Identifying barriers to adoption of irrigation scheduling tools in Rio Grande Basin

Berthold, T. Allen; Ali, Asadi; Olsovsky, Taylor; Kathuria, Dhruva

doi:10.1016/j.atech.2021.100016

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…We concluded that all types of tools that provide accurat specific information about the weather could postpone the date of first irrigation. I same way, Berthold et al [47] also showed that the use of irrigation tools make it po to optimize water by making informed decisions. These opposite effects of different of assistance variables are noteworthy; they suggest that human assistance advance date of first irrigation, while technological assistance postpones it.…”

Section: Synthesis Of Resultsmentioning

confidence: 92%

Decision-Making Process Factors Explain Some of the Heterogeneity of Irrigation Practices among Maize Farmers in Southwestern France

Albert

Bergez

Couture

et al. 2021

Water

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Agricultural practices are heterogeneous among farmers in the face of climate hazards. Structural and material resources as well as risk preferences explain some of this heterogeneity, but little is known about how psychological factors associated with the decision-making process may explain differences in practices among farmers. The aim of this study was to understand whether decision-making process factors help explain the heterogeneity of a specific practice—the date of first irrigation—among maize farmers, along with material and structural factors. We conducted semi-directed interviews with 35 farmers who irrigated maize in southwestern France. We analyzed discriminating factors of the decision-making process, such as reactivity (i.e., capacity to change plans), deliberation (i.e., level of internal information used to make decisions) and assistance (i.e., level of external information used to make decisions). We used two complementary statistical methods (linear regression and regression trees) to analyze the database. Our study confirms the influence of material and structural factors, and also reveals the strong influence of decision-making process factors. A high level of reactivity is associated with adaptive behavior. Moreover, using decision-support tools and technologies helps farmers to manage the use of water resources. These elements could be used by advisors and public policy-makers in the agriculture sector to improve adaptation.

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Section: Synthesis Of Resultsmentioning

confidence: 92%

Decision-Making Process Factors Explain Some of the Heterogeneity of Irrigation Practices among Maize Farmers in Southwestern France

Albert

Bergez

Couture

et al. 2021

Water

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Free weather forecast and open-source crop modeling for scientific irrigation scheduling: proof of concept

Ajaz,

Berthold,

Xue

et al. 2023

Irrig Sci

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IS-SAR: an irrigation scheduling web application for Hass avocado orchards based on Sentinel-1 images

Erazo-Mesa,

Murillo-Sandoval,

Ramírez-Gil

et al. 2023

Irrig Sci

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As the Hass avocado crop expands exponentially in Colombia, concern about its increasing water use is on the rise. This research aimed to develop IS-SAR, a free-access web application to schedule irrigation for Valle del Cauca’s Hass growers. We calibrated the water cloud (WCM) and artificial neural network (ANN) models using field data measurements from Hass avocado orchard plots in Valle del Cauca (Colombia) and Sentinel 1 (S1) satellite imagery measurements and evaluated their performance computing the root-mean-square error (RMSE) and Pearson correlation coefficient ($$r$$ r ). IS-SAR estimates the surface soil water content from the most recent S1 image, becomes it in water depth, and recommends to users apply irrigation according to allowable depletion limits, computed from a spatially distributed $$\theta$$ θ at field capacity and permanent wilting point obtained from a soil database of the study area. Our results indicate that the surface soil water content was retrieved with a better performance by the ANN (RMSE = 0.05 m3 m−3, $$r=0.74$$ r = 0.74 ), compared with the WCM (RMSE = 0.06 m3 m−3, $$r=0.47$$ r = 0.47 ). IS-SAR simulations in validation orchard plots result in irrigation events of up to 107 L tree−1 for 3.4 h. The IS-SAR web application provides near-real-time irrigation information to assist Hass avocado growers in designing better irrigation routines and improving the regional understanding of water crop consumption.

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Identifying barriers to adoption of irrigation scheduling tools in Rio Grande Basin

Cited by 4 publications

References 41 publications

Decision-Making Process Factors Explain Some of the Heterogeneity of Irrigation Practices among Maize Farmers in Southwestern France

Decision-Making Process Factors Explain Some of the Heterogeneity of Irrigation Practices among Maize Farmers in Southwestern France

Free weather forecast and open-source crop modeling for scientific irrigation scheduling: proof of concept

IS-SAR: an irrigation scheduling web application for Hass avocado orchards based on Sentinel-1 images

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