e.g. agricultural policy makers) with key information to design ecosystem management strategies that promote a balanced bundle of ES.