Agronomy for Sustainable Development最新文献

Cynodon dactylon (L.) Pers. is one of the worst agricultural weeds and invasive species in the world, being widely established in many countries. Despite its impact on agriculture and the growing awareness of authorities and consumers about the consequences of synthetic herbicides, alternative control methods for this weed have been poorly reviewed. A systematic review of the literature published over the last 50 years was used to assess the most studied control methods of C. dactylon (excluding synthetic herbicides) and to summarize the trends and knowledge gaps. The major findings are as follows: (1) the number of publications that studied alternative methods to synthetic chemical control in C. dactylon management has been increasing exponentially since 1972; (2) most of the studies were made under controlled conditions (57%) and lack observations under real production conditions; (3) most of the field experiments were carried out in Asia (42%), under temperate subtropical and arid climates; (4) the publication of articles studying allelopathy stands out significantly (50% of the papers found), with two species from the Poaceae family, rice (Oryza sativa L.) and sorghum (Sorghum bicolor (L.) Moench), showing very high allelopathic inhibitory effects (often above 80%), especially under open field conditions; and (5) preventive soil tillage is the most studied treatment among indirect weed control treatments, and although there is a high risk of propagation, the results indicate that tillage can significantly contribute to control C. dactylon, when compared to no-tillage treatments. Further research is needed to optimize treatments and methods so that they can be applied by farmers under real production conditions.

While improvement of soybean productivity under a changing climate will be integral to ensuring sustainable food security, the relative importance of genetic progress attributed to historical yield gains remains uncertain. Here, we compiled 16,934 cultivar-site-year observations from experiments during the period of 2006–2020 to dissect effects of genetic progress and climate variability on China’s soybean yield gains over time. Over the past 15 years, mean yields in the Northeast China (NEC), Huang-Huai-Hai Plain (HHH), and Southern Multi-cropping Region (SMR) were 2830, 2852, and 2554 kg ha⁻¹, respectively. Our findings show that genetic progress contributed significantly to yield gains, although underpinning mechanisms varied regionally. Increased pod number per plant (PNPP) drove yield gains in the NEC, while both PNPP and 100-grain weight (100-GW) contributed to yield gains in the HHH. In all regions, incremental gains in the reproductive growing periods increased PNPP, 100-GW, and yields. While heat stress in the reproductive period reduced average yields in all regions, superior yielding cultivars (top 25%) in the HHH and SMR were less sensitive to heat stress during the reproductive phases, indicating that the higher yielding cultivars benefited from genetic improvement in heat stress tolerance. Our results highlight the importance of genetic improvements in enabling sustainable food security under global warming and increasingly frequent heat stress.

Phenological development is critical for crop adaptation. Phenology models are typically driven by temperature and photoperiod, but chickpea phenology is also modulated by soil water, which is not captured in these models. This study is aimed at evaluating the hypotheses that accounting for soil water improves (i) the prediction of flowering, pod-set, and flowering-to-pod-set interval in chickpea and (ii) the computation of yield-reducing frost and heat events after flowering. To test these hypotheses, we compared three variants of the Agricultural Production System Simulator (APSIM): (i) APSIMc, which models development with no temperature threshold for pod-set; (ii) APSIMx, which sets a threshold of 15 °C for pod-set; and (iii) APSIMw, derived from APSIMc with an algorithm to moderate the developmental rate as a function of soil water, in addition to temperature and photoperiod common to all three models. Comparison of modelled and actual flowering and pod-set of a common cheque cultivar PBA Boundary^A in 54 diverse environments showed that accuracy and precision were superior for APSIMw. Because of improved prediction of flowering and pod-set timing, APSIMw improved the computation of the frequency of post-flowering frosts compared to APSIMc and APSIMx. The number of heat events was similar for all three models. We conclude that accounting for water effects on plant development can allow better matching between phenology and environment.

New models of collective agriculture have been developing in France over the past 10 years that could encourage the emergence of more diversified and sustainable systems. However, as such production systems are relatively more complex to manage, the involvement of more people may be required. This raises renewed questions concerning the collective organization of work. Our study’s main hypothesis is that the collective organization of work could encourage crop-livestock integration and underlying agroecological processes. To test this hypothesis, we implemented a participatory design approach in a case study in Camargue (France). We first used ecological network analysis to characterize flows of material between production units and assess associated biotechnical performances, namely, efficiency, resilience, productivity, and dependence. In a second step, we drew from the QuaeWork method, which we adapted to the study of collective farms, to characterize the organizational dimension. These two methods allowed us to generate quantitative indicators related to the performance of the system (expressed in kgN.ha⁻¹.yr⁻¹) and to calculate and estimate the time spent on various types of work (h.yr⁻¹). Using a participatory design approach, we then developed and assessed three scenarios with varying levels of integration between activities. The results indicate that the gradual substitution of external resources by internal resources leads to a broader range of flows within the system, generating performances that vary depending on the scenario. The design of the scenarios revealed the repercussions of the organization of work within production units. The two most integrated scenarios are more efficient and resilient than the scenario without integration between units, but they are less productive. Our research contributes novel insights into the impact of agroecological practices on the organization of work on collective farms. Our findings enable a deeper understanding of the complex link between the collective organization of production and the articulation of activities.

China is the largest soybean-consuming country in the world, but its self-sufficiency rate (SSR) of 16% is very low and it therefore has to heavily rely on imports. To solve the soybean dilemma in China, it is necessary to examine the maximum amount of soybean that could be grown on the land currently used, how much land could reasonably be used to expand soybean acreage, and whether China could sustainably increase soybean self-sufficiency to reduce the risks of import reliance. To answer these questions, our paper presents a high-resolution spatial analysis of potential soybean production in China using primary data of weather and crop production practices that govern this potential. We employed a “bottom-up” scaling protocol to estimate gaps between potential yield with optimal management and current yields in three major soybean-planting regions, namely, Northeast China, Central China, and South China. We found that current soybean yield gap (Yg) in China is 49% and 45% of potential yield under irrigated and rainfed cropping systems, respectively. By closing the yield gap, Northeast China could provide additional soybean production equivalent to 32% of the current national total. Our results show that SSR could only be increased to 21–23% in 2030 by Yg closure alone but could be increased to a maximum of 52% by combining Yg closure and a reasonable area expansion. Even so, at least 61.08 million tons of soybean accounting for 38% of global soybean trade would still need to be imported to meet future domestic demand. We discuss strategies for soybean production increase based on Yg closure in the most valuable areas and cropland expansion in a sustainable manner in order to increase SSR as well as lessen the import pressure on the global market.

Achieving food security remains a pressing challenge for small-scale farmers, especially in sub-Saharan Africa and Latin America. Ongoing climate change, invasive noxious weeds, and crop pests further exacerbate the situation. Optimizing traditional cropping systems for sustainable yields and climate-resilient production is imperative in order to address this challenge. The pre-Columbian milpa system of intercropping maize with companion crops such as beans (Phaseolus vulgaris) and squash (Cucurbita spp.) is one effective system that has been shown to produce outstanding yields per unit area compared to monoculture systems. The Push-Pull Technology developed in East Africa, based on the use of repellent and trap companion plants intercropped with maize (and to a lesser extent sorghum), is seen to be similarly effective in minimizing the impact of major pests on yields, including striga weed (Striga spp.), maize stemborers, and the fall armyworm (Spodoptera frugiperda). Although both systems have the potential to compensate for each other’s limitations, there has been no cross-system learning between the Mesoamerican milpa and the East African Push-Pull Technology. Here, we review both systems and present the advantages likely to be obtained by combining these technologies in small-scale farming. The proposed milpa push-pull system could adapt to different gradients of altitude, rainfall, and soil nutrient levels, in addition to controlling pests, and therefore has the potential to become a fundamental cropping technique in Latin America and sub-Saharan Africa.

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.

This virtual issue comprises papers that address diversification for providing sustainable solutions at different scales from cropping and grassland to food systems. The authors investigated processes in case studies at the landscape scale where synergies and trade-offs between social and environmental objectives become the most tangible. Contributions from all continents highlighted regional specificities related to diversification and include research from natural and social sciences, with inter- and transdisciplinary approaches including synthesis of knowledge (reviews), empirical studies with experiments as well as assessments with interviews in case studies: Model-based design of crop diversification, the role of digitalization for achieving sustainability in the European context, ecological engineering for rice pest suppression in China, the role of cereal species mixtures in Ethiopian smallholder farmers, diversified planting in arid irrigation areas in northwestern China, integration of legumes in European and Canadian cropping systems, screening of native forage legumes for northern Swedish grassland systems, cropping system diversification of smallholder farmers in south-central Bangladesh, identification of how farmers imagine diversified landscapes in southern Idaho in the US, farm diversification affecting impacts from COVID-19 across Europe, the role of diversified farming in Mato Grosso Brazil, diversification and soil management measures in Germany, value chain formation for the scaling of crop diversification, and the design process with farmers and scientists for the transition toward legume-supported farming in Europe. A key finding from these examples is that agricultural intensification has led to the simplification of cropping systems and landscapes in terms of species diversity and ecosystem function. To instead move towards sustainable transformation, all system levels (i.e. from the plot, farm, landscape, governance and overall food systems) need to interact and reinforce each other for diversification to deliver the desired outcomes.

Duru et al. (Agron Sustain Dev 35:1259-1281, 2015) highlighted a missing tool for studying and improving the performance of cropping systems in the transition to highly diversified agriculture. In response, this paper proposes a concept for designing, modeling, monitoring, and auditing desired ecosystem services, in intercropping and agroforestry systems. We have labelled this concept ESSU (Ecosystem Services functional Spatial Unit). It delimits the smallest spatial unit encompassing all the interacting species and other functional components (e.g., crops, trees, livestock, spontaneous vegetation, semi-natural habitats such as hedges, ditches, forest patches, and animals) that together provide a specified set of ecosystem services. The novel ESSU concept allows representation of an entire diversified agroecosystem by the repetition of the spatial unit that provides the same sets of targeted ecosystem services as the agroecosystem it represents. It can then be used for various activities, such as the (i) design of more efficient agroecological systems according to the targeted ecosystem services; (ii) rapid audit of farming practices for biodiversity/resilience across large tracts of farmland as part of achieving Sustainable Development Goal 2 targets of sustainable food systems; and (iii) modeling such diversified agroecosystems using a motif adapted to represent the targeted ecosystem services and the species spacing design. We demonstrate that the ESSU concept is highly flexible and applicable to a wide range of diversified agroecosystems, like arable intercropping, crop-tree intercropping, tree-tree agroforestry, and agro-pastoralism. We also show its relevance and suitability for representing temporal changes over 1 year, across several years, and over decades, indicating its generalizability and flexibility. We argue that ESSU could open new theoretical and practical research avenues for the study of diversified agroecosystems. Considered with all the knowledge available on practices, biodiversity, and ecosystem services, ESSU might provide a learning-support tool to fill the knowledge gap about relationships among practices, biodiversity, and associated ecosystem services.