Performance evaluation and identification of highland quality protein maize hybrids in Ethiopia

Adefris Teklewold (2022, [Artículo])

Quality Protein Conventional Maize CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE PROTEIN QUALITY CROSS-BREEDING HYBRIDS

Dead level contour technical design parameters required for sustainable crop production in semi-arid areas of Zimbabwe

Menas Wuta Isaiah Nyagumbo (2021, [Artículo])

Maize Yield Optimum Interval Dead Level Contours CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA TECHNOLOGY DRY SPELLS MAIZE YIELDS RAINWATER HARVESTING

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

Editorial: Model organisms in plant science: Maize

Manje Gowda (2023, [Artículo])

Model Organism Genomic Selection CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE PLANT SCIENCES RESEARCH CROP IMPROVEMENT PLANT PHYSIOLOGY PLANT BREEDING

On-farm storage loss estimates of maize in Kenya using community survey methods

Hugo De Groote Anani Bruce (2023, [Artículo])

Maize is the most important staple in sub-Saharan Africa (SSA), with highly seasonal production. High storage losses affect food security, but good estimations are lacking. A new method using focus group discussions (FGDs) was tested with 121 communities (1439 farmers, 52% women) in Kenya's six maize-growing zones, to estimate the maize losses to storage pests and analyze farmer practices. As control strategies, half of the farmers used chemical pesticides (49%), while hermetic bags (16%) and botanicals (15%) were also popular. Relative loss from weevils in the long rains was estimated at 23%, in the short rains 18%, and annually 21%. Fewer farmers were affected by the larger grain borer (LGB) than by maize weevils: 42% in the long rainy season and 32% in the short rainy season; losses from LGB were also smaller: 19% in the long season, 17% in the short season, and 18% over the year. Total storage loss, from both species combined, was estimated at 36%, or 671,000 tonnes per year. The greatest losses occur in the humid areas, especially the moist mid-altitudes (56%), and with smaller loss in the drylands (20–23%). Extrapolating the point data and overlaying with the maize production map shows the geographic distribution of the losses, with the most important area found around Lake Victoria. FGDs provide convenient and cheap tools to estimate storage losses in representative communities, but a total loss estimate of 36% is higher than is found in other studies, so its accuracy and framing effects need to be assessed. We conclude that storage pests remain a major problem, especially in western Kenya, and that the use of environmentally friendly technologies such as hermetic storage and botanicals needs more attention, both by the public extension service and private agrodealers.

Larger Grain Borer Maize Weevil CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE STORAGE LOSSES PESTS SURVEY METHODS

REMATTOOL-R: a smart tool for identifying superior maize genotypes from multi-environment yield trials

Lewis Machida Dan Makumbi (2023, [Artículo])

Maize Variety Testing Multienvironment Trial Analysis Relative Maturity REMATTOOL-R Superior Varieties Identification CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE VARIETIES MATURITY IDENTIFICATION YIELDS

Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design

Junjie Fu XUECAI ZHANG (2023, [Artículo])

Genomic Prediction Prediction Model Genetic Effects Hybrid Performance CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE GENETICS HYBRIDS PERFORMANCE ASSESSMENT

Forest proximity positively affects natural enemy mediated control of fall armyworm in Southern Africa

Frédéric Baudron Terence Sunderland (2022, [Artículo])

Insectivorous Birds Bat Predation Maize Cultivation CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FALL ARMYWORMS BIOLOGICAL PEST CONTROL INSECTIVOROUS ANIMALS MAIZE PREDATOR PREY RELATIONS

Multi-omic characterization of the maize GPI synthesis mutant gwt1 with defects in kernel development

Sarah Hearne zhiyuan fu (2023, [Artículo])

Maize Endosperm Development Membrane Proteomics Glycosyl-Phosphatidyl-Inositol Membrane Anchored Proteins CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE ENDOSPERM MEMBRANES PROTEOMICS TRANSCRIPTOMICS

Transmission, localization, and infectivity of seedborne maize chlorotic mottle virus

Suresh L.M. Pierce Paul Margaret Redinbaugh (2023, [Artículo])

Maize Lethal Necrosis Transmission Mechanisms CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE VIROSES SEEDS INOCULATION DISEASE MANAGEMENT