HighlightsRice yield decreased in the spring-summer and summer-autumn seasons and increased in the winter-spring season.The average annual rice yield in the Artibonite Valley is expected to decrease.MarkSim climate data linked with DSSAT provide a means to simulate climate change impacts on crop yield.Abstract. Rice (Oryza sativa) is one of the major crops in the world and one of the most consumed agricultural products in Haiti, with the main production area in the Artibonite Valley. Crop management, poor soil conditions, and weather uncertainty affect rice production in this region. The objective of this study was to determine the potential impact of climate change on rice yield in the Artibonite Valley of Haiti for future periods (near-term: 2010-2039 and mid-century: 2040-2069) under two Representative Concentration Pathways (RCPs 4.5 and 8.5) defined by the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). The Crop Estimation Resource and Environment Synthesis (CERES)-Rice model of the Decision Support for Agrotechnology Transfer (DSSAT) cropping system model was used to perform the simulations using local soil characteristics, meteorological data, and crop management following model calibration with local experimental data. Temperature (maximum and minimum) was predicted to increase during all three rice-growing seasons (spring-summer, summer-autumn, and winter-spring). Under both RCPs (4.5 and 8.5), the simulation results indicated that the ensemble-mean rice yield decreased during the spring-summer and summer-autumn seasons (by 5.1% to 6.6% and by 5.4% to 8.3%, respectively) and increased during the winter-spring season (by 2.3% to 3.6%). Although yield increased during the winter-spring season, the average annual yield was predicted to decrease by 3.6% to 7.1% and by 4.2% to 9.6% for the near-term and mid-century climate periods, respectively. These findings could assist with the implementation of adaptation strategies to mitigate the projected negative impact of climate change on rice production in Haiti. Keywords: Cropping system model, DSSAT, Food security, Global climate model, Rice production, Systems analysis.
California has unsustainable use of agricultural water and energy, as well as problems of severe drought, nitrate pollution and groundwater salinity. As the leading producer and exporter of agricultural produce in the United States, 5.6 percent of California’s energy is currently used for pumping groundwater. These problems and new regulatory policies (e.g., Sustainable Groundwater Management Act, Irrigated Lands Regulatory Program) pressure growers to schedule, account and maintain records of water, energy and nutrients needed for crop and soil management. Growers require varying levels of decision support to integrate different irrigation strategies into farm operations. Decision support can come from the public or private sector, where there are many tradeoffs between cost, underlying science, user friendliness and overall challenges in farm integration. Thus, effective irrigation management requires clear definitions, decision support and guidelines for how to incorporate and evaluate the water–nutrient–energy nexus benefits of different practices and combinations of practices under shifting water governance. The California Energy Commission-sponsored Energy Product Evaluation Hub (Cal-EPE Hub) project has a mission of providing science-based evaluation of energy-saving technologies as a direct result of improved water management for irrigation in agriculture, including current and future irrigation decision support systems in California. This project incorporates end-user perceptions into evaluations of existing decision support tools in partnership with government, agricultural and private stakeholders. In this article, we review the policy context and science underlying the available irrigation decision support systems (IDSS), discuss the benefits/tradeoffs and report on their efficacy and ease of use for the most prevalent cropping systems in California. Finally, we identify research and knowledge-to-action gaps for incorporating irrigation decision support systems into new incentives and requirements for reporting water and energy consumption as well as salinity and nitrogen management in the state of California.
Salt accumulation in the root zone can impair crop yields and profitability. This study utilized a biophysical model to evaluate the economic and environmental impacts of groundwater salinity on the yields and profits for four crops (alfalfa, almonds, table grapes, and processing tomatoes) across California's Central Valley. The model analyzed five different levels of groundwater/irrigation water salinity, which ranged from 0.5 to 5.5 dS/m, with up to 12 mm/day of irrigation water for simulation. The results showed that the model's relative yield prediction was best for alfalfa, while the profits prediction was best for almonds. The study also found that an irrigation water salinity level of 5.5 dS/m could cause a decrease in the relative yield of almonds by up to 40%. The spatial component developed for the model indicated that yield and profits would vary based on soil type and water salinity across the Valley. At a daily irrigation rate of 3 mm, no profits were predicted. When the daily irrigation was increased to 6 mm/day, profits of up to $1000/ha were possible for alfalfa and processing tomatoes. Profits for almonds and grapes reached up to $15000/ha at 9 mm of daily irrigation. The study emphasized the importance of considering irrigation water quality in water allocation and trading decisions, as it significantly impacts crop profitability. The methodology developed in this study can be applied to other regions facing similar challenges of water scarcity and salinity.
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