Repositorio Nacional

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

Combination of linkage and association mapping with genomic prediction to infer QTL regions associated with gray leaf spot and northern corn leaf blight resistance in tropical maize

David Berger Yoseph Beyene Collins Juma Suresh L.M. Manje Gowda (2023, [Artículo])

Gray Leaf Spot Northern Corn Leaf Blight CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE LEAF SPOTS QUANTITATIVE TRAIT LOCI ASSOCIATION MAPPING GENOME-WIDE ASSOCIATION STUDIES

Spatiotemporal analysis of rainfall and temperature variability and trends for climate resilient maize farming system in major agroecology zones of northwest Ethiopia

Kindie Tesfaye Dereje Ademe Enyew Adgo (2023, [Artículo])

Spatiotemporal studies of the annual and seasonal climate variability and trend on an agroecological spatial scale for establishing a climate-resilient maize farming system have not yet been conducted in Ethiopia. The study was carried out in three major agroecological zones in northwest Ethiopia using climate data from 1987 to 2018. The coefficient of variation (CV), precipitation concertation index (PCI), and rainfall anomaly index (RAI) were used to analyze the variability of rainfall. The Mann-Kendall test and Sen’s slope estimator were also applied to estimate trends and slopes of changes in rainfall and temperature. High-significance warming trends in the maximum and minimum temperatures were shown in the highland and lowland agroecology zones, respectively. Rainfall has also demonstrated a maximum declining trend throughout the keremt season in the highland agroecology zone. However, rainfall distribution has become more unpredictable in the Bega and Belg seasons. Climate-resilient maize agronomic activities have been determined by analyzing the onset and cessation dates and the length of the growth period (LGP). The rainy season begins between May 8 and June 3 and finishes between October 26 and November 16. The length of the growth period (LGP) during the rainy season ranges from 94 to 229 days.

Climate Trends Spatiotemporal Analysis Agroecology Zone CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGROECOLOGY CLIMATE CLIMATE VARIABILITY MAIZE

Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize

Alexander Loladze Francelino Rodrigues Cesar Petroli Felix San Vicente Garcia Bruno Gerard Osval Antonio Montesinos-Lopez Jose Crossa Johannes Martini (2024, [Artículo])

Common Rust Rp1 Locus CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS REMOTE SENSING VEGETATION INDEX MAIZE CHROMOSOME MAPPING