Sen4Rust: sentinel satellites for wheat rust disease forecasting

Gerald Blasch (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MONITORING SATELLITE OBSERVATION RUSTS FORECASTING

Multi-environment genomic prediction of plant traits using deep learners with dense architecture

Osval Antonio Montesinos-Lopez Jose Crossa (2018, [Artículo])

Shared Data Resources Deep Learning Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ACCURACY GENOMICS NEURAL NETWORKS FORECASTING DATA MARKER-ASSISTED SELECTION

Establishment of heterotic groups for hybrid wheat breeding

Yunbi Xu (2022, [Artículo])

Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE CROPS FORECASTING PLANTS COMBINING ABILITY HETEROSIS HETEROTIC GROUPS MALE INFERTILITY PLANT HEIGHT WHEAT

Population and food in the early twenty-first century: meeting future food demand of an increasing population

Nurul Islam (1995, [Libro])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FOOD POLICIES FOOD PRODUCTION FOOD SUPPLY FORECASTING

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

Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall

Nachiketa Acharya Carlo Montes Timothy Joseph Krupnik (2023, [Artículo])

Bangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate-risk-prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi-model ensemble (CMME) of seven state-of-the-art general circulation models (GCMs) from the North American Multi-Model Ensemble project. The lead-1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA-based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real-time seasonal rainfall forecasts using this new forecast system.

Multi-Model Ensemble Seasonal Forecasting CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE SERVICES FORECASTING MONSOONS

Genomic approaches for improving grain zinc and iron content in wheat

Chandan Roy Govindan Velu (2022, [Artículo])

Genome-Wide Association Study New Breeding Techniques Genomic Selection CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BIOFORTIFICATION MARKER-ASSISTED SELECTION MALNUTRITION BREEDING QUANTITATIVE TRAIT LOCI MAPPING SPEED BREEDING ZINC IRON WHEAT

Análisis de velocidad de soldadura robotizada para proceso MIG en acero1045 AISI

Carlos Eduardo Hernández Acero (2022, [Tesis de maestría])

Esta investigación denominada “análisis de velocidad de soldadura robotizada para proceso MIG en acero 1045 AISI” busca el desarrollo de un modelo matemático que permita, mediante parámetros conocidos, el cálculo de la penetración de soldadura en piezas unidas con proceso Metal Inert Gas (MIG). El objetivo se centra en buscar la combinación de valores paramétricos para la velocidad de soldadura, el voltaje y la corriente, los que se obtienen mediante la aplicación de un diseño experimental, que, ejecutadas en el proceso, pueda lograr una penetración de soldadura aceptable para la unión de la pieza. El modelo de superficie de respuesta aplicado fue un diseño de experimentos 2k con diseño central compuesto, y posteriormente el modelo se utilizó para estimar la velocidad de soldadura. Este diseño de experimentos se ejecutó por medio del programa estadístico Minitab versión 17. (Hernández Acero et al., 2022). Una vez teniendo la penetración de soldadura deseada en la pieza, se puede calcular la velocidad de soldadura necesaria para el cálculo de tiempo ciclo y utilizar los parámetros definidos de corriente y voltaje. Los valores de estos factores serán útiles para comenzar la programación del robot ya con la celda funcional y reducir el tiempo de arranque en automático (Hernández Acero et al., 2022).

This research, named “robotic welding speed analysis for MIG process in 1045 AISI steel”, seeks to develop a mathematical model that allow, through know parameters, the calculation of welding penetration in parts joined with Metal Inert Gas (MIG) process. The objective is focused on finding the combination of parametric values for robot welding speed, voltage and current, which are obtained through the application of a design of experiments, which, executed in the process, can achieve an acceptable welding penetration for the union of the part. The response surface model applied was a 2k design of experiments with central composite design, and subsequently the model was used to estimate the welding speed. This design of experiments was obtained using the statistical software Minitab version 17. (Hernández Acero et al., 2022). Once having the desired welding penetration in the part, it can calculate the welding speed necessary for the cycle time calculation and use the defined parameters of current and voltage to start programming the robot with the functional cell and reduce the time of startup (Hernández Acero et al., 2022).

Soldadura MIG Penetración Tiempo ciclo Velocidad de soldadura MIG welding Penetration Cycle time Welding speed INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS OTRAS ESPECIALIDADES TECNOLÓGICAS OTRAS OTRAS

Smallholder maize yield estimation using satellite data and machine learning in Ethiopia

Zhe Guo Jordan Chamberlin Liangzhi You (2023, [Artículo])

The lack of timely, high-resolution data on agricultural production is a major challenge in developing countries where such information can guide the allocation of scarce resources for food security, agricultural investment, and other objectives. While much research has suggested that remote sensing can potentially help address these gaps, few studies have indicated the immediate potential for large-scale estimations over both time and space. In this study we described a machine learning approach to estimate smallholder maize yield in Ethiopia, using well-measured and broadly distributed ground truth data and freely available spatiotemporal covariates from remote sensing. A neural networks model outperformed other algorithms in our study. Importantly, our work indicates that a model developed and calibrated on a previous year's data could be used to reasonably estimate maize yield in the subsequent year. Our study suggests the feasibility of developing national programs for the routine generation of broad-scale and high-resolution estimates of smallholder maize yield, including seasonal forecasts, on the basis of machine learning algorithms, well-measured ground control data, and currently existing time series satellite data.

Sentinel-2 Smallholder Agriculture Yield Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA INTENSIFICATION SMALLHOLDERS AGRICULTURE YIELD FORECASTING

Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India

Nand Lal Kushwaha Paresh Shirsath Dipaka Ranjan Sena (2022, [Artículo])

FResampler1 Seasonal Climate Forecasts Decision Support System for Agrotechnology Transfer CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE DECISION SUPPORT SYSTEMS YIELDS RICE RISK MANAGEMENT