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Weeds are a major threat in tropical regions where climate conditions favor their growth and development. This is particularly true in low-input rice-based cropping systems in the Malagasy highlands, where weed management is mainly done by manual removal. Crop rotation is often promoted as an efficient way to control weed infestations, while the role of fertilization is more controversial. In this study, we compared rice monoculture to three rainfed rice-based two-year rotations: rice followed by groundnut, rice followed by sorghum-cowpea mixture, and rice followed by a velvet-bean crotalaria mixture. Each rotation was tested with two levels of fertilization (5 t DM ha−1 organic manure, sole or in combination with mineral fertilizer - 400 kg ha−1 NPK + 200 kg ha−1 urea). We assessed the effect of rotation and fertilization on weed composition, diversity, biomass and rice yield. Additionally, the farmers' perception of weed harmfulness and the relation between their assessment of weed harmfulness and rice production was tested. Our results showed that weed biomass significantly decreased rice yield but only under the low fertilization level. The rotation of rice with the velvet bean-crotalaria mixture was efficient in reducing weed biomass, modified weed community composition and allowed to achieve the highest rice yield. A significant negative relationship was found between weed community harmfulness index and weed species richness. Yet, the lowest rice yield was observed under rice monoculture despite a higher species richness over years and under high fertilization level. The lack of significant correlation between the harmfulness index and the actual rice yield is probably because our index is partly based on farmer's perception, and only on major weeds. More studies on tropical weed harmfulness are needed to support the design of ecologically intensified cropping systems.

Intercropping, i.e., growing several species in the same field for a major part of their growing periods, often improves yield and weed control, but their performance greatly varies across situations. The aim of this study was to evaluate the effects of bi-species legume–cereal intercrops on weed dynamics and their impact on crop production, in the absence of nitrogen or water stress, via simulations with FlorSys. This individual-based 3D model simulates daily crop–weed seed and plant dynamics over the years, from cropping system and pedoclimate, focusing on competition for light. The study tested seven species proportions in two species mixtures (wheat–faba bean and barley–pea) and nine spatial sowing patterns in three species mixtures (triticale–faba bean, wheat–faba bean, wheat–pea), in both cases comparing the intercrops with the corresponding sole crops (controls). Intercrops and controls were inserted into rotations and simulated over 30 years and repeated with 10 climate scenarios from South-Western France, either with or without weeds. The simulations showed that: (1) the intercrops that best controlled weeds were barley–pea and triticale–faba bean, (2) the spatial pattern alternating one cereal row with one legume row as well as the 67 %-cereal–33 %-legume and 100 %-cereal–50 %-legume species proportions were those that maximised yields and minimised losses due to weeds, (3) the weed biomass in intercrop was greater than or equal to that of the sole cereal, and less than that of the sole legume, and (4) legumes benefitted more from intercropping than cereals because cereals are more competitive against weeds. Intercrop yield was best when combining species with contrasting shading responses (etiolated with stockier plants, leafy with stemmier plants) but early and good plant emergence was essential, particularly for weed suppression.

Peanut is a worldwide oilseed crop and the need to assess germplasm in a non-destructive manner is important for seed nutritional breeding. In this study, Near Infrared Spectroscopy (NIRS) was applied to rapidly assess germplasm variability from whole seed of 699 samples, field-collected and assembled in four genetic and environment-based sets: one set of 300 varieties of a core-collection and three sets of 133 genotypes of an interspecific population, evaluated in three environments in a large spatial scale of two countries, Mbalmayo and Bafia in Cameroon and Nioro in Senegal, under rainfed conditions. NIR elemental spectra were gathered on six subsets of seeds of each sample, after three rotation scans, with a spectral resolution of 16 cm-1 over the spectral range of 867 nm to 2530 nm. Spectra were then processed by principal component analysis (PCA) coupled with Partial least squares-discriminant analysis (PLS-DA). As results, a huge variability was found between varieties and genotypes for all NIR wavelength within and between environments. The magnitude of genetic variation was particularly observed at 11 relevant wavelengths such as 1723 nm, usually related to oil content and fatty acid composition. PCA yielded the most chemical attributes in three significant PCs (i.e., eigenvalues >10), which together captured 93% of the total variation, revealing genetic and environment structure of varieties and genotypes into four clusters, corresponding to the four samples sets. The pattern of genetic variability of the interspecific population covers, remarkably half of spectrum of the core-collection, turning out to be the largest. Interestingly, a PLS-DA model was developed and a strong accuracy of 99.6% was achieved for the four sets, aiming to classify each seed sample according to environment origin. The confusion matrix achieved for the two sets of Bafia and Nioro showed 100% of instances classified correctly with 100% at both sensitivity and specificity, confirmi

Banana crown rot is caused by a parasitic complex of fungal origin, with symptoms most often appearing after harvest. The source-sink ratio has an impact on disease development that could be deleterious with a high Black Leaf Streak Disease pressure. The aim of this work was to assess the effects of hand removal as a lever to rebalance this ratio to control crown rot in a production context. The results confirmed a significant effect of hand number and functional leaf area on banana susceptibility to crown rot. A high source-sink ratio resulting from hand removal enabled the fruit to better resist disease with limited impact on yield.

Background and aims: Intercrops offer multiple advantages over sole crops. The aim of our study was to characterize root growth and interactions in spring wheat/faba bean intercrops to better understand belowground interactions that govern resource capture. Materials and methods: A field experiment was conducted with one faba bean cultivar and two spring wheat cultivars sown at three sowing densities, defining three intercropping designs. Destructive root coring was conducted (0–100 cm) in the intercrops and sole crops at two development stages. FTIR spectroscopy was used to discriminate the species' root masses. The plant-plant interaction index was calculated to represent the belowground interactions. Results: A negative impact of intercropping on total root mass was observed in the treatment with high sowing density in both stages. For the fully and partial replacement design treatments, plant-plant facilitation was more pronounced than competition in all layers. Competition dominated root growth in the treatment with high sowing density in both stages. Lower sowing densities encouraged deep root growth of wheat (both cultivars) in intercropping. The early root growth in depth and in density of one spring wheat cultivar impacted negatively faba bean root growth. Intercropping resulted in a grain yield advantage in both fully and only one partial replacement design treatment. Conclusion: In the intercrops, total root mass and plant-plant interactions were affected more by sowing density than by the spring wheat cultivar. Understanding the effect of sowing density on root growth in intercropping can help to support the design of sustainable intercropping systems.

Background: On tropical regions, phosphorus (P) fixation onto aluminum and iron oxides in soil clays restricts P diffusion from the soil to the root surface, limiting crop yields. While increased root surface area favors P uptake under low-P availability, the relationship between the three-dimensional arrangement of the root system and P efficiency remains elusive. Here, we simultaneously assessed allelic effects of loci associated with a variety of root and P efficiency traits, in addition to grain yield under low-P availability, using multi-trait genome-wide association. We also set out to establish the relationship between root architectural traits assessed in hydroponics and in a low-P soil. Our goal was to better understand the influence of root morphology and architecture in sorghum performance under low-P availability. Result: In general, the same alleles of associated SNPs increased root and P efficiency traits including grain yield in a low-P soil. We found that sorghum P efficiency relies on pleiotropic loci affecting root traits, which enhance grain yield under low-P availability. Root systems with enhanced surface area stemming from lateral root proliferation mostly up to 40 cm soil depth are important for sorghum adaptation to low-P soils, indicating that differences in root morphology leading to enhanced P uptake occur exactly in the soil layer where P is found at the highest concentration. Conclusion: Integrated QTLs detected in different mapping populations now provide a comprehensive molecular genetic framework for P efficiency studies in sorghum. This indicated extensive conservation of P efficiency QTL across populations and emphasized the terminal portion of chromosome 3 as an important region for P efficiency in sorghum. Increases in root surface area via enhancement of lateral root development is a relevant trait for sorghum low-P soil adaptation, impacting the overall architecture of the sorghum root system. In turn, particularly concerning th

Pulse production in India has stagnated relative to staple grains and cash crops, raising concerns about rural protein consumption. We experimentally evaluate an effort to increase local pulse production in Bihar. This intervention consisted of 2 years of input subsidies and extension to facilitate learning, followed by the creation of marketing organizations and a year of output price support to raise profitability. Farmers respond to price signals by expanding inputs when subsidized and increasing pulse sales under price supports. However, we see no evidence that the program shifted equilibrium production portfolios as pulses return to pre-intervention levels after the support ends. Results indicate that short-term learning by doing cannot overcome long-run barriers to local pulse production, even when farmers have a viable outlet to sell their surplus output.

Introduction: Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects. Methods: In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits. Results: The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used. Discussion: These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.

High-dimensional climate data collected on a daily, monthly, or seasonal time step are now commonly used to predict crop yields worldwide with standard statistical models or machine learning models. Since the use of all available individual climate variables generally leads to calculation problems, over-fitting, and over-parameterization, it is necessary to aggregate the climate data used as predictors. However, there is no consensus on the best way to perform this task, and little is known about the impacts of the type of aggregation method used and of the temporal resolution of weather data on model performances. Based on historical data from 1981 to 2016 of soybean yield and climate on 3447 sites worldwide, this study compares different temporal resolutions (daily, monthly, or seasonal) and dimension reduction techniques (principal component analysis (PCA), partial least square regression, and their functional counterparts) to aggregate climate data used as inputs of machine learning and linear regression (LR) models predicting yields. Results showed that random forest models outperformed and were less sensitive to climate aggregation methods than LRs when predicting soybean yields. With our models, the use of daily climate data did not improve predictive performance compared to monthly data. Models based on PCA or averages of monthly data showed better predictive performance compared to those relying on more sophisticated dimension reduction techniques. By highlighting the high sensitivity of projected impact of climate on crop yields to the temporal resolution and aggregation of climate input data, this study reveals that model performances can be improved by choosing the most appropriate time resolution and aggregation techniques. Practical recommendations are formulated in this article based on our results.

he slow increase in grain production in sub-Saharan Africa (SSA) is largely the result of cropland expansion rather than an increase in crop yields, which have been stagnantly low (< 1.5 t ha-1). Sustainable intensification of crop production is therefore needed to feed a growing population whilst minimizing negative impacts on the environment, biodiversity, and climate. Full accounting of the net global warming potential (GWP) of management practices can provide a holistic approach for identifying cropping systems that promote sustainable agriculture intensification to ensure food security whilst mitigating climate change.

Understanding trait–trait coordination is essential for successful plant breeding and crop modeling. Notably, plant size drives variation in morphological, physiological, and performance-related traits, as described by allometric laws in ecology. Yet, as allometric relationships have been limitedly studied in crops, how they influence and possibly limit crop performance remains unknown. Here, we review how an allometry perspective on crops gains insights into the phenotypic evolution during crop domestication, the breeding of varieties adapted to novel conditions, and the prediction of crop yields. As allometry is an active field of research, modeling and manipulating crop allometric relationships can help to develop more resilient and productive agricultural systems to face future challenges.

Adapting organic farming to climate change is a major issue. Cotton yields in Mali are declining due to deteriorating climatic conditions, soil fertility, and poor management. This study aimed to improve organic cotton yield in Mali in the future climate with the optimal choice of compost type, date, and dose of application. Experimental data collected in 2021 from the Sotuba research station in Mali was used for calibration and evaluation of the crop model DSSAT CSM-CROPGRO-Cotton model using phenology, leaf area index, and seed cotton yield. Climate data from the RCP4.5 and RCP8.5 scenarios of the GFDL-ESM2M model were used for future weather datasets for 2020-2039, 2040-2059, and 2060-2079. The model was able to simulate anthesis and maturity with excellent results, with nRMSE < 4%, and seed cotton yields moderately well, an nRMSE of 26% during calibration and 20.3% in evaluation. The scenario RCP8.5 from 2060 to 2079 gave the best seed cotton yields. Seed cotton yields with RCP4.5 and RCP8.5 were all better with the mid-May application period of small ruminant silo compost at 7.5 t/ha. In such conditions, more than 75% of the cases would produce more than 2000 kg/ha of seed cotton.

Enhancing food security in the Sahel through nature-based solutions is urgent given population growth, resource scarcity and climate change. Traditional agroforestry parklands are a farmer- and nature-based widespread form of ecological intensification which randomly integrates trees into crop fields. While most studies estimating crop yields in agroforestry have been conducted in controlled experimental settings, few have addressed the inherent variability in such highly heterogeneous systems. Thus, the purpose of this study is to benefit from a UAV-based proxy-sensing and machine learning approach to address the variability of pearl millet grain yield, according to the distance to randomly distributed trees in a traditional agroforestry system dominated by Faidherbia albida (i.e. groundnut basin of Senegal). 21 vegetation indices (VIs), 32 normalized difference texture indices (NDTIs) derived from multispectral drone images, and normalization variables for radiative conditions were used with yield data collected in 15 plots (around 1 ha each) and subplots (15 m 2 each) displayed at 3 distances from the tree over five cropping seasons (2018 - 2022). In this context, the optimal phenological stage was determined for predicting pearl millet grain yield, which proved to be the pre-heading period. This period was used as the basis for our machine learning model training dataset in the subplots. Two models, Random Forest (RF) and Gradient Boosting Machine (GBM) were compared by combining VIs, NDTIs and normalization variables. GBM was the best-performing model, explaining 78% of observed pearl millet yield variability over five years in the subplots, with a RMSE of 16 g. m −2 . This study revealed that NDTIs calculated from red and green bands were more influential for yield estimation than those based on near-infrared. These results were subsequently used to predict yield in all plots, resulting in a mean relative error of 17.5% between yields estimated by the farmers

Due to increasing climate uncertainties, optimizing plant traits is essential for sustainable agriculture. This article presents an approach that combines advanced modelling techniques to identify optimal plant traits under various agro-environmental conditions. By integrating a crop model, a climate generator, and our PEQI algorithm (Profile Expected Quantile Improvement), our method aims to create ideotype maps tailored to specific regions. We use the SAMARA model (Simulator of crop trait Assembly, MAnagement Response, and Adaptation), calibrated with trials carried in Sahel on a set of local varieties, to simulate crop growth in diverse environments. The PEQI algorithm adjusts varietal parameters to maximize expected yield, defining precise selection objectives known as ideotypes, which are particularly important in regions with irregular rainfall patterns like the Sahel. With the PEQI algorithm based on a kriging metamodel, we ensure effective adaptation to spatially variable environments. By leveraging a climate generator to simulate meteorological variability, our integrated approach optimizes crop yields in regions such as Senegal, southern Mali, Burkina Faso, and Guinea-Bissau. The outcome is an ideotype map for sorghum, providing breeders with a robust decision-support tool to enhance crop performance amidst climate uncertainty.

Cotton, a high-value crop of primary financial importance for Greece, is directly affected by a wide range of environmental parameters, and water scarcity threatens the sustainability of irrigated cotton production in many regions around the world. Reducing inputs with an appropriate cultivar may not decrease cotton production and fiber quality while improving sustainability. To investigate cotton varietal responses to water stress and lack of nutrients, in the climate of north Greece, a three-year experiment was conducted in Thessaloniki, Greece. Four cotton cultivars (three of Greek origin and one from Africa) were cultivated under four irrigation (normal and reduced by 75%, 50%, and 25%) and two fertilization levels (normal and 50%). The effect of these treatments on productivity and quality were estimated, with the final objective being the reduction of inputs and more sustainable cotton production. Cotton producers are dealing with a cost price squeeze and the present study demonstrates that reducing the fertilization inputs even by half and the reduction of irrigation by 25% has no significant effect neither on yield nor on the main technological characteristics. This highlights the fact that a more sustainable use of inputs, contrary to common management, will have almost the same yield and even increase the farmer's income.

Drought impacts monitoring has been called the missing piece in drought assessment. The potential to improve drought management is high but uncertain due to rare analyses of impacts datasets, predominantly because there are few impacts monitoring programmes to generate the datasets. Drought impacts monitoring is conducted on the ground in much of Brazil by local observers at monthly and municipality scale to support the Brazilian Drought Monitor. In Ceará state, within drought-prone semiarid northeast Brazil, over 3600 drought impacts reports were completed by agricultural extension officers from 2019 to 2022. We investigated, through manual coding and observer interviews, the reported drought impacts and impact drivers. Analysis provided a catalogue of the experienced impacts and showed that impacts still occur, and are often normalised, during non-drought periods, sometimes as lingering effects of previous droughts. The impact drivers were predominantly non-extreme hydrometeorological conditions or a result of socio-technical vulnerabilities such as insufficient water infrastructure. The normalisation of “impacts” included, in particular: a generally accepted high level of crop losses and consistently low reservoir levels around which the agricultural and domestic systems are adapted. Conventional drought indices often did not align with experienced impact severity, highlighting the limitations of relying solely on these indices for emergency response. Continual impacts monitoring could be extremely valuable anywhere in the world for identifying vulnerabilities and informing proactive measures to reduce drought and other hazard risk, in addition to guiding targeted mitigation efforts.

Agroforestry is a promising way to sustain land use efficiency in semi-arid areas. In this study, we introduce quinoa as a drought- and salinity-tolerant crop in olive-based agroforestry. We investigated how the microclimate created by olive trees affects agronomic and biochemical traits in quinoa and evaluated the performance of this new olive-based agroforestry system in terms of land equivalent ratio (LER). Field experiments were carried out under two pedoclimatic conditions (S1) and (S2) using a randomized complete block design with two cropping systems (sole crop (SCS) and agroforestry (AFS) systems), four quinoa cultivars (Puno, Titicaca, ICBA-Q5, and ICBA-Q4) and one olive orchard as a control (OR) in each block. Our results show that AFS had lower grain yield (−45%), dry biomass (−49%), and crop water productivity (−44%), but higher plant height (12%), grain protein (4%), saponin (26%), total polyphenol (12%), and DPPH (9%) contents compared to SCS. The highest grain yield was recorded for Titicaca and ICBA-Q5 (1.6 t ha−1). The LER ranged from 1.57 to 2.07, indicating that the overall productivity was 57% to 107% higher in the agroforestry system compared with the monoculture. We suggest that quinoa–olive tree intercropping could be a promising agroecological practice under semi-arid conditions.

The expansion of the oil palm industry in Indonesia has improved livelihoods in rural communities, but comes at the cost of biodiversity and ecosystem degradation. Here, we investigated ways to balance ecological and economic outcomes of oil palm cultivation. We compared a wide range of production systems, including smallholder plantations, industrialized company estates, estates with improved agronomic management, and estates with native tree enrichment. Across all management types, we assessed multiple indicators of biodiversity, ecosystem functions, management, and landscape structure to identify factors that facilitate economic–ecological win–wins, using palm yields as measure of economic performance. Although, we found that yields in industrialized estates were, on average, twice as high as those in smallholder plantations, ecological indicators displayed substantial variability across systems, regardless of yield variations, highlighting potential for economic–ecological win–wins. Reducing management intensity (e.g., mechanical weeding instead of herbicide application) did not lower yields but improved ecological outcomes at moderate costs, making it a potential measure for balancing economic and ecological demands. Additionally, maintaining forest cover in the landscape generally enhanced local biodiversity and ecosystem functioning within plantations. Enriching plantations with native trees is also a promising strategy to increase ecological value without reducing productivity. Overall, we recommend closing yield gaps in smallholder cultivation through careful intensification, whereas conventional plantations could reduce management intensity without sacrificing yield. Our study highlights various pathways to reconcile the economics and ecology of palm oil production and identifies management practices for a more sustainable future of oil palm cultivation.

Controlling pest populations in cotton fields is crucial to reduce yield losses due to plant damage. However, knowing the economic, environmental and agronomical issues related to the systematic use of chemical insecticides, it is urgently needed to better assist spraying decisions based on the development of an Early Warning System. In Benin, one of the major cotton producers in West Africa, a comprehensive annual monitoring of the incidence of key pests has been implemented for more than ten years. Here, we propose to use these long-term and multilocal observational data to (1) better understand the relationships between environmental variables, including climate, landscape context, but also crop management, and intra- or inter-annual pest population dynamics within the cotton-growing area, and to (2) better predict the risk of yield loss due to key pests as to better target interventions. As a perspective, we suggest developing standardized data collection and management throughout the West-African cotton-growing area for areawide pest management.

Genetic improvement is crucial for ensuring food security globally. Indeed, plant breeding has contributed significantly to increasing the productivity of major crops, including rice, over the last century. Evaluating the efficiency of breeding strategies necessitates a quantification of this progress. One approach involves assessing the genetic gain achieved through breeding programs based on quantitative traits. This study aims to provide a theoretical understanding of genetic gain, summarize the major results of genetic gain studies in rice breeding, and suggest ways of improving breeding program strategies and future studies on genetic gain. To achieve this, we present the concept of genetic gain and the essential aspects of its estimation. We also provide an extensive literature review of genetic gain studies in rice (Oryza sativa L.) breeding programs to understand the advances made to date. We reviewed 29 studies conducted between 1999 and 2023, covering different regions, traits, periods, and estimation methods. The genetic gain for grain yield, in particular, showed significant variation, ranging from 1.5 to 167.6 kg/ha/year, with a mean value of 36.3 kg/ha/year. This translated into a rate of genetic gain for grain yield ranging from 0.1% to over 3.0%. The impact of multi-trait selection on grain yield was clarified by studies that reported genetic gains for other traits, such as plant height, days to flowering, and grain quality. These findings reveal that while breeding programs have achieved significant gains, further improvements are necessary to meet the growing demand for rice. We also highlight the limitations of these studies, which hinder accurate estimations of genetic gain. In conclusion, we offer suggestions for improving the estimation of genetic gain based on quantitative genetic principles and computer simulations to optimize rice breeding strategies.

In agricultural value chains, in particular cocoa, environmental, and social standards are becoming increasingly important in meeting sustainability challenges. The main aim of this article is to examine the role of certification primarily on the yields and incomes of cocoa farmers in the Central Cameroon region. From June to July 2017, a survey of certified and non-certified cocoa producers was conducted in the districts of Ngoumou and Mbangassina. At the end of the survey, a total sample of 114 certified and non-certified producers was obtained. Using the cost-benefit analysis, in this preliminary phase of the certification process, the results show that there is no significant difference in costs between certified and non-certified producers for each of the districts. There is also no price difference between certified and non-certified cocoa and the payment of the certification premium is not systematically distributed. However, the profit analysis shows on average significantly higher yields and profits for certified producers. Nevertheless, this increase in profits would mainly be due to the implementation of good agricultural practices rather than the certification itself. Additionally, the exploration of on farm income shows that cocoa-based households remain on average below the poverty line. One of the long-term challenges for the cocoa sector in Cameroon is to combine the government's objective of increasing production with that of improving quality. To this end, the advantages of the predominantly agroforestry systems present in Cameroon should be exploited to produce a critical mass of quality and sustainable cocoa that would allow a significant increase in producer prices.

Late planting due to erratic onset of the rainy season is becoming more frequent in the Sahelo-Sudanian climate where cotton is grown, causing seed cotton yield (SCY) loss and higher risk of drought at the end of the crop cycle. Therefore, cultivars should be adapted to late (from July 10) planting date (PD) in Senegal. The aim of this study was to analyse the interaction between genotypes and PD on SCY in Senegal under rainfed conditions. Field experiments were conducted in 2018 and 2019 using a split-plot design (two PDs, eight cultivars) at three experimental stations. Robust analysis of SCY was used to moderate the effect of potential outliers. The average SCY was 1404 kg/ha under early planting, and 714 kg/ha under late planting. The best SCY was obtained under early planting conditions, in environments with good rainfall. The loss due to late planting was significantly affected by cultivar choice. None of the cultivars performed best under both early and late PD. Under early PD, cultivar CS 50 gave the best SCY, while under late PD it was cultivar IRMA Q302. The best performing cultivar on average depended on the proportion of early plantings. A model was developed to identify the best overall cultivar based on the expected proportion of early planting, as a decision support tool for the cotton development company, if only one cultivar is released. The benefit of releasing a second cultivar for late-planted fields is considered.

In-field data were collected in Costa Rica between 2018–2021 on newly planted grafted and non-grafted coffee plants grown under artificial shade nets and across an elevation gradient (1050, 1250 and 1450 m.a.s.l). The coffee plants consisted of Coffea arabica F1 hybrid plants ('H3 i.e. Caturra cv. X Ethiopian 531'), which were derived from a somatic embryogenesis clonal propagation process, an American C. arabica pure line ('Villa Sarchi') and C. canephora 'Nemaya' (the latter two both being produced by seed). Data from eight different coffee types (including these three genotypes) and different grafting combinations (including reverse and auto-grafting) were collected. Data concerned plant traits such as grafting compatibility (plant collar diameters above and below graft union), agronomic characteristics (aerial and root traits), leaf ecophysiology (leaf gas-exchange and chlorophyll fluorescence), yield and quality attributes (bean size, peaberry percentage, WB100 and SCA note). Climate data were also included for comparison on the farm plots along the elevation gradient. Linear mixed models were used to test for effects of elevation (test sites), coffee types (grafted or non-grafted combinations) and interaction between coffee types and elevations. Least square mean estimates were calculated for significant fixed effects and Tukey tests applied for pairwise tests. A tangential hyperbola curve was used to analyse leaf gas-exchange data. These datasets and R scripts can be re-used as a guide for future analyses concerning coffee agronomy or eco-physiological interactions for other plant species. Other potential re-uses could be meta-analyses aimed at comparing coffee yield, quality, or other agronomic traits across different environmental conditions (such as under shade of an agroforestry system or across different elevation sites).

The growing demand for sustainable agriculture is raising interest in intercropping for its multiple potential benefits to avoid or limit the use of chemical inputs or increase the production per surface unit. Predicting the existence and magnitude of those benefits remains a challenge given the numerous interactions between interspecific plant-plant relationships, their environment, and the agricultural practices. Soil-crop models are critical in understanding these interactions in dynamics during the whole growing season, but few models are capable of accurately simulating intercropping systems. In this study, we propose a set of simple and generic formalisms (i.e. the structure and mathematical representation necessary for designing a model) for simulating key interactions in bi-specific intercropping systems that can be readily included into existing dynamic crop models. This requires simulating important processes such as development, light interception, plant growth, N and water balance, and yield formation in response to management practices, soil conditions, and climate. These formalisms were integrated into the STICS soil-crop model and evaluated using observed data of intercropping systems of cereal and legumes mixtures, including Faba bean-Wheat, Pea-Barley, Soybean-Sunflower, and Wheat-Pea mixtures. We demonstrate that the proposed formalisms provide a comprehensive simulation of soil-plant interactions in various types of bispecific intercrops. The model was found consistent and generic under a range of spring and winter intercrops (nRMSE = 25% for maximum leaf area index, 23% for shoot biomass at harvest, and 18% for grain yield). This is the first time a complete set of formalisms has been developed and published for simulating bi-specific intercropping systems and integrated into a soil-crop model. With its emphasis on being generic, sufficiently accurate, simple, and easy to parameterize, STICS is well-suited to help researchers designing in silico

Increasing yield resiliency under water deficits remains a high priority for crop improvement. In considering the yield benefit of a plant trait modification, two facts are often overlooked: (1) the total amount of water available to a crop through a growing season ultimately constrains growth and yield cannot exceed what is possible with the limited amount of available water, and (2) soil water content always changes over time, so plant response needs to be considered within a temporally dynamic context of day-to-day variation in soil water status. Many previous evaluations of drought traits have implicitly considered water deficit from a “static” perspective, but while the static approach of stable water deficit treatments is experimentally congruous, the results are not realistic representations of real-world drought conditions, where soil water levels are always changing. No trait always results in a positive response under all drought scenarios. In this paper, we suggest two key traits for improving grain legume yield under water deficit conditions: (1) partial stomata closure at elevated atmospheric vapor pressure deficit that results in soil water conservation, and (2) lessening of the high sensitivity of nitrogen fixation activity to soil drying.

Societal Impact Statement : Yam is a major tropical root crop and a staple food for millions of people in West Africa. The model used in this study shows that promoting the use of improved seed tubers would help increase yields and profitability for farmers. This could lead to improved food security, increased income and higher standards of living. Additionally, the model serves as a useful decision-support tool for farmers and technicians to choose, depending on the species, the optimum seed-tuber weight and planting date. This study provides agronomic arguments to justify investments in the improvement of yam planting materials in West Africa. Summary: Yam (Dioscorea spp.) is a major tropical root crop, grown mainly in West Africa using traditional extensive techniques. Farmers typically reuse seed tubers by setting aside up to 30% of their production for the next season, leading to high planting material variability that affects yields. Several initiatives aim to promote the use of improved seed tubers. However, to help their adoption, it is necessary to quantify the agronomic and economic advantages. To address this, a model for individual plant growth and development was developed based on six experiments in Benin from 2007 to 2009. This model simulates the combined effect of emergence (through photoperiod and temperature) and seed-tuber weight on yam plant growth and development. Its predictions were highly correlated with observed plant tuber yield (R2 > 0.83). Results highlight the crucial role of key processes such as seed-tuber physiological age and photoperiod sensitivity. The study shows that for the traditional planting dates, the use of improved planting material could lead to a yield increase of 22%–27% and a gain in profitability of 30% and 40% for Dioscorea alata and Dioscorea rotundata, respectively. The model proved to be a useful decision-support tool for choosing an optimum seed-tuber weight, depending on the species and the planting date. This

Description du sujet. Le débat sur la relation entre la taille des exploitations agricoles et les résultats de production est ancien, mais il n'est pas encore tranché. Au Sénégal, ce débat est alimenté par le fait que les gouvernements récents tendent à favoriser des exploitations irriguées de grande taille, alors que les premiers périmètres distribués étaient de très petites parcelles. Objectifs. Cet article contribue à identifier, dans une zone où il y a des grandes et des petites exploitations, le type d'exploitation qui produit le plus et qui a les meilleurs rendements. Méthode. Une enquête a été réalisée auprès de 326 ménages de riziculteurs dans les communes de Diama et de Ross Béthio, sises dans le delta du fleuve Sénégal. Résultats. Les petites exploitations obtiennent de meilleurs rendements mais leurs revenus globaux sont trop faibles. Les plus grandes exploitations rizicoles réalisent des rendements moins importants mais permettent à leurs exploitants d'obtenir des revenus suffisants pour vivre et faire fonctionner l'exploitation. Selon nos enquêtes, les couts de production sont les principaux facteurs explicatifs des rendements parmi les riziculteurs enquêtés. Les petits producteurs ont des couts de production relativement élevés pour maximiser leur profit là où les grands exploitants limitent les couts de production pour obtenir des rendements qui restent acceptables pour un revenu d'exploitation décent. Conclusions. Pour mieux lutter contre la pauvreté et atteindre l'autosuffisance en riz, les politiques agricoles devraient permettre aux petits cultivateurs de capter de nouveaux investissements et encourager les grands producteurs à investir plus dans la riziculture afin d'augmenter leurs rendements et leurs profits.

La diversification des cultures est un des leviers qui pourraient contribuer à stabiliser, voire améliorer, les rendements dans les régions soudano-sahéliennes contraintes par des conditions climatiques semi-arides et des sols très faiblement fertiles. Elle peut permettre aussi d'atténuer les risques liés aux marchés. Cette diversification peut être mise en oeuvre par la pratique des associations et des rotations ainsi que par l'enrichissement de celles-ci avec de nouvelles espèces. Pendant trois années d'expérimentation, nous avons comparé sept espèces, dont quatre légumineuses y compris le haricot mungo, cultivées seules ou en association avec le sorgho. Le haricot mungo est apparu comme une espèce très bien adaptée aux conditions semi-arides du Burkina Faso. Parmi les sept espèces testées, c'est celle qui a produit le plus de grains, entre 0,8 et 1,8 t ha−1 en culture pure et entre 0,35 et 0.9 t ha−1 en association avec le sorgho. Nos résultats confirment aussi le gain de rendement de la culture de sorgho associée avec les légumineuses par rapport à la culture pure. Vingt-cinq producteurs du centre nord du Burkina Faso ont testé le haricot mungo dans leurs parcelles et apprécié sa productivité et sa rusticité. Ils ont obtenu un rendement moyen de 0,7 t ha−1. Toutefois, l'absence de marché, par comparaison au niébé, reste un frein à sa diffusion.

Cereal-legume intercropping has been traditionally practiced across West Africa by farmers and provides resilience of agriculture to climate variability. Intensification of these extensive intercropping systems in order to meet future food demand is critical. This study aims at evaluating the agronomic performance of the intensification of millet-cowpea intercropping with low cowpea density, and its variation with climate variability, using an on-station experiment in Bambey, Senegal. Two trials (irrigated vs rainfed) were set up to compare millet sole- and inter-cropping with a grain and a fodder variety of cowpea, in 2018 and 2019. Two levels of fertilization were tested: 0 kg(N) ha−1 and 69 kg(N) ha−1. The two cropping years were contrasting and water stress around flowering and/or during grain filling (indicated by the Fraction of Transpirable Soil Water) was higher in 2019 than in 2018 in the rainfed experiment. In both experiment and for all treatments, land equivalent ratio (LER) in the intercropping was 1.6 and 1.4 for grain and biomass respectively. Millet aboveground biomass was significantly higher in intercropping than in sole cropping in the irrigated experiment but not in the rainfed experiment. In the rainfed experiment, the interaction between cropping system and year was significant, so that millet aboveground biomass was greater in intercropping than in sole cropping in 2018 (year of lower water stress) but not in 2019 (year of higher water stress). The effect of fertilization on millet aboveground biomass did not significantly interact with cropping system (sole vs intercrop). For grain yield, fertilization interacted significantly with the cropping system in the irrigated trial: the benefits of intercropping on millet grain yield were greater with 69 kg(N) ha−1 than with 0 kg(N) ha−1. This significant interaction could not be observed in the rainfed trial, potentially due to water stress. These results show that the level of water stress (related

Plant architecture plays a major role in flowering and therefore in crop yield. Attempts to visualize and analyse strawberry plant architecture have been few to date. Here, we developed open-source software combining two- and three-dimensional representations of plant development over time along with statistical methods to explore the variability in spatio-temporal development of plant architecture in cultivated strawberry. We applied this software to six seasonal strawberry varieties whose plants were exhaustively described monthly at the node scale. Results showed that the architectural pattern of the strawberry plant is characterized by a decrease of the module complexity between the zeroth-order module (primary crown) and higher-order modules (lateral branch crowns and extension crowns). Furthermore, for each variety, we could identify traits with a central role in determining yield, such as date of appearance and number of branches. By modeling the spatial organization of axillary meristem fate on the zeroth-order module using a hidden hybrid Markov/semi-Markov mathematical model, we further identified three zones with different probabilities of production of branch crowns, dormant buds, or stolons. This open-source software will be of value to the scientific community and breeders in studying the influence of environmental and genetic cues on strawberry architecture and yield.

Agroecological studies on sugarcane intercropping commonly generate complex datasets. A generic database (AEGIS – Agro-Ecological Global Information System) has been developed to facilitate the use of these datasets. The data described in this paper includes data from 8 experiments carried out on Reunion Island from 2012 to 2021 under three soil and climatic conditions to assess the capacity of cover crops in the sugarcane inter-row to control weed growth. Each experiment consisted of the comparison of three treatments in the inter-row: i) sugarcane with chemical weeding, ii) sugarcane with a cover crop sowed in the inter-row, and iii) sugarcane with spontaneous weed flora in the inter-row. The datasets contain data for sugarcane and cover crop observations (e.g., yield), weed flora, including 104 weed species (e.g., ground cover), crop management (including manual and chemical weedings), soil analyses, and daily weather. This dataset provides an adequate experimental dataset to calibrate or validate crop model simulations under intercropping.

Background: Rice is one of the most salt sensitive crops at seedling, early vegetative and reproductive stages. Varieties with salinity tolerance at seedling stage promote an efficient growth at early stages in salt affected soils, leading to healthy vegetative growth that protects crop yield. Saltol major QTL confers capacity to young rice plants growing under salt condition by maintaining a low Na+/K+ molar ratio in the shoots. Results: Marker-assisted backcross (MABC) procedure was adopted to transfer Saltol locus conferring salt tolerance at seedling stage from donor indica IR64-Saltol to two temperate japonica varieties, Vialone Nano and Onice. Forward and background selections were accomplished using polymorphic KASP markers and a final evaluation of genetic background recovery of the selected lines was conducted using 15,580 SNP markers obtained from Genotyping by Sequencing. Three MABC generations followed by two selfing, allowed the identification of introgression lines achieving a recovery of the recurrent parent (RP) genome up to 100% (based on KASP markers) or 98.97% (based on GBS). Lines with highest RP genome recovery (RPGR) were evaluated for agronomical-phenological traits in field under non-salinized conditions. VN1, VN4, O1 lines were selected considering the agronomic evaluations and the RPGR% results as the most interesting for commercial exploitation. A physiological characterization was conducted by evaluating salt tolerance under hydroponic conditions. The selected lines showed lower standard evaluation system (SES) scores: 62% of VN4, and 57% of O1 plants reaching SES 3 or SES 5 respectively, while only 40% of Vialone Nano and 25% of Onice plants recorded scores from 3 to 5, respectively. VN1, VN4 and O1 showed a reduced electrolyte leakage values, and limited negative effects on relative water content and shoot/root fresh weight ratio. Conclusion: The Saltol locus was successfully transferred to two elite varieties by MABC in a time frame of

Heat and drought stresses have become more frequent and intense in the Mediterranean, strongly influencing arable crop phenology, growth, and grain yield. Agroforestry systems can effectively buffer the adverse climate conditions and stabilize or even increase crop yield under climate change. However, the positive effects of agroforestry remain uncertain due to the possible intense competition between trees and crops, especially for legume crops that have been less studied than cereals in such context. This study aimed to assess the response of the phenology, growth, grain yield, and yield-related traits of chickpea (Cicer arietinum), faba bean (Vicia faba), and lentil (Lens culinaris) to olive-based agroforestry (AFS) as compared to sole crops system (SCS) in the South of the Mediterranean. We conducted a field experiment during two growing seasons marked by an intense drought, either at the beginning (year 1) or at the end (year 2) of the crop cycle. Crop growth and yield were lower in year 1 than in year 2, reflecting the adverse growing conditions caused by the early drought. They were also lower in AFS than in SCS for both years, indicating that trees had competitive effects on crops. In year 1, the yield loss of grains in AFS was 66 % for lentil, 47 % for chickpea, and 43 % for faba bean compared to SCS, confirming the greater shade sensitivity of lentil. In year 2, the reduction was significantly smaller and was about 46 %, 34 %, 38 % for lentil, chickpea and, faba bean, respectively. The number of pods and grains were the most affected yield components by agroforestry and drought timing across the three legumes crops. Similar responses were found when comparing crops at different distances to trees within the AFS field. Crops generally had lower biomass and yield, explained by fewer pods and grains, on the northern side of trees compared to the southern side of trees or the middle of tree inter-rows, causing significant spatial heterogeneity in crops. Howeve

Orange rust caused by Puccinia kuehnii is a major emerging disease in many sugarcane-producing countries. Breeding for resistant varieties is the main strategy for controlling orange rust. The rapid spread of this disease in recently contaminated sugarcane industries offers the opportunity to use on-going breeding trials to investigate the effect of orange rust on yield traits and gauge levels of resistance required to minimize losses. Orange rust was first observed in 2018 in Reunion. This study reports the effects of the disease on cane yield (CY), recoverable sugar (RS), fiber content (FIB) and economic index (EI) in five environments of Reunion's sugarcane breeding program located in diverse agro-climatic zones. Disease resistance assessed under natural infection had high broad-sense heritability (0.76–0.91) in multi-environment analyses. Mean infection levels differed between locations congruently with location differences for two influential climatic parameters (humidity and temperature). Maximum potential yield losses (YLmax) associated with orange rust were estimated using regression analyses of yield traits versus disease susceptibility. YLmax for CY and EI varied between environments and reached up to 26.0% and 24.2% respectively, in one of the most humid environments. RS was either unaffected or only slightly increased by the disease. In contrast, FIB was always reduced by the disease (YLmax≤6.5%). Multi-environment analyses of yield traits of varieties common to all five environments gave insights into the impact of orange rust on the yielding ability of these varieties across all environments. All these data provide food for thoughts to efficient breeding strategies for varietal resistance to orange rust.

Intercropping of trees with crops on the same piece of land at a given time has been hypothesized to: enhance crop yield, increase land-use and improve land equivalent ratio (LER). To address this hypothesis, we evaluated two legumes faba bean, lentil and three cereals durum wheat, soft wheat and barley grown in olive (Olea europea) agroforestry during two growing seasons (Y) with contrasting weather (Y1: 2015-2016 and Y2: 2016-2017) under a Mediterranean climate of north western Morocco. We assessed the effect of annual crops on olive growth and yield; the effect of trees on annual crop growth, yield components, and final yields; finally, we calculated the land equivalent ratio (LER) of olive agroforestry to assess the productivity of the associations. Legumes had no effect on olive growth and yield, while cereals negatively affected shoot elongation and olive yield compared to olive in sole crop. Olive limited crop growth and yield of all associated crops and yield reduction was around 33 % for legumes and 47 % for cereals in agroforestry than sole crop. The magnitude of reduction was higher in Y1 than Y2. Similar responses were found when comparing crops at different distances from trees. Annual crops generally had lower biomass and yield, near the trees compared to the middle of tree inter-rows, causing significant spatial heterogeneity in crops. The LER reached 1.36 with lentil and 1.33 with faba bean, the lowest LER was recorded with durum wheat in both years with 1.01 in Y1 and 1.02 in Y2, and the highest LER with cereals was registered with soft wheat and reached 1.19 in Y1.

Energy cover crops for biogas production through anaerobic digestion (AD) are inserted between two primary crops. They replace either bare soil or nonharvested cover crops, and their management is usually intensified to produce more biomass. They allow the production of renewable energy as well as digestate, used as an organic fertilizer, without directly competing with food production. Because of the increased biomass production and export and of the return of a digested biomass to the soil, the impact of energy cover crops on soil organic carbon (SOC) is questioned. The objective of this paper was to study the difference in SOC stocks induced by the introduction of energy cover crops for AD coupled with the application of the resulting amount of digestate. We used the AD model Sys-Metha combined with the soil C model AMG to simulate SOC stocks for 13 case studies in France, with scenarios comparing different intercrop management practices, with or without cover crops, harvested or not. Our results indicated that the higher biomass production of energy cover crops (from 6.7 to 11.1 t DM ha−1) in comparison with nonharvested cover crops (2 t DM ha−1) or bare soil led to higher humified C input (belowground input and digestate), despite the high C fraction exported in AD. This resulted in an increase in SOC stocks in comparison with nonharvested cover crops or bare soil (from 0.01 to 0.12 t C ha−1 year−1 over 30 years). The uncertainties in the model parameters did not modify these results. However, in the case of equal biomass production between energy cover crops and nonharvested cover crops, SOC stocks would be lower with energy cover crops.

La productivité des exploitations laitières au Burkina Faso connait une baisse chaque année en saison sèche par suite de la rareté des ressources pastorales. L'une des options retenues par les éleveurs pour y palier est la culture fourragère. Cette étude vise à déterminer les performances technico-économiques de la culture pure du Mucuna pruriens (L.) DC., var deeringiana, une légumineuse fourragère de grand intérêt. L'approche méthodologique a été celle de l'Expérimentation Chez et Par le Paysan de CHIA (2004). Elle a consisté au suivi de 26 parcelles de 0,25 ha chacune chez des éleveurs testeurs volontaires (ETV). Les données collectées ont été soumises à une série d'analyses multivariées ayant abouti à la formation de classes de producteurs suivant leurs pratiques culturales et les performances de productions obtenues. Il en ressort que le meilleur rendement en biomasse fourragère est obtenu par la classe 3 (3 554,5 ± 1 381,11 kg MS/ha). Il est déterminé par l'apport en fumure organique (100% des producteurs de la classe 3), une plus forte densité de semis (36.000 ±7.000 poquets/ha) avec peu de temps consacré au sarclage (10 ± 6 jours de travail par ha). Un usage raisonné des herbicides n'améliore pas les rendements fourrager mais contribue à augmenter les performances économiques de la culture du Mucuna en limitant le recours à la main d'oeuvre pour les opérations de sarclage. Les résultats suggèrent de mettre l'accent sur l'apport de fumure organique, une bonne densité de semis et l'usage raisonné des herbicides pour une production rentable du Mucuna.

The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs.

Micronutrients are essential to plants, and enhancing their availability is one of the agronomic challenges to improving crop quality and yield. This study, under controlled greenhouse conditions, compares tomato plants' responses to two different micronutrient EDTA-chelated formulations, one of them including a newly developed fulvate–lignosulfonate coating. Growth, yield, and several physiological parameters, including photosynthetic gas exchange, water-use efficiency, leaf nutrient content, leaf greenness and the effective quantum yield of photosystem II, were measured to compare their efficiency. The results showed that the new coated formulation significantly improved growth and most of the determined physiological parameters. At the end of the experiment, higher foliar levels of Fe (2.4-fold) and Mn (2.9-fold) were measured, revealing increased availability of lignofulfonate-complexed micronutrients compared to the traditional fertiliser. Moreover, the photosynthesis rate and stomatal conductance were 9- and 20-fold higher, respectively, than when using the standard fertiliser. In conclusion, the new coated fulvate–lignosulfonated fertiliser provided a more suitable source of micronutrients for tomato plant fertilisation, allowing for higher yields, which correlated with a generally improved physiological response.

Accurate dry matter determination (DM) in Hass avocados is vital for optimal harvesting and ensuring fruit quality. Predictive models based on NIRS need to capture fruit DM gradient. This work aimed to determine the DM content in Hass avocado whole by NIRS scanning different fruit zones. Spectra were recorded for each zone of the fruit: peduncle (P), equator (E), and base (B). The calibration and validation included fruit from different orchards in two harvest cycles. The results show a DM gradient within the fruit: 24.47% (E), 24.68% (B), and 24.79% (P). The DM gradient was observed within the spectra using the RMSi (root mean square) criterion and PCA. The results show that at least one spectrum per fruit zone was needed to represent the variability within the fruit. The performances of the calibration using the whole set of data were R2: 0.74 and standard error of cross-validation (SECV) = 1.18%. In the validation stage using independent validation sets, the models showed similar performance (R2: 0.75, SECV 1.15%) with low values of the standard error of prediction (SEP): 1.62%. These results demonstrate the potential of near-infrared spectroscopy for high-throughput sorting of avocados based on their commercial quality.