Editorial: Genome wide association studies and genomic selection for crop improvement in the era of Big Data

Alison Bentley Charles Chen Nunzio D'Agostino (2022, [Artículo])

Allele Mining High-Throughput Phenotyping Genomic Estimated Breeding Value CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROP IMPROVEMENT DNA CHROMOSOME MAPPING GENETIC LINKAGE GENOMES GENOTYPING GERMINATION HEAT STRESS QUALITY CONTROL SINGLE NUCLEOTIDE POLYMORPHISM TRITICUM AESTIVUM GENETIC DIVERSITY (AS RESOURCE) HIGH-THROUGHPUT SEQUENCING

Diversifying with grain legumes amplifies carbon in management-sensitive soil organic carbon pools on smallholder farms

Regis Chikowo Sieglinde Snapp (2023, [Artículo])

Crop diversification with grain legumes has been advocated as a means to increase agroecological resilience, diversify livelihoods, boost household nutrition, and enhance soil health and fertility in cereal-based cropping systems in sub-Saharan Africa and around the world. Soil organic carbon (SOC) is a primary indicator of soil health and there is limited data regarding SOC pools and grain legume diversification on smallholder farms where soils are often marginal. In Malawi, a range of legume diversification options are under investigation, including rotations and a doubled-up legume rotation (DLR) system in which two compatible legumes are intercropped and then rotated with a cereal. The impact of the DLR system on SOC has not yet been determined, and there is a lack of evidence regarding SOC status over a gradient of simple to complex grain legume diversified systems. We address this knowledge gap by evaluating these systems in comparison to continuous sole maize (Zea mays L.) at three on-farm trial sites in central Malawi. After six years of trial establishment, we measured SOC in bulk soils and aggregate fractions and in faster cycling SOC pools that respond more rapidly to management practices, including water extractable organic carbon (WEOC), particulate organic matter carbon (POM-C), potentially mineralizable carbon (C), and macroaggregate C. Cropping treatment differences were not seen in bulk SOC or total N, but they were apparent in SOC pools with a shorter turnover time. The DLR system of intercropped pigeonpea (Cajanus cajan (L.) Millsp.) and groundnut (Arachis hypogaea L.) rotated with maize had higher WEOC, POM-C, potentially mineralizable C, macroaggregate and microaggregate C values than continuous maize. Of the single legume rotations, the pigeonpea-maize rotation had more mineralizable C and microaggregate C compared to continuous maize, while the groundnut-maize rotation had similar C values to the maize system. Overall, this study shows the potential for crop rotations diversified with grain legumes to enhance C in management sensitive SOC pools, and it is one of the first reports to show this effect on smallholder farm sites.

Crop Diversification Water Extractable Organic Carbon CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DIVERSIFICATION LEGUMES PARTICULATE ORGANIC MATTER SOIL ORGANIC CARBON

Dead level contour technical design parameters required for sustainable crop production in semiarid areas of Zimbabwe

Menas Wuta Isaiah Nyagumbo (2021, [Artículo])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE DRY SPELLS RAINWATER HARVESTING CROP PRODUCTION TECHNOLOGY

Agricultural value chains in Sudan: an annotated bibliography

Hugo De Groote Paswel Marenya (2023, [Libro])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURAL VALUE CHAINS CASH CROPS FOOD CROPS LIVESTOCK SOCIOECONOMIC ASPECTS GENDER SOCIAL INCLUSION POSTHARVEST LOSSES BIBLIOGRAPHIES

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics

Xu Wang Sandesh Kumar Shrestha Philomin Juliana Suchismita Mondal Francisco Pinto Govindan Velu Leonardo Abdiel Crespo Herrera JULIO HUERTA_ESPINO Ravi Singh Jesse Poland (2023, [Artículo])

New Crop Varieties Plant Breeding Programs Yield Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA LEARNING GRAIN YIELDS WHEAT BREEDING FOOD SECURITY

Editorial: Sorghum and pearl millet as climate resilient crops for food and nutrition security

Palak Chaturvedi Mahalingam Govindaraj Govindan Velu Wolfram Weckwerth (2022, [Artículo])

Climate Smart Crops Foxtail Millet CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BREEDING FINGER MILLET FOOD SECURITY SETARIA ITALICA GENETIC RESOURCES PEARL MILLET SORGHUM

GWAS identifies genetic loci underlying nitrogen responsiveness in the climate resilient C4 model Setaria italica (L.)

Tirthankar Bandyopadhyay Stéphanie M. Swarbreck Vandana Jaiswal Rajeev Gupta Alison Bentley Manoj Prasad (2022, [Artículo])

C4 Model Crop Climate Resilience CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE RESILIENCE FOOD SECURITY GENE EXPRESSION NITROGEN

Modeling the growth, yield and N dynamics of wheat for decoding the tillage and nitrogen nexus in 8-years long-term conservation agriculture based maize-wheat system

C.M. Parihar Dipaka Ranjan Sena Prakash Chand Ghasal Shankar Lal Jat Yashpal Singh Saharawat Mahesh Gathala Upendra Singh Hari Sankar Nayak (2024, [Artículo])

Context: Agricultural field experiments are costly and time-consuming, and their site-specific nature limits their ability to capture spatial and temporal variability. This hinders the transfer of crop management information across different locations, impeding effective agricultural decision-making. Further, accurate estimates of the benefits and risks of alternative crop and nutrient management options are crucial for effective decision-making in agriculture. Objective: The objective of this study was to utilize the Crop Environment Resource Synthesis CERES-Wheat model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based wheat system. The study aimed to calibrate the model using data from a field experiment conducted during the 2019-20-2020-21 growing seasons and evaluation it with independent data from the year 2021–22. Method: Crop simulation models, such as the Crop Environment Resource Synthesis CERES-Wheat (DSSAT v 4.8), may provide valuable insights into crop growth and nitrogen dynamics, enabling decision makers to understand and manage production risk more effectively. Therefore, the present study employed the CERES-Wheat (DSSAT v 4.8) model and calibrated it using field data, including plant phenological phases, leaf area index, aboveground biomass, and grain yield from the 2019-20-2020-21 growing seasons. An independent dataset from the year 2021–22 was used for model evaluation. The model was used to investigate the relationship between growing degree days (GDD), temperature, nitrate and ammonical concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on wheat yields. The experimental site is situated at ICAR-Indian Agricultural Research Institute (IARI), New Delhi, representing Indian Trans-Gangetic Plains Zone (28o 40’N latitude, 77o 11’E longitude and an altitude of 228 m above sea level). The treatments consist of four nitrogen management options, viz., N0 (zero nitrogen), N150 (150 kg N ha−1 through urea), GS (Green seeker based urea application) and USG (urea super granules @150 kg N ha−1) in two contrasting tillage systems, i.e., CA-based zero tillage (ZT) and conventional tillage (CT). Result: The outcomes exhibited favorable agreement between the model’s simulations and the observed data for crop phenology (With less than 2 days variation in 50% onset of flowering), grain and biomass yield (Root mean square error; RMSE 336 kg ha−1 and 649 kg ha−1, respectively), and leaf area index (LAI) (RMSE 0.28 & normalized RMSE; nRMSE 6.69%). The model effectively captured the nitrate-N (NO3−-N) dynamics in the soil profile, exhibiting a remarkable concordance with observed data, as evident from its low RMSE = 12.39 kg ha−1 and nRMSE = 13.69%. Moreover, as it successfully simulated the N balance in the production system, the nitrate leaching and ammonia volatilization pattern as described by the model are highly useful to understand these critical phenomena under both conventional tillage (CT) and CA-based Zero Tillage (ZT) treatments. Conclusion: The study concludes that the DSSAT-CERES-Wheat model has significant potential to assess the impacts of tillage and nitrogen management practices on crop growth, yield, and soil nitrogen dynamics in the western Indo-Gangetic Plains (IGP) region. By providing reliable forecasts within the growing season, this modeling approach can facilitate better planning and more efficient resource management. Future implications: The successful implementation of the DSSAT-CERES-Wheat model in this study highlights its applicability in assessing crop performance and soil dynamics. Future research should focus on expanding the model’s capabilities by reducing its sensitivity to initial soil nitrogen levels to refine its predictions further. Moreover, the model’s integration with decision support systems and real-time data can enhance its usefulness in aiding agricultural decision-making and supporting sustainable crop management practices.

Nitrogen Dynamics Mechanistic Crop Growth Models Crop Simulation CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA NITROGEN CONSERVATION AGRICULTURE WHEAT MAIZE CROP GROWTH RATE SIMULATION MODELS

Research for development approaches in mixed crop-livestock systems of the Ethiopian highlands

Million Gebreyes James Hammond Lulseged Tamene Getachew Agegnehu Rabe Yahaya Anthony Whitbread (2023, [Artículo])

This study presents processes and success stories that emerged from Africa RISING's Research for Development project in the Ethiopian Highlands. The project has tested a combination of participatory tools at multiple levels, with systems thinking and concern for sustainable and diversified livelihoods. Bottom-up approaches guided the selection of technological interventions that could address the priority farming system challenges of the communities, leading to higher uptake levels and increased impact. Joint learning, appropriate technology selection, and the creation of an enabling environment such as the formation of farmer research groups, the establishment of innovation platforms, and capacity development for institutional and technical innovations were key to this study. The study concludes by identifying key lessons that focus more on matching innovations to community needs and geographies, systems orientation/integration of innovations, stepwise approaches to enhance the adoption of innovations, documenting farmers' capacity to modify innovations, building successful partnerships, and facilitating wider scaling of innovations for future implementation of agricultural research for development projects.

Action Research Systems Thinking CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA INNOVATION PARTNERSHIPS SCALING UP INTEGRATED CROP-LIVESTOCK SYSTEMS