The Experts below are selected from a list of 64662 Experts worldwide ranked by ideXlab platform
James P. Chryssanthacopoulos - One of the best experts on this subject based on the ideXlab platform.
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A climate generator for Agricultural Planning in southeastern
2015Co-Authors: Arthur M. Greene, Lisa M. Goddard, James P. ChryssanthacopoulosAbstract:A method is described for the generation of climate scenarios in a form suitable for driving Agricultural models. The scenarios are tailored to the region in southeastern South America bounded by 25–40
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A climate generator for Agricultural Planning in southeastern South America
Agricultural and Forest Meteorology, 2015Co-Authors: Arthur M. Greene, Lisa M. Goddard, Paula Gonzalez, Amor Valeriano M. Ines, James P. ChryssanthacopoulosAbstract:Abstract A method is described for the generation of climate scenarios in a form suitable for driving Agricultural models. The scenarios are tailored to the region in southeastern South America bounded by 25–40° S, 45–65° W, denoted here as SESA. SESA has been characterized by increasing summer precipitation, particularly during the late 20th century, which, in the context of favorable market conditions, has enabled increases in Agricultural production. Since about year 2000, however, the upward tendency appears to have slowed or possibly stopped, raising questions about future climate inputs to regional Agricultural yields. The method is not predictive in the deterministic sense, but rather attempts to characterize uncertainty in near-term future climate, taking into account both forced trends and unforced, natural climate fluctuations. It differs from typical downscaling methods in that GCM information is utilized only at the regional scale, subregional variability being modeled based on the observational record. Output, generated on the monthly time scale, is disaggregated to daily values with a weather generator and used to drive soybean yields in the crop model DSSAT-CSM, for which preliminary results are discussed. The simulations produced permit assessment of the interplay between long-range trends and near-term climate variability in terms of Agricultural production.
Mohammad Karamouz - One of the best experts on this subject based on the ideXlab platform.
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developing an Agricultural Planning model in a watershed considering climate change impacts
Journal of Water Resources Planning and Management, 2013Co-Authors: Mohammad Karamouz, Behzad Ahmadi, Zahra ZahmatkeshAbstract:AbstractSocieties are facing major challenges in allocating water resources to growing water demands due to population growth and industrial and Agricultural developments. With increasing water scarcity, the need to increase Agricultural water productivity is receiving significant attention in developing countries. Among alternative options for meeting increasing water demand, improving productivity has received considerable attention. Therefore, Planning of water systems to face future development conditions needs further studies on land, water use, and resources as well as consideration of objectives to maximize crop production to achieve the maximum net return. Because climate change is likely to have impact on the hydrological cycle and consequently on the available water resources and Agricultural water demand, there are concerns about the effects of climate change on Agricultural productivity. Considering climate change impacts, in order to optimize Agricultural productivity, practical frameworks an...
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integrated Planning of land use and water allocation on a watershed scale considering social and water quality issues
Journal of Water Resources Planning and Management, 2012Co-Authors: Azadeh Ahmadi, Mohammad Karamouz, Ali MoridiAbstract:AbstractSustainable development in river basins depends on sound management of land use and water allocation policies. Integrated water resources management (IWRM) is considered a path to bring many elements within the development schemes together toward a unified land-water Planning and management process. In this study, an integrated water resources management model is developed to connect three groups of decision makers in pollution control, Agricultural Planning, and water resources allocation with economic, environmental, and social objectives. A genetic algorithm–based optimization model is developed for providing desirable water quality and quantity while maximizing Agricultural production in the upstream region, mitigating the unemployment (social) impacts of land use changes, and providing reliable water supply to the downstream region. The upstream region is divided into subbasins, and a fuzzy-based multiobjective optimization model is used to determine the optimal land uses in each subbasin and...
Samuel G. K. Adiku - One of the best experts on this subject based on the ideXlab platform.
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Decision support tools for site-specific fertilizer recommendations and Agricultural Planning in selected countries in sub-Sahara Africa
Nutrient Cycling in Agroecosystems, 2018Co-Authors: Dilys S. Maccarthy, Job Kihara, Patricia Masikati, Samuel G. K. AdikuAbstract:Recommendations and decisions of crop management in sub-Saharan Africa (SSA) are often based on traditional field experimentation. This usually ignores the variability of production factors in space and time, and hence invalidates such decisions and recommendations outside of the experimental sites. Yet, the use of alternative or complementary decision support approaches such as crop modelling is limited. In this paper, we reviewed the state of the use of crop modelling in informing site specific fertilizer recommendations in some countries in SSA. Even though nitrogen fertilizer recommendations in most countries across Africa are blanket, the limited employment of models show that optimum nitrogen application should be differentiated according to soil types, management and climate. A number of studies reported on increased fertilizer use efficiency and reduced crop production risks with the use of decision support tools (DST). The review also showed that the gross limitation of the use of models as Agricultural decision-making tools in SSA could be attributed to factors such as low capacity due to limited training opportunities, and the general lack of support from national governments for model development and application for policy formulation. Proposals identified to overcome these limitations include (1) introduction of the science of DST in the curricula at the tertiary level, (2) encouragement and support for the adoption of model use by governmental and non-governmental organizations as additional tools for decision making and (3) simplifying DSTs to facilitate their use by non-scientific audience to scale uptake and use for farm management.
Paul Block - One of the best experts on this subject based on the ideXlab platform.
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Predicting Rainy Season Onset in the Ethiopian Highlands for Agricultural Planning
Journal of Hydrometeorology, 2020Co-Authors: Jonathan Lala, Seifu A. Tilahun, Paul BlockAbstract:ABSTRACTThe Kiremt rainy season is the foundation of agriculture in the Ethiopian Highlands and a key driver of economic development as well as the instigator of famines that have plagued the country’s history. Despite the importance of these rains, relatively little research exists on predicting the season’s onset; even less research evaluates statistical modeling approaches, in spite of their demonstrated utility for decision-making at local scales. To explore these methods, predictions are generated conditioned on three definitions of onset, at three lead times, using partial least squares (PLS) regression and random forest classification. Results illustrate moderate prediction skill and an ability to avoid false onsets, which may guide planting decisions; however, they are highly sensitive to how onset is defined, suggesting that future prediction approaches should additionally consider local Agricultural definitions of onset.
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Integrating climate prediction and regionalization into an agro-economic model to guide Agricultural Planning
Climatic Change, 2019Co-Authors: Ying Zhang, Liangzhi You, Donghoon Lee, Paul BlockAbstract:Abstract Advanced skill in seasonal climate prediction coupled with sectoral decision models can provide decision makers with opportunities to benefit or reduce unnecessary losses. Such approaches are particularly beneficial to rainfed agriculture, the livelihood choice for the majority of the world’s poor population, for which yields are highly sensitive to climate conditions. However, a notable gap still exists between scientific communities producing predictions and the end users who may actually realize the benefits. In this study, an interdisciplinary approach connecting climate prediction to Agricultural Planning is adopted to address this gap. An ex ante evaluation of seasonal precipitation prediction is assessed using an agro-economic equilibrium model to simulate Ethiopia’s national economy, accounting for interannual climate variability and prediction-guided Agricultural responses. Given the high spatial variability in Ethiopian precipitation, delineation of homogeneous climatic regions (i.e., regionalization) is also considered in addition to growing season precipitation prediction. The model provides perspectives across various economic indices (e.g., gross domestic product, calorie consumption, and poverty rate) at aggregated (national) and disaggregated (zonal) scales. Model results illustrate the key influence of climate on the Ethiopian economy, and prospects for positive net benefits under a prediction-guided Agricultural Planning (e.g., reallocation of crop types) strategy, as compared with static business-as-usual Agricultural practices.
Arthur M. Greene - One of the best experts on this subject based on the ideXlab platform.
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A climate generator for Agricultural Planning in southeastern
2015Co-Authors: Arthur M. Greene, Lisa M. Goddard, James P. ChryssanthacopoulosAbstract:A method is described for the generation of climate scenarios in a form suitable for driving Agricultural models. The scenarios are tailored to the region in southeastern South America bounded by 25–40
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A climate generator for Agricultural Planning in southeastern South America
Agricultural and Forest Meteorology, 2015Co-Authors: Arthur M. Greene, Lisa M. Goddard, Paula Gonzalez, Amor Valeriano M. Ines, James P. ChryssanthacopoulosAbstract:Abstract A method is described for the generation of climate scenarios in a form suitable for driving Agricultural models. The scenarios are tailored to the region in southeastern South America bounded by 25–40° S, 45–65° W, denoted here as SESA. SESA has been characterized by increasing summer precipitation, particularly during the late 20th century, which, in the context of favorable market conditions, has enabled increases in Agricultural production. Since about year 2000, however, the upward tendency appears to have slowed or possibly stopped, raising questions about future climate inputs to regional Agricultural yields. The method is not predictive in the deterministic sense, but rather attempts to characterize uncertainty in near-term future climate, taking into account both forced trends and unforced, natural climate fluctuations. It differs from typical downscaling methods in that GCM information is utilized only at the regional scale, subregional variability being modeled based on the observational record. Output, generated on the monthly time scale, is disaggregated to daily values with a weather generator and used to drive soybean yields in the crop model DSSAT-CSM, for which preliminary results are discussed. The simulations produced permit assessment of the interplay between long-range trends and near-term climate variability in terms of Agricultural production.
