Water Allocation and Integrative Management of Precision Irrigation: A Systematic Review

Liang, Zhongwei; Liu, Xiaochu; Xiong, Jianguo; Xiao, Jinrui

doi:10.3390/w12113135

Cited by 27 publications

(9 citation statements)

References 116 publications

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“…Previous studies pointed out that too low T c might predict no stress [54,55]. The findings were also proven by our study in which CWSI was only 0.0-0.25 with significant low T c in over-wintering, which might result in an incorrect irrigation decision [56]. Thus, T c combined with soil moisture measurement may be a better way to quantify the water stress of wheat during the over-wintering period.…”

Section: Diurnal Dynamics Of Canopy Temperature and Crop Evapotranspirationsupporting

confidence: 76%

Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield

et al. 2021

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The temperature-based crop water stress index (CWSI) can accurately reflect the extent of crop water deficit. As an ideal carrier of onboard thermometers to monitor canopy temperature (Tc), center pivot irrigation systems (CPIS) have been widely used in precision irrigation. However, the determination of reliable CWSI thresholds for initiating the CPIS is still a challenge for a winter wheat–summer maize cropping system in the North China Plain (NCP). To address this problem, field experiments were carried out to investigate the effects of CWSI thresholds on grain yield (GY) and water use efficiency (WUE) of winter wheat and summer maize in the NCP. The results show that positive linear functions were fitted to the relationships between CWSI and canopy minus air temperature (Tc − Ta) (r2 > 0.695), and between crop evapotranspiration (ETc) and Tc (r2 > 0.548) for both crops. To make analysis comparable, GY and WUE data were normalized to a range of 0.0 to 1.0, corresponding the range of CWSI. With the increase in CWSI, a positive linear relationship was observed for WUE (r2 = 0.873), while a significant inverse relationship was found for the GY (r2 = 0.915) of winter wheat. Quadratic functions were fitted for both the GY (r2 = 0.856) and WUE (r2 = 0.629) of summer maize. By solving the cross values of the two GY and WUE functions for each crop, CWSI thresholds were proposed as being 0.252 for winter wheat, and 0.229 for summer maize, corresponding to a Tc − Ta threshold value of 0.925 and 0.498 °C, respectively. We conclude that farmers can achieve the dual goals of high GY and high WUE using the optimal thresholds proposed for a winter wheat–summer maize cropping system in the NCP.

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Section: Diurnal Dynamics Of Canopy Temperature and Crop Evapotranspirationsupporting

confidence: 76%

Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield

et al. 2021

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“…The research shows that DI has a significant improvement in water-saving capacity in drought-stricken areas compared with traditional BI. Many previous studies have focused on the development of irrigation systems when implementing DI techniques on many crops, and different methods of implementing the same irrigation method will result in different WUE and yield [37][38][39]. Different irrigation systems and management methods under different DI conditions in different crops and regions can increase the WUE and yield by more than 15% [36,40,41], which indicates that DI has strong water-saving potential and needs to be developed urgently.…”

Section: Compared With Precious Studies?mentioning

confidence: 99%

Effect of Drip Irrigation on Soil Water Balance and Water Use Efficiency of Maize in Northwest China

Wang

Cui

et al. 2021

Water

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Drip irrigation (DI) has been widely utilized for crops and its water-saving effect has been confirmed by numerous studies. However, whether this technology can save so much water under the field scale during practical application is still uncertain. In order to answer this question, evapotranspiration (ET), soil water content, transpiration and evaporation over the DI and border irrigation (BI) in an arid area of NW China were continuously measured by two eddy covariance systems, micro-lysimeters, the packaged stem sap flow gauges and CS616 sensors during 2014–2018 growing seasons. The results showed that the DI averagely increased crop water use efficiency (CWUE) by 11% per year against BI. The deep drainage under DI treatment was lower than BI by 8% averagely for the five-year period. While for the ET, the DI averagely decreased ET by 7% and 40mm per year against the traditional BI. The decrease in ET was mainly due to the significant reduction in soil evaporation instead of transpiration. Oppositely, we found that DI may increase maize (Zea mays L.) transpiration in some year for the better preponderant growth of crop. Thus, the accelerating effect on transpiration of DI and its reducing effect on soil evaporation should be considered simultaneously. In our experiment, DI only improved CWUE and WUE (water use efficiency) by 11% and 15% on average in a large farmland scale, unable to always be more than a 20% improvement, as concluded by many other field experiments. Consequently, the water-saving effect of DI should not be overestimated in water resource evaluation.

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“…To quantify the applicability and efficiency of the RSAE-ANFIS approach with alternative ones, such frequently used approaches as genetic optimization, simulated annealing-genetic algorithm (SA-GA), Taguchi parameter estimation, artificial neural network-simulated annealing (ANN-SA) prediction, and genetically optimized neural network (GONN) have been employed in the experimental conditions prearranged by Tables 1 and 2 [34][35][36][37][38]. Figure 9a-d showed the value comparisons of θ, e a , e t and C u between the predicted and actual measured results in tests A-K.…”

Section: Assessments Of Adaptive Prediction Qualitymentioning

confidence: 99%

Adaptive Prediction of Water Droplet Infiltration Effectiveness of Sprinkler Irrigation Using Regularized Sparse Autoencoder–Adaptive Network-Based Fuzzy Inference System (RSAE–ANFIS)

Liang

Liu

Zou

et al. 2021

Water

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As the high productive efficiency of sprinkler irrigation is largely based on balanced soil moisture distribution, it is essential to study the exact effectiveness of water droplet infiltration, which provides a theoretical basis for rationally scheduling the circulation efficiency of groundwater in agricultural irrigation performance. This research carried out adaptive prediction of the droplet infiltration effectiveness of sprinkler irrigation by using a novel approach of a regularized sparse autoencoder–adaptive network-based fuzzy inference system (RSAE–ANFIS), for the purpose of quantifying actual water droplet infiltration and effectiveness results of precision irrigation in various environmental conditions. The intelligent prediction experiment we implemented could be phased as: the demonstration of governing equations of droplet infiltration for sprinkler irrigation modeling; the measurement and computation of probability densities in water droplet infiltration; innovative establishment and working analysis of RSAE–ANFIS; and the adaptive prediction of infiltration effectiveness indexes, such as average soil moisture depth increment (θ, mm), irrigation infiltration efficiency (ea, %), irrigation turn duration efficiency (et, mm/min), and the uniformity coefficient of soil moisture infiltration (Cu, %), which were implemented to provide a comprehensive illustration for the effective scheduling of sprinkler irrigation. Result comparisons indicated that when jetting pressure (Pw) was 255.2 kPa, the impinge angle (Wa) was 42.5°, the water flow rate (Fa) was 0.67 kg/min, and continuous irrigation time (Tc) was 32.4 min (error tolerance = ±5%, the same as follows), thereby an optimum and stable effectiveness quality of sprinkler irrigation could be achieved, whereas average soil moisture depth increment (θ) was 57.6 mm, irrigation infiltration efficiency (ea) was 62.5%, irrigation turn duration efficiency (et) was 34.5 mm/min, and the uniformity coefficient of soil moisture infiltration (Cu) was 53.6%, accordingly. It could be concluded that the proposed approach of the regularized sparse autoencoder–adaptive network-based fuzzy inference system has outstanding predictive capability and possesses much better working superiority for infiltration effectiveness in accuracy and efficiency; meanwhile, a high agreement between the adaptive predicted and actual measured values of infiltration effectiveness could be obtained. This novel intelligent prediction system has been promoted constructively to improve the quality uniformity of sprinkler irrigation and, consequently, to facilitate the productive management of sprinkler irrigated agriculture.

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Water Allocation and Integrative Management of Precision Irrigation: A Systematic Review

Cited by 27 publications

References 116 publications

Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield

Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield

Effect of Drip Irrigation on Soil Water Balance and Water Use Efficiency of Maize in Northwest China

Adaptive Prediction of Water Droplet Infiltration Effectiveness of Sprinkler Irrigation Using Regularized Sparse Autoencoder–Adaptive Network-Based Fuzzy Inference System (RSAE–ANFIS)

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