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Adaptation to current and future climatic risks in agriculture: Madhya Pradesh, India
Paresh Shirsath Anil Pimpale Pramod Aggarwal (2022, [Libro])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RISK CLIMATE RESILIENCE AGRICULTURE CLIMATE CHANGE ADAPTATION
Adaptation to current and future climatic risks in agriculture: Rajasthan, India
Paresh Shirsath Anil Pimpale Pramod Aggarwal (2022, [Libro])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RISK CLIMATE RESILIENCE AGRICULTURE CLIMATE CHANGE ADAPTATION
Adaptation to current and future climatic risks in agriculture: Maharashtra, India
Paresh Shirsath Anil Pimpale Pramod Aggarwal (2022, [Libro])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RISK CLIMATE RESILIENCE AGRICULTURE CLIMATE CHANGE ADAPTATION
MARKUS SEBASTIAN GROSS (2016, [Artículo])
In previous work, the authors demonstrated how data from climate simulations can be utilized to estimate regional wind power densities. In particular, it was shown that the quality of wind power densities, estimated from the UPSCALE global dataset in offshore regions of Mexico, compared well with regional high resolution studies. Additionally, a link between surface temperature and moist air density in the estimates was presented. UPSCALE is an acronym for UK on PRACE (the Partnership for Advanced Computing in Europe)-weather-resolving Simulations of Climate for globAL Environmental risk. The UPSCALE experiment was performed in 2012 by NCAS (National Centre for Atmospheric Science)- Climate, at the University of Reading and the UK Met Office Hadley Centre. The study included a 25.6-year, five-member ensemble simulation of the HadGEM3 global atmosphere, at 25km resolution for present climate conditions. The initial conditions for the ensemble runs were taken from consecutive days of a test configuration. In the present paper, the emphasis is placed on the single climate run for a potential future climate scenario in the UPSCALE experiment dataset, using the Representation Concentrations Pathways (RCP) 8.5 climate change scenario. Firstly, some tests were performed to ensure that the results using only one instantiation of the current climate dataset are as robust as possible within the constraints of the available data. In order to achieve this, an artificial time series over a longer sampling period was created. Then, it was shown that these longer time series provided almost the same results than the short ones, thus leading to the argument that the short time series is sufficient to capture the climate. Finally, with the confidence that one instantiation is sufficient, the future climate dataset was analysed to provide, for the first time, a projection of future changes in wind power resources using the UPSCALE dataset. It is hoped that this, in turn, will provide some guidance for wind power developers and policy makers to prepare and adapt for climate change impacts on wind energy production. Although offshore locations around Mexico were used as a case study, the dataset is global and hence the methodology presented can be readily applied at any desired location. © Copyright 2016 Gross, Magar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reprod
atmosphere, climate change, Europe, Mexico, sampling, time series analysis, university, weather, wind power, climate, risk, theoretical model, wind, Climate, Models, Theoretical, Risk, Wind CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS DE LA TIERRA Y DEL ESPACIO OCEANOGRAFÍA OCEANOGRAFÍA
Kindie Tesfaye Dereje Ademe Enyew Adgo (2023, [Artículo])
This study determined the most effective plating density (PD) and nitrogen (N) fertilizer rate for well-adapted BH540 medium-maturing maize cultivars for current climate condition in north west Ethiopia midlands. The Decision Support System for Agrotechnology Transfer (DSSAT)-Crop Environment Resource Synthesis (CERES)-Maize model has been utilized to determine the appropriate PD and N-fertilizer rate. An experimental study of PD (55,555, 62500, and 76,900 plants ha−1) and N (138, 207, and 276 kg N ha−1) levels was conducted for 3 years at 4 distinct sites. The DSSAT-CERES-Maize model was calibrated using climate data from 1987 to 2018, physicochemical soil profiling data (wilting point, field capacity, saturation, saturated hydraulic conductivity, root growth factor, bulk density, soil texture, organic carbon, total nitrogen; and soil pH), and agronomic management data from the experiment. After calibration, the DSSAT-CERES-Maize model was able to simulate the phenology and growth parameters of maize in the evaluation data set. The results from analysis of variance revealed that the maximum observed and simulated grain yield, biomass, and leaf area index were recorded from 276 kg N ha−1 and 76,900 plants ha−1 for the BH540 maize variety under the current climate condition. The application of 76,900 plants ha−1 combined with 276 kg N ha−1 significantly increased observed and simulated yield by 25% and 15%, respectively, compared with recommendation. Finally, future research on different N and PD levels in various agroecological zones with different varieties of mature maize types could be conducted for the current and future climate periods.
Maize Model Planting Density CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE MODELS SPACING NITROGEN FERTILIZERS YIELDS
Hambulo Ngoma João Vasco Silva Frédéric Baudron Isaiah Nyagumbo Christian Thierfelder (2024, [Artículo])
Sustainable agricultural practices such as conservation agriculture have been promoted in southern Africa for nearly three decades, but their adoption remains low. It is of policy interest to unpack behavioural drivers of adoption to understand why adoption remains lower than anticipated. This paper assesses the effects of risk aversion and impatience on the extent and intensity of the adoption of conservation agriculture using panel data collected from 646 households in 2021 and 2022 in Zambia. We find that 12% and 18% of the smallholders were impatient and risk averse, respectively. There are two main empirical findings based on panel data Probit and Tobit models. First, on the extensive margin, being impatient is correlated with a decreased likelihood of adopting combined minimum-tillage (MT) and rotation by 2.9 percentage points and being risk averse is associated with a decreased propensity of adopting combined minimum tillage (MT) and mulching by 3.2 percentage points. Being risk averse is correlated with a decreased chance of adopting basins by 2.8 percentage points. Second, on the intensive margin, impatience and risk aversion are significantly correlated with reduced adoption intensity of basins, ripping, minimum tillage (MT), and combined MT and rotation by 0.02–0.22 ha. These findings imply a need to embed risk management (e.g., through crop yield insurance) in the scaling of sustainable agricultural practices to incentivise adoption. This can help to nudge initial adoption and to protect farmers from yield penalties that are common in experimentation stages.
Risk and Time Preferences CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA TECHNOLOGY ADOPTION RISK SUSTAINABLE INTENSIFICATION SMALLHOLDERS
Martin van Ittersum (2023, [Artículo])
Context: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective: The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods: A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results: Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion: Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance: The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.
Model Accuracy Model Precision Linear Mixed Models CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MACHINE LEARNING SUSTAINABLE INTENSIFICATION BIG DATA YIELDS MODELS AGRONOMY
MLN disease diagnostics, MLN disease-free seed production and MLN disease management
Suresh L.M. (2022, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DISEASES DISEASE MANAGEMENT SEED PRODUCTION MAIZE NECROSIS YIELD LOSSES ECONOMIC IMPACT SURVEILLANCE SYSTEMS TRAINING
Tackling Maize Lethal Necrosis (MLN) in eastern Africa through effective phytosanitary approaches
Suresh L.M. Yoseph Beyene Dan Makumbi Manje Gowda Prasanna Boddupalli (2023, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE NECROSIS DISEASE MANAGEMENT PLANT HEALTH GENE EDITING GERMPLASM
Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])
Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to
the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding