AquaCrop model calibration and validation for chickpea(Cicer arietinum)in Southern Africa.

Abstract

The study parameterised and validated performance of AquaCrop model, to simulate attainable yields for chickpea crop in response to the effects of planting date in the Northestern Region of South Africa. Model calibration data were obtained from two field experiments of contrasting water regime, planted in 2014 winter season at University of Venda, South Africa, whilst data for model validation was obtained from 2015 planting season from the same station. The model performance was satisfactory, with a good combination between the simulated and observed canopy cover (CC), Soil water content (SWC), biomass (B) and grain yield (Y). All the statistical indicators (R 2 , RMSE, and MAPE) used to compare field observed and model-predicted parameters, showed good performance. For example, the regression analysis of simulated and observed yield showed a good relationship with R 2 values of 0.949, 0.928 and 0.990 for early, normal and late planting dates, respectively, whilst RMSE was 0.135, 0.143 and 0.031 for early, normal and late planting dates. Similarly, SWC had strong regression relationship (R 2 = 0.982, 0.949, 0.954, respectively, and a RMSE of 0.226, 0.696 and 0.310, respectively, in early, normal and late planting date). The results indicate that the model could be used for evaluating the effects of different planting dates on chickpea yield.

Performance Evaluation of AquaCrop Model of Tomato under Stage Wise Deficit Drip Irrigation at Southern Ethiopia

“…This shows that applying stage deficit irrigation improves yield and water productivity compared to FI [4]. This result is also confirmed by [22,24,47,48,49,50]; the best WP was observed at the lowest irrigation depth.…”

“…This could be due to the fact that AquaCrop clarifies the process of canopy cover decrease in crop senescence [25]. Biomass and yield water productivity decreases with the increase in transpiration [22]. The highest WP (1.79 kg/m 3 ) was recorded in the application of I50V × 100R which was not different from I75V × 100R, I75F × 100R and I75R × 100R in the experimental seasons.…”

“…This practice has been carried out in different climatic and agronomic conditions on the yield and productivity of different crops in different areas, such as potato [15,17,18], maize [19,20], cassava [21], chickpea [22], canola [23], and tomato [24,25,26,27,28]. However, most of these studies have investigated the applicability of the AquaCrop model under reduced water application of independent of growth stages and also the model performance varied with variations of crop types, climate, locations, irrigation seasons [29], the soil texture affects the model performance [30], some reports were found in the literature concerning the calibration of this model for arid climates on tomato crop.…”

Regional water footprint assessment for a semi-arid basin in India

“…Numerous studies have concluded that WF can be reduced by adopting strategies, methods, and technologies to reduce non-beneficial consumptive water use ( Jovanovic et al, 2020 ). Some practices can upgrade the water management in agricultural fields by implementing precision irrigation methods ( Smith, 2011 ; Abioye et al, 2020 ), improving irrigation efficiency ( Evans & Sadler, 2008 ; Greenwood et al, 2010 ), and irrigation scheduling ( Hinton Consulting, 2001 ; Tesema et al, 2011 ; Wen, Shang & Yang, 2017 ), adopting better agricultural practices like drip irrigation and mulching ( Chukalla, Krol & Hoekstra, 2015 ; Nouri et al, 2019 ; Scardigno, 2020 ; Ding et al, 2021 ) and augmenting water productivity ( Igbadun, Ramalan & Oiganji, 2012 ; Muhammad, Zhu & Bazai, 2017 ; Mubvuma, Ogola & Mhizha, 2021 ). Agronomics practices and in-situ water conservation can significantly reduce local water scarcity ( Kumar et al, 2021 ; Sharma et al, 2021 ; Singh et al, 2021 ).…”