Silicon (Si) enhances maize resistance to drought. While previous studies have mainly focused on the seedling stage, the mediation of drought stress by Si imposed at the vegetative and reproductive stages has been rarely investigated. A soil-column experiment was thus conducted under a rainproof shelter to quantify the effect s of Si application on the physiological and agronomic responses of maize to drought stress imposed at the 6-leaf (D-V6), 12-leaf (D-V12), and blister (D-R2) stages. The observed parameters included plant growth, photosynthesis, osmolytes, antioxidant activity, and grain yield. The results showed that drought stress strongly decreased the leaf area, leaf water content, photosynthetic rate, chlorophyll content, and antioxidant activity (superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT)) and markedly increased lipid peroxidation. D-V6, D-V12, and D-R2 decreased grain yields by 12.9%, 28.9%, and 44.8%, respectively, compared to the well-watered treatment (CK). However, Si application markedly increased leaf area, chlorophyll content, photosynthetic rate, osmolyte content, and enzymatic antioxidant activities (SOD, POD, and CAT), and decreased malondialdehyde (MDA) and superoxide radical accumulation, ultimately improving maize yields by 12.4%, 69.8%, and 80.8%, respectively, compared to the non-Si treated plants under drought stress at the V6, V12, and R2 stages. Furthermore, maize yields had a significant positive correlation with chlorophyll content and SOD and POD activity during the three stages. Our findings suggest that Si-induced changes in chlorophyll content and antioxidant activity might constitute important mechanisms for mitigating drought stress. In conclusion, this study provides physico-biochemical evidence for the beneficial role of Si in alleviating drought-induced yield reduction in maize, particularly during the late vegetative or early reproductive stages. Thus, Si application constitutes an effective approach for improving maize yield in rain-fed agricultural systems.

The equivalent water thickness (EWT) is an important biophysical indicator of water status in crops. The effective monitoring of EWT in wheat under different nitrogen and water treatments is important for irrigation management in precision agriculture. This study aimed to investigate the performances of machine learning (ML) algorithms in retrieving wheat EWT. For this purpose, a rain shelter experiment (Exp. 1) with four irrigation quantities (0, 120, 240, 360 mm) and two nitrogen levels (75 and 255 kg N/ha), and field experiments (Exps. 2–3) with the same irrigation and rainfall water levels (360 mm) but different nitrogen levels (varying from 75 to 255 kg N/ha) were conducted in the North China Plain. The canopy reflectance was measured for all plots at 30 m using an unmanned aerial vehicle (UAV)-mounted multispectral camera. Destructive sampling was conducted immediately after the UAV flights to measure total fresh and dry weight. Deep Neural Network (DNN) is a special type of neural network, which has shown performance in regression analysis is compared with other machine learning (ML) models. A feature selection (FS) algorithm named the decision tree (DT) was used as the automatic relevance determination method to obtain the relative relevance of 5 out of 67 vegetation indices (Vis), which were used for estimating EWT. The selected VIs were used to estimate EWT using multiple linear regression (MLR), deep neural network multilayer perceptron (DNN-MLP), artificial neural networks multilayer perceptron (ANN-MLP), boosted tree regression (BRT), and support vector machines (SVMs). The results show that the DNN-MLP with R² = 0.934, NSE = 0.933, RMSE = 0.028 g/cm², and MAE of 0.017 g/cm² outperformed other ML algorithms (ANN-MPL, BRT, and SVM- Polynomial) owing to its high capacity for estimating EWT as compared to other ML methods. Our findings support the conclusion that ML can potentially be applied in combination with VIs for retrieving EWT. Despite the complexity of the ML models, the EWT map should help farmers by improving the real-time irrigation efficiency of wheat by quantifying field water content and addressing variability.

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 (T_c), 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 (T_c − T_a) (r² > 0.695), and between crop evapotranspiration (ET_c) and T_c (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 T_c − T_a 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.

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 ET_c 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.

Estimation of ground-truth daily evapotranspiration (ET_c) 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 ET_c. Here, we introduced an alternative to lysimetry for ET_c 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 ET_c was evaluated using data on clear days and rainy days independently. We found that daily ET_c 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 ET_c was 19% higher than that of Insentek ET_c. 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 ET_c 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–ET_c model may offer significant potential for accurate estimation of maize ET_c in semi-humid environment of the NCP.

Summer maize crop development, yield, and water use characteristics under water deficit conditions at different growth stages were investigated in this study using different irrigation regime treatments at the seedling (S), jointing (J), tasseling (T), and grain filling stages (F) in 2018 and 2019 in China. Ten different irrigation treatments were set, including three-irrigation application intervals (JTFi, STFi, SJFi, SJTi), two-irrigation applications (STi, JTi, JFi), and single-irrigation applications (Ti, Ji). These were compared to the control treatment (CK), which had sufficient irrigation provided at four intervals (SJTFi). The results showed that compared to CK, a water deficit at the seedling and jointing stages had a greater effect on plant height, whereas a water deficit at the tasseling and filling stages had a greater effect on the leaf area index, and a continuous water deficit had an effect on the stem diameter of summer maize. Limitations in terms of the growth and development of summer maize increased with less frequent irrigation. As irrigation decreased, the grain yield decreased, and the water use efficiency increased, and a water deficit at the tasseling stage had the greatest effect on the yield and water use efficiency. The JTFi treatment was the optimal irrigation regime with a yield decline, and its water consumption was reduced by 16.9% (p < 0.05) on average. However, compared to CK, the water use efficiency of the JTFi treatment increased by 17.3% (p < 0.05). Moreover, the JTFi treatment had the smallest maize yield response factor value (K_y) of 0.16, and its comprehensive score was the second highest after CK.