Chapter 11. Genomic insights on global journeys of adaptive wheat genes that brought us to modern wheat

deepmala sehgal Laura Dixon Diego Pequeno Jose Crossa Alison Bentley Susanne Dreisigacker (2024, [Capítulo de libro])

Hexaploid Wheat Adaptive Genes Novel Genomic Regions Gene-Based Modeling Process-Based Modeling Global Food Security CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HEXAPLOIDY WHEAT QUANTITATIVE TRAIT LOCI MODELLING FOOD SECURITY

Catching-up with genetic progress: Simulation of potential production for modern wheat cultivars in the Netherlands

João Vasco Silva Frits K. Van Evert Pytrik Reidsma (2023, [Artículo])

Context: Wheat crop growth models from all over the world have been calibrated on the Groot and Verberne (1991) data set, collected between 1982 and 1984 in the Netherlands, in at least 28 published studies to date including various recent ones. However, the recent use of this data set for calibration of potential yield is questionable as actual Dutch winter wheat yields increased by 3.1 Mg ha-1 over the period 1984 – 2015. A new comprehensive set of winter wheat experiments, suitable for crop model calibration, was conducted in Wageningen during the growing seasons of 2013–2014 and of 2014–2015. Objective: The present study aimed to quantify the change of winter wheat variety traits between 1984 and 2015 and to examine which of the identified traits explained the increase in wheat yield most. Methods: PCSE-LINTUL3 was calibrated on the Groot and Verberne data (1991) set. Next, it was evaluated on the 2013–2015 data set. The model was further recalibrated on the 2013–2015 data set. Parameter values of both calibrations were compared. Sensitivity analysis was used to assess to what extent climate change, elevated CO2, changes in sowing dates, and changes in cultivar traits could explain yield increases. Results: The estimated reference light use efficiency and the temperature sum from anthesis to maturity were higher in 2013–2015 than in 1982–1984. PCSE-LINTUL3, calibrated on the 1982–1984 data set, underestimated the yield potential of 2013–2015. Sensitivity analyses showed that about half of the simulated winter wheat yield increase between 1984 and 2015 in the Netherlands was explained by elevated CO2 and climate change. The remaining part was explained by the increased temperature sum from anthesis to maturity and, to a smaller extent, by changes in the reference light use efficiency. Changes in sowing dates, biomass partitioning fractions, thermal requirements for anthesis, and biomass reallocation did not explain the yield increase. Conclusion: Recalibration of PCSE-LINTUL3 was necessary to reproduce the high wheat yields currently obtained in the Netherlands. About half of the reported winter wheat yield increase was attributed to climate change and elevated CO2. The remaining part of the increase was attributed to changes in the temperature sum from anthesis to maturity and, to a lesser extent, the reference light use efficiency. Significance: This study systematically addressed to what extent changes in various cultivar traits, climate change, and elevated CO2 can explain the winter wheat yield increase observed in the Netherlands between 1984 and 2015.

Light Use Efficiency Potential Yield CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROP MODELLING LIGHT PHENOLOGY MAXIMUM SUSTAINABLE YIELD TRITICUM AESTIVUM WINTER WHEAT

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

Planting rice at monsoon onset could mitigate the impact of temperature stress on rice–wheat systems of Bihar, India

Carlo Montes Anton Urfels Eunjin Han Balwinder-Singh (2023, [Artículo])

Rainy Season TIMESAT APSIM Agricultural Production Systems Simulator Climate Adaptation CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RICE WHEAT MONSOONS WET SEASON CROP MODELLING CLIMATE CHANGE ADAPTATION

Modelado y acoplamiento de la conductividad eléctrica e hidráulica a partir de tomografía de rocas

Modeling and coupling of electrical and hydraulic conductivity from rock tomography

Miguel Ángel Martínez Rodríguez (2022, [Tesis de maestría])

En este trabajo se emplearon técnicas de modelado numérico para simular el flujo de corriente eléctrica y de fluido a través de medios porosos con el fin de determinar el factor de resistividad y la permeabilidad, así como la distribución de los campos de densidad de corriente eléctrica y velocidad de flujo. Para el modelado de flujo eléctrico se desarrolló un algoritmo basado en diferencias finitas, mientras que para el modelado hidráulico se empleó una librería reportada en la literatura, basada en el método de redes de Boltzmann. En ambos esquemas de modelado se establecieron condiciones en la frontera poro-grano para modelar los procesos físicos exclusivamente en el espacio poroso. Los valores estimados de factor de resistividad y de permeabilidad, así como la porosidad, se emplearon para estudiar las correlaciones entre estas propiedades a través de relaciones petrofísicas. Para esto, se propuso una expresión que relaciona la permeabilidad y la porosidad y, empleando una relación existente entre el factor de resistividad y la porosidad, se propuso también una relación directa entre la permeabilidad y el factor de resistividad. Las relaciones propuestas fueron aplicadas a los valores numéricos obtenidos para paquetes de esferas generados numéricamente y se encontró que se ajustan mejor a los datos en comparación con las relaciones más comúnmente utilizadas, especialmente para porosidades altas. Se mostró también que estas relaciones petrofísicas toman la forma de las relaciones más comunes conocidas cuando se trata con porosidades bajas. Valores obtenidos de imágenes digitales de un paquete de esferas sintético y una muestra de dolomita mostraron que las expresiones para porosidades bajas son suficientes para ajustar datos de medios porosos con porosidades menores a un valor entre 0.3 y 0.4. Finalmente, se analizaron el factor de resistividad, la permeabilidad, las relaciones petrofísicas, y las distribuciones espaciales y estadísticas de los campos vectoriales de flujo se analizaron para comparar los fenómenos de transporte eléctrico e hidráulico, encontrando que algunos factores, como la porosidad efectiva, son importantes en ambos fenómenos de flujo; mientras que otros, como la adherencia del fluido a las paredes del poro, son particularmente relevantes para el flujo hidráulico.

In this work, numerical modeling techniques were used to simulate the flow of electric current and fluid through porous media in order to determine the resistivity factor and permeability, as well as the distribution of electric current density and flow velocity fields. For electric flow modeling, an algorithm based on finite differences was developed, while for hydraulic modeling, a library reported in the literature, based on lattice Boltzmann method, was used. In both modeling schemes, pore-grain boundary conditions were established to model the physical processes exclusively in the pore space. The estimated values of resistivity factor and permeability, as well as porosity, were used to study the correlations between these properties through petrophysical relationships. An expression relating permeability and porosity was proposed and, using an existing relationship between the resistivity factor and the porosity, a direct relation between permeability and resistivity factor was also proposed. The proposed relations were applied to data obtained for numerically generated sphere packs and were found to fit the data better than the most commonly used relationships, especially for high porosities. It was also shown that these petrophysical relationships take the form of the most common relationships known when dealing with low porosities. Modeling data on digital images of a synthetic sphere pack and a dolomite sample showed that the expressions for low porosities are sufficient to fit data from porous media with porosities lower than 0.3 to 0.4. Finally, resistivity factors, permeabilities, petrophysical relationships, and spatial and statistical distributions of flow vector fields were analyzed to compare electrical and hydraulic transport phenomena, finding that some factors, such as the effective porosity, are important in both flow phenomena; whereas some other, such as the pore-wall adherence, are particularly relevant to hidraulic flux.

Física de rocas, modelado numérico, relaciones petrofísicas, fenómenos de transporte, factor de resistividad, permeabilidad, porosidad, tomografía de rocas, campos vectoriales, distribución estadística Rock physics, numerical modelling, petrophysical relations, transport phenomena, resistivity factor, permeability, porosity, rock tomography, vector fields, statistical distribution CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS DE LA TIERRA Y DEL ESPACIO GEOFÍSICA GEOFÍSICA DE LA MASA SÓLIDA TERRESTRE GEOFÍSICA DE LA MASA SÓLIDA TERRESTRE