scholarly journals Field experimentation based simulation of yield response of maize crop to deficit irrigation using AquaCrop model, Arba Minch, Ethiopia

2015 ◽  
Vol 10 (4) ◽  
pp. 269-280 ◽  
Author(s):  
Gebreselassie Yemane ◽  
Ayana Mekonen ◽  
Tadele Kassa
Keyword(s):  
Deficit Irrigation ◽  
Yield Response ◽  
Maize Crop ◽  
Aquacrop Model ◽  
Field Experimentation

scholarly journals An Economic-Based Evaluation of Maize Production under Deficit and Supplemental Irrigation for Smallholder Farmers in Northern Togo, West Africa

Resources ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 175
Author(s):  
Agossou Gadedjisso-Tossou ◽  
Tamara Avellán ◽  
Niels Schütze
Keyword(s):  
West Africa ◽  
Smallholder Farmers ◽  
Irrigation Management ◽  
Management Strategies ◽  
Irrigation Scheduling ◽  
Yield Response ◽  
Exceedance Probability ◽  
World Population ◽  
Supplemental Irrigation ◽  
Aquacrop Model

While the world population is expected to reach 9 billion in 2050, in West Africa, it will more than double. This situation will lead to a high demand for cereals in the region. At the same time, farmers are experiencing yield losses due to erratic rainfall. To come up with a sound and effective solution, the available but limited water should be used to achieve high yields through irrigation. Therefore, full and deficit irrigation management strategies were evaluated. The expected profit that can be obtained by a smallholder farmer under a conventional irrigation system in the short-term of investment was also assessed considering rope and bucket, treadle pump, and motorized pump water-lifting methods. The study focused on maize in northern Togo. The framework used in this study consisted of (i) a weather generator for simulating long-term climate time series; (ii) the AquaCrop model, which was used to simulate crop yield response to water; and (iii) a problem-specific algorithm for optimal irrigation scheduling with limited water supply. Results showed high variability in rainfall during the wet season leading to significant variability in the expected yield under rainfed conditions. This variability was substantially reduced when supplemental irrigation was applied. This holds for the irrigation management strategies evaluated in the dry season. Farmers’expected net incomes were US$ 133.35 and 78.11 per hectare for treadle pump and rope and bucket methods, respectively, under 10% exceedance probability. The motorized pump method is not appropriate for smallholder farmers in the short run.

Simulation of Canopy Cover, Soil Water Content and Yield Using FAO-AquaCrop Model under Deficit Irrigation Strategies

2020 ◽  
Vol 46 (3) ◽  
pp. 279-288
Author(s):  
Mohmed A. M. Abdalhi ◽  
Zhonghua Jia ◽  
Wan Luo ◽  
Osama O. Ali ◽  
Cheng Chen
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Deficit Irrigation ◽  
Canopy Cover ◽  
Cover Soil ◽  
Aquacrop Model

scholarly journals Maize Crop Coefficient Estimated from UAV-Measured Multispectral Vegetation Indices

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5250 ◽  
Author(s):  
Yu Zhang ◽  
Wenting Han ◽  
Xiaotao Niu ◽  
Guang Li
Keyword(s):  
Water Management ◽  
Deficit Irrigation ◽  
Regression Models ◽  
Vegetation Index ◽  
Measurement Data ◽  
Crop Coefficient ◽  
Maize Crop ◽  
Significant Difference ◽  
Farm Scale ◽  
Different Levels

The rapid, accurate, and real-time estimation of crop coefficients at the farm scale is one of the key prerequisites in precision agricultural water management. This study aimed to map the maize crop coefficient (Kc) with improved accuracy under different levels of deficit irrigation. The proposed method for estimating the Kc is based on multispectral images of high spatial resolution taken using an unmanned aerial vehicle (UAV). The analysis was performed on five experimental plots using Kc values measured from the daily soil water balance in Ordos, Inner Mongolia, China. To accurately estimate the Kc, the fraction of vegetation cover (fc) derived from the normalized difference vegetation index (NDVI) was used to compare with field measurements, and the stress coefficients (Ks) calculated from two vegetation index (VI) regression models were compared. The results showed that the NDVI values under different levels of deficit irrigation had no significant difference in the reproductive stage but changed significantly in the maturation stage, with a decrease of 0.09 with 72% water applied difference. The fc calculated from the NDVI had a high correlation with field measurement data, with a coefficient of determination (R2) of 0.93. The ratios of transformed chlorophyll absorption in reflectance index (TCARI) to renormalized difference vegetation index (RDVI) and TCARI to soil-adjusted vegetation index (SAVI) were used, respectively, to establish two types of Ks regression models to retrieve Kc. Compared to the TCARI/SAVI model, the TCARI/RDVI model under different levels of deficit irrigation had better correlation with Kc, with R2 and root-mean-square error (RMSE) values ranging from 0.68 to 0.80 and from 0.140 to 0.232, respectively. Compared to Kc calculated from on-site measurements, the Kc values retrieved from the VI regression models established in this study had greater ability to assess the field variability of soil and crops. Overall, use of the UAV-measured multispectral vegetation index approach could improve water management at the farm scale.

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