Performance of predicted evapotranspiration and yield of rainfed wheat in the northeast Iran using gridded AgMERRA weather data

Yaghoubi, Fatemeh; Bannayan, Mohammad; Asadi, Ghorban Ali

doi:10.1007/s00484-020-01931-y

Cited by 11 publications

(5 citation statements)

References 41 publications

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“…The climate data for this study were obtained from the website of Climate Change, Agriculture and Food Security (CCAFS) (www.ccafs-climate.org/data/ (accessed on 31 May 2021)), developed by Consultative Group on International Agricultural Research (CGIAR), under a research program addressing the climate change impact on agricultural production. The AgMERRA, a high-resolution (0.25 • × 0.25 • ) daily time-series climate dataset, has been used in CCAFS data portal to simulate future climate data through CMIP5 GCMs, for the purpose of agriculture modeling [20][21][22][23]. Therefore, the AgMIP Modern-Era Retrospective Analysis for Research and Applications (AgMERRA) baseline (historical) data from 1980-2004 (25 years) were used as an available observed data on this website to generate the simulated baseline, and future climate data of 40 years (2020-2059).…”

Section: Climatic Datasets and Correction Methodologymentioning

confidence: 99%

Assessment of Climatic Parameters for Future Climate Change in a Major Agricultural State in India

Jha

Kalita

Cooke

2021

Climate

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The change in future climate will have a prominent impact on crop production and water requirement. Crop production is directly related to climatic variables. Temperature, solar radiation, wind, precipitation, CO2 concentration and other climatic variables dictate crop yield. This study, based on long-term historical data, investigates the patterns and changes in climatic variables (precipitation, temperature, and solar radiation) that would most significantly affect the future crop production in many parts of the world, and especially in India, where most farmers depend on rainfall for rice production. Statistical analyses—box and whisker plot, mean absolute error, Taylor diagram, double mass curve, Mann–Kendall trend test, and projected climate change—were used to assess the significance of the climatic factors for the purpose of agricultural modeling. Large variability in precipitation may cause the flash floods and affect the farming, and at the same time, increase in temperature from baseline period will lead to high water requirement by crops, and may cause drought if rainfall does not occur. Decrease in solar radiation will affect crop growth and development, and thus, would hamper the crop production. The results of this study would be useful in identifying the negative issues arising from climate change in future agricultural practices in Bihar, India. Furthermore, the results can also help in developing management strategies to combat the climate change impact on crop production.

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Section: Climatic Datasets and Correction Methodologymentioning

confidence: 99%

Assessment of Climatic Parameters for Future Climate Change in a Major Agricultural State in India

Jha

Kalita

Cooke

2021

Climate

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“…RMSE represents the average standard deviation of the model's prediction compared to the observation (Kobayashi & Salam 2000, Bai et al 2010, and it is important to highlight, in this case, that the differences between prediction and observation are squared, so that this index shows the greatest deviations between one and the other. MB has an equal dimension between predicted and observed values, and its value is interpreted as the mean deviation between the samples (Yaghoubi et al 2020).…”

Section: Validation Of the Meteorological Datamentioning

confidence: 99%

“…Many studies employ mean square error (MSE) and its rooted variant (RMSE), or mean absolute error (MAE) and its percentage variant (MAPE). Although they are useful, these rates share a common drawback: since their values can range between zero and +∞, a single value of them does not give much information about the performance of the regression with respect to the distribution of the ground truth elements (Yaghoubi et al 2020).…”

Section: Validation Of the Meteorological Datamentioning

confidence: 99%

Exposure To Climate Risk: A Case Study For Coffee Farming In The Region Of Alta Mogiana, São Paulo

Torres

Zezzo

José

et al. 2022

An. Acad. Bras. Ciênc.

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Studies around the world show an increase in global average temperatures, with a consequent increase in extreme events and changes in the distribution of precipitation, causing a decrease in agricultural production and changes in planting areas. This study analyzed the exposure to climate risk that the coffee crop in the region of Alta Mogiana/SP, Brazil has been presenting in the past thirty years . Time series of daily data of maximum and minimum temperature and precipitation were used. By the statistical tests we observed a trend of increase in maximum temperatures daily of approximately 1.4°C and minimum daily of 0.8°C in the municipalities of the region and a trend towards a decrease in precipitation of 0.9 mm daily, indicating greater exposure of the coffee crop in the region to climate risk and increased vulnerability for the coffee producer. In view of these analyses, a literature review was carried out, suggesting agroforestry systems and mechanical irrigation as the most promising strategies to manage climate risk in coffee plantations. In addition, drought-resistant cultivars, training courses for farmers, increased rural insurance, and nutritional control of the plants can also be considered efficient options for climate exposure in coffee plantations from Alta Mogiana.

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“…In this study, the suggested approach optimizes the operation of 10 3 ha agricultural lands in the northeast of Iran in the province of Great Khorasan. 46 The main aim is to integrate the farm DR to the IPG during a warm day in summer. Figure 6 shows the locational information of the understudied farms in terms of latitude and longitude.…”

Section: Input Datamentioning

confidence: 99%

“…The locational data of understudied agricultural lands in the province of Khorasan in the northeast of Iran 46 [Colour figure can be viewed at wileyonlinelibrary.com]…”

Section: Numerical Studiesmentioning

confidence: 99%

Operational scheduling of responsive prosumer farms for day‐ahead peak shaving by agricultural demand response aggregators

Golmohamadi

2020

Int J Energy Res

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This paper proposes a novel structure to optimize the operational strategies of responsive farms for day-ahead peak shaving. To achieve the aim, the modern irrigation system of farms, including groundwater, surface water, and booster water pumps, are modeled mathematically. To develop the demand response (DR) potentials of the farms, electrical storage systems, and self-generation facilities, including thermal distributed generations and on-farm solar sites, are addressed. In order to facilitate the integration of the agricultural DR programs into the electricity market, a mathematical formulation for agricultural demand response aggregator (ADRA) is suggested. The ADRA participates in the dayahead electricity market on behalf of the responsive farms. To overcome the price uncertainty of the electricity market, a robust optimization approach is addressed. This approach determines the robust decisions of farms in the worstcase realizations of the uncertain electricity price. Regarding on-farm solar sites located in rural areas, a data-driven approach is used to estimate the solar power generation of a significant number of sites without needing to install costly communication and measurement infrastructures. Finally, the proposed approach is implemented on agricultural lands in the northeast of Iran. The numerical results show that the suggested approach provides day-ahead peak shaving for the power systems meeting the crop's water requirements.

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Performance of predicted evapotranspiration and yield of rainfed wheat in the northeast Iran using gridded AgMERRA weather data

Cited by 11 publications

References 41 publications

Assessment of Climatic Parameters for Future Climate Change in a Major Agricultural State in India

Assessment of Climatic Parameters for Future Climate Change in a Major Agricultural State in India

Exposure To Climate Risk: A Case Study For Coffee Farming In The Region Of Alta Mogiana, São Paulo

Operational scheduling of responsive prosumer farms for day‐ahead peak shaving by agricultural demand response aggregators

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