Metrics for optimum allocation of resources on the composition and characterization of crop collections: The CIMMYT wheat collection as a proof of concept

M. Humberto Reyes-Valdés Juan Burgueño Carolina Sansaloni Thomas Payne Rosa Angela Pacheco Gil (2022, [Artículo])

Crop Genebanks Optimization Relative Balance CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROPS GENE BANKS WHEAT

Economics of crop residue management

Vijesh Krishna Maxwell Mkondiwa (2023, [Artículo])

More than five billion metric tons of agricultural residues are produced annually worldwide. Despite having multiple uses and significant potential to augment crop and livestock production, a large share of crop residues is burned, especially in Asian countries. This unsustainable practice causes tremendous air pollution and health hazards while restricting soil nutrient recycling. In this review, we examine the economic rationale for unsustainable residue management. The sustainability of residue utilization is determined by several economic factors, such as local demand for and quantity of residue production, development and dissemination of technologies to absorb excess residue, and market and policy instruments to internalize the social costs of residue burning. The intervention strategy to ensure sustainable residue management depends on public awareness of the private and societal costs of open residue burning.

Crop Biomass Residue Burning Environmental Effects CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROPS BIOMASS RESIDUES ENVIRONMENTAL IMPACT CLIMATE CHANGE SMALLHOLDERS TECHNOLOGY ADOPTION

Expanding the WOFOST crop model to explore options for sustainable nitrogen management: A study for winter wheat in the Netherlands

João Vasco Silva Pytrik Reidsma (2024, [Artículo])

Nitrogen (N) management is essential to ensure crop growth and to balance production, economic, and environmental objectives from farm to regional levels. This study aimed to extend the WOFOST crop model with N limited production and use the model to explore options for sustainable N management for winter wheat in the Netherlands. The extensions consisted of the simulation of crop and soil N processes, stress responses to N deficiencies, and the maximum gross CO2 assimilation rate being computed from the leaf N concentration. A new soil N module, abbreviated as SNOMIN (Soil Nitrogen for Organic and Mineral Nitrogen module) was developed. The model was calibrated and evaluated against field data. The model reproduced the measured grain dry matter in all treatments in both the calibration and evaluation data sets with a RMSE of 1.2 Mg ha−1 and the measured aboveground N uptake with a RMSE of 39 kg N ha−1. Subsequently, the model was applied in a scenario analysis exploring different pathways for sustainable N use on farmers' wheat fields in the Netherlands. Farmers' reported yield and N fertilization management practices were obtained for 141 fields in Flevoland between 2015 and 2017, representing the baseline. Actual N input and N output (amount of N in grains at harvest) were estimated for each field from these data. Water and N-limited yields and N outputs were simulated for these fields to estimate the maximum attainable yield and N output under the reported N management. The investigated scenarios included (1) closing efficiency yield gaps, (2) adjusting N input to the minimum level possible without incurring yield losses, and (3) achieving 90% of the simulated water-limited yield. Scenarios 2 and 3 were devised to allow for soil N mining (2a and 3a) and to not allow for soil N mining (2b and 3b). The results of the scenario analysis show that the largest N surplus reductions without soil N mining, relative to the baseline, can be obtained in scenario 1, with an average of 75%. Accepting negative N surpluses (while maintaining yield) would allow maximum N input reductions of 84 kg N ha−1 (39%) on average (scenario 2a). However, the adjustment in N input for these pathways, and the resulting N surplus, varied strongly across fields, with some fields requiring greater N input than used by farmers.

Crop Growth Models WOFOST CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROPS NITROGEN-USE EFFICIENCY WINTER WHEAT SOIL WATER

Innovative approaches to integrating gender into conventional maize breeding: lessons from the Seed Production Technology for Africa project

Rachel Voss Jill Cairns Michael Olsen Esnath Tatenda Hamadziripi (2023, [Artículo])

The integration of gender concerns in crop breeding programs aims to improve the suitability and appeal of new varieties to both women and men, in response to concerns about unequal adoption of improved seed. However, few conventional breeding programs have sought to center social inclusion concerns. This community case study documents efforts to integrate gender into the maize-focused Seed Production Technology for Africa (SPTA) project using innovation history analysis drawing on project documents and the authors’ experiences. These efforts included deliberate exploration of potential gendered impacts of project technologies and innovations in the project’s approach to variety evaluation, culminating in the use of decentralized on-farm trials using the tricot approach. Through this case study, we illustrate the power of active and respectful collaborations between breeders and social scientists, spurred by donor mandates to address gender and social inclusion. Gender integration in this case was further facilitated by open-minded project leaders and allocation of funding for gender research. SPTA proved to be fertile ground for experimentation and interdisciplinary collaboration around gender and maize breeding, and has provided proof of concept for larger breeding projects seeking to integrate gender considerations.

Crop Breeding On-Farm Trials Tricot CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENDER CROPS BREEDING ON-FARM RESEARCH SOCIAL INCLUSION CITIZEN SCIENCE MAIZE

Application of ammonium to a N limited arable soil enriches a succession of bacteria typically found in the rhizosphere

Yendi Navarro-Noya Marco Luna_Guido Nele Verhulst Bram Govaerts Luc Dendooven (2022, [Artículo])

Crop residue management and tillage are known to affect the soil bacterial community, but when and which bacterial groups are enriched by application of ammonium in soil under different agricultural practices from a semi-arid ecosystem is still poorly understood. Soil was sampled from a long-term agronomic experiment with conventional tilled beds and crop residue retention (CT treatment), permanent beds with crop residue burned (PBB treatment) or retained (PBC) left unfertilized or fertilized with 300 kg urea-N ha-1 and cultivated with wheat (Triticum durum L.)/maize (Zea mays L.) rotation. Soil samples, fertilized or unfertilized, were amended or not (control) with a solution of (NH4)2SO4 (300 kg N ha-1) and were incubated aerobically at 25 ± 2 °C for 56 days, while CO2 emission, mineral N and the bacterial community were monitored. Application of NH4+ significantly increased the C mineralization independent of tillage-residue management or N fertilizer. Oxidation of NH4+ and NO2- was faster in the fertilized soil than in the unfertilized soil. The relative abundance of Nitrosovibrio, the sole ammonium oxidizer detected, was higher in the fertilized than in the unfertilized soil; and similarly, that of Nitrospira, the sole nitrite oxidizer. Application of NH4+ enriched Pseudomonas, Flavisolibacter, Enterobacter and Pseudoxanthomonas in the first week and Rheinheimera, Acinetobacter and Achromobacter between day 7 and 28. The application of ammonium to a soil cultivated with wheat and maize enriched a sequence of bacterial genera characterized as rhizospheric and/or endophytic independent of the application of urea, retention or burning of the crop residue, or tillage.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AMMONIUM CROP RESIDUES WHEAT MAIZE TILLAGE SOIL

Genomic selection: A tool for accelerating the efficiency of molecular breeding for development of climate-resilient crops

UTTAM KUMAR Rajeev Ranjan Kumar Philomin Juliana Sundeep Kumar (2022, [Artículo])

Genomic Selection Single-Trait Genomic Selection Multi-Trait Genomic Selection Genomic Estimated Breeding Value Climate-Resilient Crops CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MARKER-ASSISTED SELECTION CLIMATE CHANGE STRESS CLIMATE RESILIENCE CROPS ABIOTIC STRESS BIOTIC STRESS

High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

Kindie Tesfaye Vakhtang Shelia Pierre C. Sibiry Traore Dawit Solomon Gerrit Hoogenboom (2023, [Artículo])

Seasonal climate variability determines crop productivity in Ethiopia, where rainfed smallholder farming systems dominate in the agriculture production. Under such conditions, a functional and granular spatial yield forecasting system could provide risk management options for farmers and agricultural and policy experts, leading to greater economic and social benefits under highly variable environmental conditions. Yet, there are currently only a few forecasting systems to support early decision making for smallholder agriculture in developing countries such as Ethiopia. To address this challenge, a study was conducted to evaluate a seasonal crop yield forecast methodology implemented in the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT). CRAFT is a software platform that can run pre-installed crop models and use the Climate Predictability Tool (CPT) to produce probabilistic crop yield forecasts with various lead times. Here we present data inputs, model calibration, evaluation, and yield forecast results, as well as limitations and assumptions made during forecasting maize yield. Simulations were conducted on a 0.083° or ∼ 10 km resolution grid using spatially variable soil, weather, maize hybrids, and crop management data as inputs for the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). CRAFT combines gridded crop simulations and a multivariate statistical model to integrate the seasonal climate forecast for the crop yield forecasting. A statistical model was trained using 29 years (1991–2019) data on the Nino-3.4 Sea surface temperature anomalies (SSTA) as gridded predictors field and simulated maize yields as the predictand. After model calibration the regional aggregated hindcast simulation from 2015 to 2019 performed well (RMSE = 164 kg/ha). The yield forecasts in both the absolute and relative to the normal yield values were conducted for the 2020 season using different predictor fields and lead times from a grid cell to the national level. Yield forecast uncertainties were presented in terms of cumulative probability distributions. With reliable data and rigorous calibration, the study successfully demonstrated CRAFT's ability and applicability in forecasting maize yield for smallholder farming systems. Future studies should re-evaluate and address the importance of the size of agricultural areas while comparing aggregated simulated yields with yield data collected from a fraction of the target area.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROP MODELLING DECISION SUPPORT SYSTEMS FORECASTING MAIZE

Alternative cropping and feeding options to enhance sustainability of mixed crop-livestock farms in Bangladesh

Timothy Joseph Krupnik Jeroen Groot (2024, [Artículo])

We investigated alternative cropping and feeding options for large (>10 cows), medium (5–10 cows) and small (≤4 cows) mixed crop – livestock farm types, to enhance economic and environmental performance in Jhenaidha and Meherpur districts – locations with increasing dairy production – in south western Bangladesh. Following focus group discussions with farmers on constraints and opportunities, we collected baseline data from one representative farm from each farm size class per district (six in total) to parameterize the whole-farm model FarmDESIGN. The six modelled farms were subjected to Pareto-based multi-objective (differential evolution algorithm) optimization to generate alternative dairy farm and fodder configurations. The objectives were to maximize farm profit, soil organic matter balance, and feed self-reliance, in addition to minimizing feed costs and soil nitrogen losses as indicators of sustainability. The cropped areas of the six baseline farms ranged from 0.6 to 4.0 ha and milk production per cow was between 1,640 and 3,560 kg year−1. Feed self-reliance was low (17%–57%) and soil N losses were high (74–342 kg ha−1 year−1). Subsequent trade-off analysis showed that increasing profit and soil organic matter balance was associated with higher risks of N losses. However, we found opportunities to improve economic and environmental performance simultaneously. Feed self-reliance could be increased by intensifying cropping and substituting fallow periods with appropriate fodder crops. For the farm type with the largest opportunity space and room to manoeuvre, we identified four strategies. Three strategies could be economically and environmentally benign, showing different opportunities for farm development with locally available resources.

Ruminant Feed Pareto-Based Optimization Farm Bioeconomic Model CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUMINANT FEEDING BIOECONOMIC MODELS MIXED CROPPING FARMS LIVESTOCK

Editorial: Functional genomic approaches in molecular breeding for crop improvement

Philomin Juliana (2023, [Artículo])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DNA MARKER-ASSISTED SELECTION QUANTITATIVE TRAIT LOCI CROP IMPROVEMENT

A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm.

Ali Mirzazadeh Afshin Azizi Yousef Abbaspour_Gilandeh José Luis Hernández-Hernández Mario Hernández Hernández Iván Gallardo Bernal (2021, [Artículo])

Estimation of crop damage plays a vital role in the management of fields in the agricultura sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds¿ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of Deep learning-based models to classify other damaged crops.

rapeseed classification damaged crops deep neural networks INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ALIMENTOS