Abstract
Manipulating plant densities under different irrigation rates can have a significant impact on grain yield and water use efficiency by exerting positive or negative effects on ET. Whereas traditional spectral reflectance indices (SRIs) have been used to assess biophysical parameters and yield, the potential of multivariate models has little been investigated to estimate these parameters under multiple agronomic practices. Therefore, both simple indices and multivariate models (partial least square regression (PLSR) and support vector machines (SVR)) obtained from hyperspectral reflectance data were compared for their applicability for assessing the biophysical parameters in a field experiment involving different combinations of three irrigation rates (1.00, 0.75, and 0.50 ET) and five plant densities (D1: 150, D2: 250, D3: 350, D4: 450, and D5: 550 seeds m-2) in order to improve productivity and water use efficiency of wheat. Results show that the highest values for green leaf area, aboveground biomass, and grain yield were obtained from the combination of D3 or D4 with 1.00 ET, while the combination of 0.75 ET and D3 was the best treatment for achieving the highest values for water use efficiency. Wheat yield response factor (ky) was acceptable when the 0.75 ET was combined with D2, D3, or D4 or when the 0.50 ET was combined with D2 or D3, as the ky values of these combinations were less than or around one. The production function indicated that about 75% grain yield variation could be attributed to the variation in seasonal ET. Results also show that the performance of the SRIs fluctuated when regressions were analyzed for each irrigation rate or plant density specifically, or when the data of all irrigation rates or plant densities were combined. Most of the SRIs failed to assess biophysical parameters under specific irrigation rates and some specific plant densities, but performance improved substantially for combined data of irrigation rates and some specific plant densities. PLSR and SVR produced more accurate estimations of biophysical parameters than SRIs under specific irrigation rates and plant densities. In conclusion, hyperspectral data are useful for predicting and monitoring yield and water productivity of spring wheat across multiple agronomic practices.
Highlights
Overexploitation and uncontrolled pumping of groundwater in several arid regions has resulted in a persistent decline in groundwater levels, a decrease in cropping area, and the amount of water allocated to each crop, and making sustainable agriculture difficult in these regions [1,2]
Irrigation rates combined with different plant densities exerted a significant effect on the measured agronomic parameters, namely Green leaf area (GLA), aboveground total dry weight (TDW), grain yield (GY), and Water use efficiency (WUE), in both growing seasons (Fig 2)
The results of this study showed that the PLSR and support vector machines (SVM) models performed better than the individual spectral reflectance indices (SRIs) when estimating all measured parameters under specific irrigation rate and plant density (Tables 7 and 8)
Summary
Overexploitation and uncontrolled pumping of groundwater in several arid regions has resulted in a persistent decline in groundwater levels, a decrease in cropping area, and the amount of water allocated to each crop, and making sustainable agriculture difficult in these regions [1,2]. Falling groundwater levels during the last two decades are causing serious concerns in the sustainability of the agricultural sector in this country It is more pressing than ever to develop water-saving strategies that will improve the marginal benefit produced per unit of water applied rather than those for enhancing crop production per unit area. Several studies have reported the vital role of drought-tolerant genotypes in dynamically improving irrigation water use; this can only be achieved when combined with appropriate agronomic practices [3,4,5,6] In this context, we propose that the implementation of appropriate agronomic practices could be an essential and economically feasible approach for improving irrigation water use and confronting the adverse effects of water deficits on crop production in droughtprone areas
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