Hybrid Genetic Algorithm−Based BP Neural Network Models Optimize Estimation Performance of Reference Crop Evapotranspiration in China

Precise estimation of reference evapotranspiration (ET0) is of significant importance in hydrologic processes. In this study, a genetic algorithm (GA) optimized back propagation (BP) neural network model was developed to estimate ET0 using different combinations of meteorological data across various climatic zones and seasons in China. Fourteen climatic locations were selected to represent five major climates. Meteorological datasets in 2018–2020, including maximum, minimum and mean air temperature (Tmax, Tmin, Tmean, °C) and diurnal temperature range (∆T, °C), solar radiation (Ra, MJ m−2 d−1), sunshine duration (S, h), relative humidity (RH, %) and wind speed (U2, m s−1), were first subjected to correlation analysis to determine which variables were suitable as input parameters. Datasets in 2018 and 2019 were utilized for training the models, while datasets in 2020 were for testing. Coefficients of determination (r2) of 0.50 and 0.70 were adopted as threshold values for selection of correlated variables to run the models. Results showed that U2 had the least r2 with ET0, followed by ∆T. Tmax had the greatest r2 with ET0, followed by Tmean, Ra and Tmin. GA significantly improved the performance of BP models across different climatic zones, with the accuracy of GABP models significantly higher than that of BP models. GABP0.5 model (input variables based on r2 > 0.50) had the best ET0 estimation performance for different seasons and significantly reduced estimation errors, especially for autumn and winter seasons whose errors were larger with other BP and GABP models. GABP0.5 model using radiation/temperature data is highly recommended as a promising tool for modelling and predicting ET0 in various climatic locations

Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast

Accurate forecasting of reference crop evapotranspiration (ET0) is vital for sustainable water resource management. In this study, four popularly used single models were selected to forecast ET0 values, including support vector regression, Bayesian linear regression, ridge regression, and lasso regression models, respectively. They all had advantages of low requirement of data input and good capability of data fitting. However, forecast errors inevitably existed in those forecasting models due to data noise or overfitting. In order to improve the forecast accuracy of models, hybrid models were proposed to integrate the advantages of the single models. Before the construction of hybrid models, each single model’s weight was determined based on two weight determination methods, namely, the variance reciprocal and information entropy weighting methods. To validate the accuracy of the proposed hybrid models, 1–30 d forecast data from January 2 to February 1, 2022, were used as a test set in Xinxiang, North China Plain. The results confirmed the feasibility of the information entropy-based hybrid model. In detail, the information entropy model generated the mean absolute percentage errors of 11.9% or a decrease by 48.9% compared to the single and variance reciprocal hybrid models. Moreover, the model generated a correlation coefficient of 0.90 for 1–30 d ET0 forecasting or an increase by 13.6% compared to other models. The standard deviation and the root mean square error of the information entropy model were 1.65 mm·d−1 and 0.61 mm·d−1 or had a decrease by 16.4% and 23.7%. The maximum precision and the F1 score were 0.9618 and 0.9742 for the information entropy model. It was concluded that the information entropy-based hybrid model had the best midterm (1–30 d) ET0 forecasting performance in the North China Plain

Analysis of the Accuracy of an FDR Sensor in Soil Moisture Measurement under Laboratory and Field Conditions

Soil water content (SWC, % vol) is a key factor affecting plant growth and development. SWC measurement is vital to rational use of water resources for irrigation, and the accuracy of sensors in SWC measurement is of significant importance to smart data-driven irrigation. Here, a laboratory experiment and a field lysimetric experiment were conducted to evaluate the accuracy of Insentek sensors under various soil conditions (1.1 to 1.5 bulk densities and sand to clay soil textures) and irrigation levels (30, 45, and 60 mm), in 2018 and 2019. A microweighing lysimeter and oven-drying method were used as standard methods to compare the Insentek method. The root mean square error (RMSE, % vol) and relative prediction deviation (RPD) between the Insentek and microlysimetric SWC values were 0.89–1.04% vol and 5.6–6.8, respectively, under laboratory condition. The RPD value is larger than the threshold value of 4.0, indicating the accuracy of the Insentek sensors is reliable under laboratory condition. Except for 60 mm irrigation treatment, the RMSE between Insentek and the oven-drying method under field condition was 1.44–1.93% vol, and the RPD value was 1.56–1.93, lower than the threshold value of 4.0. The tiny gap between the Insentek sensor and soil may accelerate water infiltration along the probe 0-3 d after irrigation while increase air filling 5–7 d after irrigation, causing greater RMSE and lower RPD values. The dissatisfied performance in field condition may also be associated with the obvious drawbacks of oven-drying method, such as disturbance in soil sampling. When using oven-drying method to analyze the accuracy of the Insentek sensors in field condition, the concerns should be well addressed

A Review on Regulation of Irrigation Management on Wheat Physiology, Grain Yield, and Quality

Irrigation has been pivotal in sustaining wheat as a major food crop in the world and is increasingly important as an adaptation response to climate change. In the context of agricultural production responding to climate change, improved irrigation management plays a significant role in increasing water productivity (WP) and maintaining the sustainable development of water resources. Considering that wheat is a major crop cultivated in arid and semi-arid regions, which consumes high amounts of irrigation water, developing wheat irrigation management with high efficiency is urgently required. Both irrigation scheduling and irrigation methods intricately influence wheat physiology, affect plant growth and development, and regulate grain yield and quality. In this frame, this review aims to provide a critical analysis of the regulation mechanism of irrigation management on wheat physiology, plant growth and yield formation, and grain quality. Considering the key traits involved in wheat water uptake and utilization efficiency, we suggest a series of future perspectives that could enhance the irrigation efficiency of wheat

A Review on Regulation of Irrigation Management on Wheat Physiology, Grain Yield, and Quality

Irrigation has been pivotal in sustaining wheat as a major food crop in the world and is increasingly important as an adaptation response to climate change. In the context of agricultural production responding to climate change, improved irrigation management plays a significant role in increasing water productivity (WP) and maintaining the sustainable development of water resources. Considering that wheat is a major crop cultivated in arid and semi-arid regions, which consumes high amounts of irrigation water, developing wheat irrigation management with high efficiency is urgently required. Both irrigation scheduling and irrigation methods intricately influence wheat physiology, affect plant growth and development, and regulate grain yield and quality. In this frame, this review aims to provide a critical analysis of the regulation mechanism of irrigation management on wheat physiology, plant growth and yield formation, and grain quality. Considering the key traits involved in wheat water uptake and utilization efficiency, we suggest a series of future perspectives that could enhance the irrigation efficiency of wheat

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

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 0.322 for winter wheat, and 0.299 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

Impacts of Irrigation Time and Well Depths on Farmers’ Costs and Benefits in Maize Production

In the North China Plain, drought usually occurs during the interval between wheat harvest and maize sowing in normal and dry years. The first irrigation for maize plays a critical role in guaranteeing seed germination and grain yields. Using experimental data from Xinxiang in 2019 and survey data of 641 farmers from the North China Plain in 2020, this study adopts a cost-benefit analysis method to investigate the impacts of irrigation time and well depths on farmers’ costs and benefits in maize production. The results showed that farms with well depth > 120 m accounted for 49% of total farms, especially in Hebei Province, and 38% wells had low water yield 3 kW−1 h−1. Delaying the time of the first irrigation made maize yields decline by up to 307 kg ha−1 day−1. Well depths increased irrigation costs and total maize production cost in an exponential manner, causing farmers’ benefits to decrease exponentially with well depths. With well depth > 180 m, the proportion of irrigation cost to total cost rose to 14%, whereas well depth > 230 m directly caused the farmers’ profits negative. A critical well depth of 230 m was put forward as the upper limit for farmers adopting maize planting in the NCP. The concept of ‘rotational irrigation strategy’ and suggestions of adopting drip irrigation, sprinkler irrigation, or hose-reel sprinkler irrigation were recommended to advance 6–8 days for the first irrigation period, compared with traditional flood irrigation

Insentek Sensor: An Alternative to Estimate Daily Crop Evapotranspiration for Maize Plants

Estimation of ground-truth daily evapotranspiration (ETc) is very useful for developing sustainable water resource strategies, particularly in the North China Plain (NCP) with limited water supplies. Weighing lysimetry is a well-known approach for measuring actual ETc. Here, we introduced an alternative to lysimetry for ETc determination using Insentek sensors. A comparison experiment was conducted for maize plants at Xuchang Irrigation Experiment Station, in the NCP, in 2015 and 2016. Insentek ETc was evaluated using data on clear days and rainy days independently. We found that daily ETc increased gradually from VE (emergence) to VT (tasseling) stages, peaked at the R1 (silking) stage with the highest value of 7.8 mm·d−1, and then declined until maturity. On average, cumulative total of lysimetric ETc was 19% higher than that of Insentek ETc. The major depth of soil water extraction might be 60 cm for maize plants on lysimeters according to soil water depletion depth monitored by Insentek sensors. Daily ETc significantly related to soil water content (SWC) in topsoil (0–30 cm) in an exponential function (coefficients of determination (R2) = 0.32–0.53), and to precipitation (Pre) in a power function (R2 = 0.84–0.87). The combined SWC (0–30 cm)–Pre–ETc model may offer significant potential for accurate estimation of maize ETc in semi-humid environment of the NCP

Incorporation of Manure into Ridge and Furrow Planting System Boosts Yields of Maize by Optimizing Soil Moisture and Improving Photosynthesis

A sustainable management strategy of soil fertility and cropping system is critical to guaranteeing food security. However, little is known about the effects of soil amendment strategies on crop growth via regulating soil moisture and photosynthesis in a ridge and furrow cropping system. Here, field experiments were carried out in 2017 and 2018 in semi-arid areas of Loess Plateau, northwest China to investigate the effects of integrated use of ridge and furrow planting and manure amendment on grain yields of maize. Four treatments were designed: CK (flat planting with 100% chemical fertilizer), RFC (ridge and furrow planting with 100% chemical fertilizer), RFR (ridge and furrow planting with 100% control-released fertilizer), and RFM (ridge and furrow planting with 50% manure fertilizer + 50% N fertilizer). On average, RFM increased photosynthetic rates (Pn) by 74%, followed by RFR by 47%, and RFC by 26%, compared to CK. Also, stomatal conductance (Cd), transpiration rates (Tr), and intercellular CO2 concentration (Ci) were highest with RFM, followed by RFR and RFC. Averaged across the two years, RFM conserved 10% more soil water storage (SWS) than CK did at harvest, followed by RFR with an increment by 8%. However, RFC consumed more soil water than CK did, with its ETc 8% higher than CK. Consequently, spring maize treated with RFM suffered less drought stress, especially in 2017 when precipitation was insufficient. On average, grain yields and water use efficiency of RFM were increased by 18% and 27%, compared to CK. Structural equation modeling analysis showed that there existed significant positive correlation between SWS in top layers and grain yields, while SWS in deep layers had negative effects on grain yields. In conclusion, the incorporation of manure into ridge and furrow planting system can be an efficient agronomic practice to improve plant photosynthesis, optimize soil moisture, and boost grain yields in semi-arid areas of Loess Plateau, northwest China

The Effects of Foliar Supplementation of Silicon on Physiological and Biochemical Responses of Winter Wheat to Drought Stress during Different Growth Stages

Drought is one of the major environmental stresses, resulting in serious yield reductions in wheat production. Silicon (Si) has been considered beneficial to enhancing wheat resistance to drought stress. However, few studies have explored the mediated effects of foliar supplementation of Si on drought stress imposed at different wheat growth stages. Therefore, a field experiment was carried out to investigate the effects of Si supplementation on the physiological and biochemical responses of wheat to drought stress imposed at the jointing (D-jointing), anthesis (D-anthesis) and filling (D-filling) stages. Our results showed that a moderate water deficit markedly decreased the dry matter accumulation, leaf relative water content (LRWC), photosynthetic rate (Pn), stomatal conductance (Sc), transpiration rate (Tr) and antioxidant activity [peroxidase (POD), superoxide dismutase (SOD) and catalase (CAT)]. On the contrary, it remarkably increased the content of osmolytes (proline, soluble sugar, soluble protein) and lipid peroxidation. The grain yields of D-jointing, D-anthesis and D-filling treatments were 9.59%, 13.9% and 18.9% lower, respectively, compared to the control treatment (CK). However, foliar supplementation of Si at the anthesis and filling stages significantly improved plant growth under drought stress due to the increased Si content. Consequently, the improvement in antioxidant activity and soluble sugar, and the reduction in the content of ROS, increased the LRWC, chlorophyll content, Pn, Sc and Tr, and ultimately boosted wheat yield by 5.71% and 8.9%, respectively, in comparison with the non-Si-treated plants subjected to water stress at the anthesis and filling stages. However, the mitigating effect of Si application was not significant at the jointing stage. It was concluded that foliar supplementation of Si, especially at the reproductive stage, was effective in alleviating drought-induced yield reduction