Agronomy for Sustainable Development最新文献

Interspecific crop diversity (e.g., intercropping) has been documented to promote sustainability in agroecological systems with benefits for pollination services and insect pollinators. These benefits may also be extended to intraspecific crop diversity (e.g., cultivation of multiple genotypes or cultivars in a production space), but no review to date has examined the benefits of intraspecific crop diversity for pollination and pollinator communities. While mixing cultivars is necessary and a widespread practice for pollination of self-incompatible or male-sterile crops, it is not as widespread for other crop species. However, many other crops have shown reduced yield quantity or quality with self-fertilization due to partial self-sterility, early acting inbreeding depression, and xenia. These crops could thus experience increased production in diverse cultivar mixtures. Cultivar mixtures could also benefit pollinator communities through providing complementary and temporally consistent floral resources, with cascading effects on pollination services. However, successfully implementing cultivar mixtures requires an understanding of how cultivar identity and arrangement affect successful cross-pollination. In this review, we describe the potential benefits of increased intraspecific crop diversity for optimal pollination and pollinator populations across insect-pollinated crops. Additionally, we explore how research advances in cultivar characteristics and insect pollinator behavior and movement, as well as crop pollen flow, can inform cultivar mixtures and spatial arrangements. We find evidence that mixing cultivars, even in self-compatible crops, improves pollination outcomes and yields. Additionally, given insect pollinator behavior and pollen flow, such mixing must occur at relatively small spatial scales. Furthermore, cultivar diversity could ensure successful pollination and resource production for pollinators under extreme weather events. We also discuss costs and benefits of diverse cultivar mixtures from a grower’s perspective and offer suggestions for future research including translating findings within the context of farming systems so that recommendations are practical and achievable.

Soil quality is pivotal for crop productivity and the environmental quality of agricultural ecosystems. Achieving sufficient yearly income and long-term farm continuity are key goals for farmers, making sustainable soil management an economic challenge. Existing bio-economic models often inadequately address soil quality. In this study, we apply the novel FARManalytics model, which integrates chemical, physical, and biological indicators of soil quality indicator, quantitative rules on how these indicators respond to farmers’ production management over time, and an economic calculation framework that accurately calculates the contribution of production management decisions towards farm income. This is the first study applying this model on existing arable farms. FARManalytics optimizes crop rotation design, cover crops, manure and fertilizer application and crop residue management. Nine Dutch arable farms were analyzed with a high variation in farm size, soil type, and cultivated crops. First, we assessed farm differences in soil quality and farm economics. Second, we optimized production management to maximize farm income while meeting soil quality targets using farm-specific scenarios. Third, we explored the impact of recent policy measures to preserve water quality and to increase the contribution of local protein production. The results show that the case farms already perform well regarding soil quality, with 75% of the soil quality indicators above critical levels. The main soil quality bottlenecks are subsoil compaction and soil organic matter input. We show that even in front-runner farms, bio-economic modeling with FARManalytics substantially improves economic performance while increasing soil quality. We found that farm income could be increased by up to €704 ha⁻¹ year⁻¹ while meeting soil quality targets. Additionally, we show that to anticipate on stricter water quality regulation and market shift for protein crops, FARManalytics is able to provide alternative production management strategies that ensure the highest farm income while preserving soil quality for a set of heterogenous farms.

Cover cropping consists in sowing non-cash crops to improve regulating and supporting services without seeking provisioning services. Cover cropping has the potential for spatio-temporal diversification of cropping systems to help address food security while also improving environmental sustainability. However, cover crops are still poorly adopted by farmers worldwide. One of the key reasons behind this poor adoption is the difficulties in ensuring cover crop establishment that is further exacerbated by the current knowledge gaps. On the other hand, no study has yet summarized key published and unpublished information on cover crop emergence and field establishment that may help fill these knowledge gaps. In light of this, for the first time, we comprehensively review the literature to summarize and quantify information related to cover crop emergence and propose strategies for improving their field establishment. The major findings are as follows. (1) Detailed statistics on the share of arable land sown to cover crops are lacking, but the available information suggests that this share is increasing over the years ranging from 4% in the USA to 9% in the EU. (2) Four key factors—regulations and public policy incentives, economic factors, knowledge factors, and environmental factors—influence the adoption or non-adoption of cover crops by farmers. (3) Poor emergence and field establishment, due to unfavorable environmental conditions, is one of the most important obstacles to cover crop adoption across temperate regions worldwide. (4) Five forms of cover crop sowing are practiced by farmers that can be grouped into two major sowing strategies—sowing before and after harvesting cash crops—each of them presenting several strengths and limits. (5) A wide range of sowing equipment is available for farmers but their choice depends on several factors including work output and costs. Finally, we emphasize the role of a decision support system and modeling, for an optimal cover crop sowing and field establishment, which are key for enhanced quantity of biomass production and ecosystem service provisioning.

In-field trees are thought to buffer arable crops from climate extremes through the creation of microclimates that may reduce the impacts of heat, wind, and cold. Much less is known about how trees and their biotic interactions (e.g. with natural enemies of pests and wild understory plants) impact crop yield stability to biotic stresses such as crop pests and disease. Modelling these interactions using conventional approaches is complex and time consuming, and we take a simplified approach, representing the agroecosystem as a Boolean regulatory network and parameterising Boolean functions using expert opinion. This allies our approach with decision analysis, which is increasingly finding applications in agriculture. Despite the naivety of our model, we demonstrate that it outputs complex and realistic agroecosystem dynamics. It predicts that, in English silvoarable, the biotic interactions of in-field trees boost arable crop yield overall, but they do not increase yield stability to biotic stress. Sensitivity analysis shows that arable crop yield is very sensitive to disease and weeds. We suggest that the focus of studies and debate on ecosystem service provision by English agroforestry needs to shift from natural enemies and pests to these ecosystem components. We discuss how our model can be improved through validation and parameterisation using real field data. Finally, we discuss how our approach can be used to rapidly model systems (agricultural or otherwise) than can be represented as dynamic interaction networks.

Beekeeping has faced increasing difficulties during the past decades, among which is the decline in floral resources. Agriculture provides essential floral resources for beekeeping, but some farming practices have also been shown to be responsible for their decline. To provide floral resources for beekeeping, what type of agricultural transformation should be promoted, and how? To answer these questions, we still lack knowledge about the floral resources that are used by beekeeping and about the technical-economic obstacles that farmers face in implementing more favorable farming practices, particularly in agropastoral settings. To help fill these gaps, we develop a novel approach that frames both agropastoral farming and beekeeping as farming systems, by characterizing the beekeeping systems of a given place, the floral resources they use, and the impacts these farming systems have on floral resources. This approach is applied to the agropastoral landscapes of Mount Lozère, southern France, using a methodology based on semi-structured interviews with farmers and beekeepers addressing the agronomical functioning of their farms. We demonstrate that the floral resources used by beekeepers on Mount Lozère are threatened by the current dominant agricultural development paths, which seek to maximize the material productivity of labor. Such paths lead to the intensification of agricultural practices in harvested areas and the extensification of rangelands. These pathways are reinforced by the low remuneration of agropastoral labor and by the current rules of the European Union Common Agricultural Policy. “Frugal” farming, a farming system based on reduced inputs and investments, and labor-intensive practices, namely, a labor-intensive use of pasture, seems an effective way to produce floral resources. Both, agropastoral farmers and beekeepers, would benefit from an increase in the number of agricultural workers in agropastoral landscapes. This calls for public policies that promote a better remuneration of agropastoral labor, either directly or by driving market mechanisms.

Agricultural systems strongly impact ecosystems by driving terrestrial degradation, water depletion, and climate change. The Life Cycle Assessment allows for comprehensive analyses of the environmental impacts of food production. Nonetheless, its application still faces challenges due to cropping systems’ increased complexity and multifunctionality. Past research has emphasized the need for more holistic approaches to consider dynamic crop interactions and diverse functions of cropping systems, beyond just meeting the demand for foods and feeds. In this context, this study applied an alternative combined and multifunctional modelling approach to compare the environmental performances of two durum wheat cropping systems. The latter differed in crop rotation schedules, farming methods, tillage techniques, and genotypes grown (including both modern and old ones). Novel methodological choices were adopted in this study, aiming at best representing the complexity and peculiarities of these systems, by considering crop rotation effects and reflecting the main durum wheat stakeholders’ perspectives. The results showed that the organic low-input landrace-growing system (Case 1) had considerably lower environmental impacts than the conventional high-input one (Case 2), regardless of the functional unit. The environmental hotspots were the increased land occupation and the bare fallow for Case 1 and Case 2, respectively. At the endpoint level, the most affected impact categories for both the systems of analysis were land use, fine particulate matter formation, global warming (human health), and human non-carcinogenic toxicity. Also, the midpoint analysis pointed out important differences in terms of other assessed impact categories, with Case 1 better performing for the majority of them. The identified improvement solutions include the following: the enhancement of the yield performances and the optimization of nitrogen provision from the leguminous crop for Case1, the shift toward a more efficient rotational scheme, the reduction of the use of external inputs, and the avoidance of unnecessary soil tillage operations for Case 2.

Cropping systems depend on external nitrogen (N) to produce food. However, we lack metrics to account for society’s fertilizer dependency, although excessive increases in N application damage human and environmental health. The objective of this study is to propose a novel indicator, N fertilizer dependency, calculated as the ratio between human-controllable external inputs and total N inputs. Nitrogen fertilizer dependency has a solid mathematical base being derived from closing the nitrogen use efficiency (NUE) equation. This study also tests the value of the N fertilizer dependency concept at the cropping system (plant-soil) scale and at different spatial scales, from field to country, as a complementary indicator to promote sustainable production. The field experiments conducted with grain cereals as a main crop showed that when replacing the barley precedent crop with a legume, N fertilizer dependency accounted for soil legacy and was reduced by 15% in fertilized treatments. In a farm population, N fertilizer dependency ranged from 47 to 95% and accounted for the relevance of biological fixation and irrigation water N inputs, adding pertinent information to performance indicators (i.e., NUE). At the country scale, N fertilizer dependency showed different temporal patterns, depending mainly on the relevance of biological atmospheric N fixation. Nitrogen fertilizer dependency of global cropping systems has risen to ≈83% in the last five decades, even though the N exchange among regions has increased. Nitrogen fertilizer dependency has great potential to monitor the achievements of efforts aiming to boost system autonomy, and within similar agricultural systems, it can be used to identify practices that lead to a reduction of fertilizer needs. In summary, N fertilizer dependency is a new indicator to evaluate the agroenvironmental sustainability of cropping systems across the scales and provides a complementary dimension to the traditional indicators such as NUE, N output, and N surplus.

The top-down approach, whereby scientists design “ready-to-use” packages to be adopted as they are by farmers, is being increasingly called into question. In reality, farmers often do not just adopt new systems that interest them, but adapt proposed systems to their own situation. Yet, these adaptations are seldom encouraged by agronomists and are even less so a focus of research. In this study, we designed and tested a new collective and individual learning-based approach to support farmers’ adaptation of innovative cropping systems, and applied this approach to increasing legume cultivation in cropping systems in a region of Burkina Faso where legumes have been neglected in favor of cotton. The approach is based on a sequence of three steps. First, collective exchanges during “farmers’ field days” were organized in each village around prototyping trials comparing different legume-based cropping system options proposed by agronomists. Second, farmers could choose the cropping system option that most interests them for implementation. Third, farmers progressively adapted this cropping system, in dedicated adaptation plots. Various degrees of adjustments and adaptations were observed between the options displayed in the prototyping trials and the adaptations made in the plots over a 2-year period. We classified these adaptations into five types of dynamics of change. We found that (i) farmers adapted the cropping system options differently depending on the flexibility as well as the farmer’s knowledge of the system, and (ii) the adaptations made by farmers were influenced by the discussions (both peer-to-peers and with the agronomists) that took place during field days. We thus show that collective exchanges on prototyping trials could contribute to support farmers embarking on a trajectory of change through step-by-step design.

Accurate production estimates, months before the harvest, are crucial for all parts of the food supply chain, from farmers to governments. While methods have been developed to use satellite data to monitor crop development and production, they typically rely on official crop statistics or ground-based data, limiting their application to the regions where they were calibrated. To address this issue, a new method called VeRsatile Crop Yield Estimator (VeRCYe) has been developed to estimate wheat yield at the pixel and field levels using satellite data and process-based crop models. The method uses the Leaf Area Index (LAI) as the linking variable between remotely sensed data and APSIM crop model simulations. In this process, the sowing dates of each field were detected (RMSE = 2.6 days) using PlanetScope imagery, with PlanetScope and Sentinel-2 data fused into a daily 3 m LAI dataset, enabling VeRCYe to overcome the traditional trade-off between satellite data that has either high temporal or high spatial resolution. The method was evaluated using 27 wheat fields across the Australian wheatbelt, covering a wide range of pedo-climatic conditions and farm management practices across three growing seasons. VeRCYe accurately estimated field-scale yield (R² = 0.88, RMSE = 757 kg/ha) and produced 3 m pixel size yield maps (R² = 0.32, RMSE = 1213 kg/ha). The method can potentially forecast the final yield (R² = 0.78–0.88) about 2 months before the harvest. Finally, the harvest dates of each field were detected from space (RMSE = 2.7 days), indicating when and where the estimated yield would be available to be traded in the market. VeRCYe can estimate yield without ground calibration, be applied to other crop types, and used with any remotely sensed LAI information. This model provides insights into yield variability from pixel to regional scales, enriching our understanding of agricultural productivity.

In the context of ever-growing demand for food and associated concerns regarding the environmental impacts of high-input agricultural systems, there is growing interest in mixed farm enterprises to deliver greater sustainability compared with mono-enterprise production systems. However, assessments of such systems are complex and require high-resolution data to determine the true value and interconnectivity across enterprises. Given the scarcity of information on mixed crop–livestock systems and the difficulties of its analysis, we perform life cycle assessment using temporally high-resolution data (2019–2022) from a long-term experiment in South America to evaluate the ‘cradle-to-farmgate exit’ greenhouse gas emissions intensities of four rotational crop–livestock systems. Systems evaluated were continuous cropping: 2 years of continuous cropping; short rotation: 2-year continuous cropping plus 2-year pasture; long rotation: 2-year continuous cropping followed by 4-year pasture; and forage rotation: continuous pasture. Emissions intensities for beef throughput were reported as kilograms of carbon dioxide equivalents (CO₂-eq) per kilogram of liveweight gain (LWG) using the Intergovernmental Panel for Climate Change’s Sixth Assessment Report (AR6 2021) CO₂ characterisation factors. Point estimate results were found to be 11.3, 11.8, 11.8 and 16.4 kg CO₂-eq/kg/LWG for continuous cropping, short rotation, long rotation and forage rotation, respectively. Emission averages arising from crops, which were separated from animal-based emissions using economic allocation, were 1.23, 0.53 and 0.52 kg CO₂-eq/kg for soybean, wheat and oat, respectively. The inclusion of soil organic carbon stock changes had notable effects on reducing each system’s emissions: by 22.4%, 19.2%, 25.3% and 42.1% under continuous cropping, short rotation, long rotation and forage rotation, respectively, when soil organic carbon was included. Given there are few life cycle assessment studies available on such mixed-enterprise ‘semi-circular’ systems, particularly with novel primary data, this study adds critical knowledge to agri-food-related sustainability literature by addressing environmental issues in complex production systems compared to extant and broad coverage of mono-enterprise systems.