Seasonal droughts and extreme weather events are threatening citrus production in south China. Investigating the effect of deficit irrigation (DI) on leaf physiology, fruit growth, yield and crop water productivity (WPc) is significantly important for the sustainable development of citrus industry. In this study, a full irrigation treatment (CK) and 16 DI treatments were designed including the low (LD, 85 %CK), mild (M1D, 70 %CK), moderate (M2D, 55 %CK) and severe (SD, 40 %CK) DI treatments at bud bust to flowering stage (I), young fruit stage (II), fruit expansion stage (III) and fruit maturation stage (IV), respectively. Compared with CK, DI treatments at stage I-IV raised the hydrogen peroxide content by 14.4 %-76.6 %, except for LD treatment. Meanwhile, the activities of superoxide dismutase, peroxidase, catalase and the content of proline also increased by 10.5 %-47.3 %, 24.9 %-77.4 %, 20.2-49.8 % and 10.1 %-39.0 %, respectively, which allowed crop to cope with DI-induced oxidative stress. When stomatal conductance (Gs) at stage I-IV reached 0.030-0.040, 0.074-0.096, 0.204-0.219, and 0.114-0.142 mmol center dot m- 2 center dot s- 1, respectively, leaf net photosynthesis rate (Pn) did not significantly change, but transpiration rate was limited, and hereby enhanced instantaneous water use efficiency. In addition, although DI treatments at all stages reduced Pn, they did not always have a negative impact on yield due to the obvious improvement of leaf photosynthesis and fruit growth after re-irrigation. Specifically, re-irrigation after IM1D, II-M1D and III-LD treatments increased the fruit growth rate at stages II, III and IV, respectively, which could further maintain or even enhance the yield, and improve WPcby 5.6 %-7.0 %, 5.7 %-8.6 % and 3.4 %4.7 %, respectively. IV-M2D treatment increased WPcby 13.7 %-14.5 %. In summary, DI treatment could regulate Gs and fruit compensatory growth after re-irrigation, respectively, so as to achieving water saving and high yield of citrus. I-M1D, II-M1D, III-LD and IV-M2D treatments was recommended as the suitable deficit drip irrigation pattern to ensure efficient citrus production.

Accurate and real -time monitoring of soil moisture content (SMC) is of utmost importance for effective field irrigation and maximizing crop water productivity. However, a comprehensive investigation into the inversion study for determining suitable combinations of unmanned aerial vehicle (UAV) image features and enhancing the precision of SMC model prediction has yet to be fully validated within a kiwifruit orchard setting. This study addresses this gap by employing a pre-processing method and an optimal band combination algorithm to assess the impact of various combinations of kiwifruit canopy reflectance and fraction vegetation coverage (FVC) features on the sensitivity of root-zone SMC. Furthermore, an optimal ensemble learning (EL) framework was developed to monitor SMC at various root-zone depths (0-10 cm [SMC10], 0-20 cm [SMC20], 0-30 cm [SMC30], 0-40 cm [SMC40], 0-50 cm [SMC50], 0-60 cm [SMC60]). The key findings of this research highlight the successful derivation of 10 wavebands and FVC features, exhibiting a strong correlation with SMC at different root depths. The gradient boosting (GBDT) model demonstrated the exceptional accuracy in estimating SMC10, with an impressive R 2 value of 0.963 +/- 0.030 and low RMSE values of 0.238 +/- 0.111. Similarly, the eXtreme Gradient Boosting (XGBoost) model outperformed in estimating SMC20 to SMC60, with R 2 and RMSE values of 0.963 +/- 0.024 and 0.117 +/- 0.053, respectively. Additionally, the utilization of the optimal EL model allows for digital mapping of SMC at different depths across fruit growth stages, showcasing superior adaptability for SMC30 to SMC60 (with R 2 and RMSE of 0.782 +/- 0.090 and 0.037 +/- 0.011) compared to SMC10 and SMC20 (with R 2 and RMSE of 0.765 +/- 0.097 and 0.056 +/- 0.024). These results underscore the potential of the EL estimation framework in characterizing the spatial distribution of root-zone SMC at the individual kiwifruit plant level.

Accurately estimating of soil moisture content (SMC) is essential for effective irrigation water management and optimizing plant water productivity. Recent advancements in multi-sensor platforms and ensemble learning (EL) algorithms such as the use of UAV-based multispectral (MS) and thermal infrared (TIR) images, have enabled precise mapping of SMC at the field level. The purpose of this study is to optimize the multi-dimensional (n-D) index set and EL model to achieve accurate inversion mapping of SMC in the 0-60 cm root zone at a kiwifruit orchard during the fruit growth period (May-Sep 2022). The n-D index set was computed using kiwifruit canopy MS and TIR data from UAV along with in-field temperature data (air temperature [Ta], kiwifruit canopy temperature [Tc], soil temperature [Ts], and Ta-Tc, Ta-Ts, Tc-Ts). The primary findings of this study are as follows: (1) The Three-Dimensional Drought Index (TDDI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) demonstrated superior performance in capturing temporal variations of root zone SMC compared to Normalized Difference Vegetation Index (NDVI) and Enhance Vegetation Index (EVI). (2) The optimal Categorical Boosting (Catboost) model with two optimal TDDIs (TDDI22: (Tc-Ts)-EVI10,6,2-CWSI space and TDDI7: Tc-NDVI7,3-CWSI space), exhibited exceptional performance for SMC estimation, with determination coefficient (R-2) and root-mean-square error (RMSE) of 0.966 +/- 0.010 and 0.046 +/- 0.003%, respectively. (3) The planted-by-planted mapping performed well in estimating root zone SMC at different growth stages and under different irrigation treatments, with a correlation coefficient (R) of 0.936 +/- 0.074 (p < 0.001). This study demonstrated that the integration of multi-sensor indicators and EL models can improve the accuracy of SMC estimation due to its robust adaptability to various field conditions. Furthermore, the planted-grid-based SMC mapping framework can be considered as a valuable approach for monitoring dynamic SMC, facilitating informed irrigation decisions at the individual planting grid.

Precision irrigation management is the key to saving water, improving fruit quality and increasing yield of citrus. In this study, six water-yield models and three water-fruit quality models were proposed based on 4-year field data. Then, six scenarios were set with crop total available water (CTW) from 550 to 800 mm at intervals of 50 mm, and a simulation optimization model coupling water-yield, water-fruit quality models and NSGA-II was developed to optimize water allocation strategies. The results showed that six water-yield models performed well in predicting citrus yield, especially Minhas model (R2=0.81). Three water-fruit quality models could well predict the physical quality of citrus fruit (R2=0.72-0.92), but only the Q-Rao model could accurately predict the chemical quality due to its development considering the response processes of fruit quality to deficit irrigation. Therefore, Minhas and Q-Rao models were recommended to predict citrus yield and fruit quality, respectively. The optimization results showed that the optimal water allocation strategy under CTW= 630 mm produced an acceptable yield while improving fruit chemical quality and water use efficiency. When CTW was greater than 630 mm, the optimal water allocation strategy had little difference. Therefore, the optimal water allocation strategy under CTW= 630 mm, which was 14, 104, 325, and 187 mm at bud bust to flowering stage, young fruit stage, fruit expansion stage, and fruit maturation stage, respectively, was recommended to be used under suf-ficient water resources conditions (CTW & GE; 630 mm). When CTW was between 550 and 630 mm, the optimal water allocation strategy changed. As a result, the optimal water allocation strategy was chosen based on the findings as well as the actual local CTW under limited water resource conditions (CTW = 550-630 mm). The findings of this study will be useful in developing appropriate irrigation strategies in Southwest China, to achieve efficient and sustainable citrus production.

Accurate estimation of maize evapotranspiration (ET) is of great significance for the improvement of crop water use efficiency and precision irrigation. The Penman-Monteith model (P-M) has been widely used to simulate crop ET. In the P-M model, the estimation accuracy of canopy resistance (r(c)) has a direct impact on ET. In this study, based on the eddy covariance system, large-scale lysimeter and meteorological station data from three sites (Yucheng, Yangling and Shangqiu) in semi-humid regions of northern China, the P-M model was applied to obtain canopy resistance (r(c)-(PM)) and correlation significances between r(c-PM )and different impact factors (R-n net radiation, T temperature, VPD saturated vapor pressure difference, theta soil moisture content, LAI leaf area index) were analysed. The whole growth period of maize was divided according to different LAI thresholds (0.1, 0.5, 1.0, 1.5, 2.0 and 3.0 m(2)m(- 2)). The Genetic Algorithms (GA) and Differential Evolution (DE) algorithms were used to optimize the empirical parameters of the Jarvis model, and the P-M model was applied to estimate ET under different LAI thresholds at the three stations. The correlation significances of r(c-PM) with different influencing factors followed the order R-n > LAI > theta > VPD > T, and it was extremely significant with Rn (P < 0.01) and significant with LAI and theta (P < 0.05). The GA and DE algorithm optimization results showed that the calculation accuracy of r(c) was highest when LAI=0.5 m(2) m(- 2 )at Yucheng station, with R(2 )of 0.80 and 0.81, respectively, and when LAI=1.0 m(2) m(-2), and the accuracy of r(c) was highest at Yangling station, with R-2 of 0.87 and 0.89, respectively, and when LAI = 1.0 m(2) m( -2), and the accuracy of r(c) was highest at Shangqiu station, with R-2 of 0.84 and 0.84, respectively. Combined with P-M model to calculate maize ET under different LAI thresholds, the simulation accuracy of ET was best when LAI = 0.5 m(2 )m(-2 )at Yucheng station, with averege R-2 of 0.85, the order of simulation ET accuracy was: 0.5 > 1.0 > 1.5 > 2.0 > 3.0 > 0.1 m(2) m(-2). When LAI = 1.0 m(2 )m(-2), and the accuracy of maize ET was highest at Yangling and Shangqiu stations, with averege R-2 of 0.83 and 0.85, respectively, the order of simulation ET accuracy was: 1.0 > 0.5 > 1.5 > 2.0 > 3.0 > 0.1 m(2) m-( 2). ET accuracy calculated by the DE optimization algorithm was better than that of the GA optimization algorithm, with R-2 of 0.40-0.84 and 0.58-0.86, respectively. This study suggests that the algorithm is of great importance to optimize the empirical parameters of the Jarvis model, of which DE optimization algorithm is recommended to simulate maize ET in semi-humid regions of northern China.

Ecosystem light use efficiency (ELUE) is generally defined as the ratio of gross primarily productivity (GPP) to photosynthetically active radiation (PAR), which is an important ecological indictor used in dry matter prediction. Herein, investigating the dynamics of ELUE and its controlling factors is of great significance for simulating ecosystem photosynthetic production. Using 35 site-years eddy covariance fluxes and meteorological data collected at 11 cropland sites globally, we investigated the dynamics of ELUE and its controlling factors in four agroecosystems with paddy rice, soybean, summer maize and winter wheat. A “U” diurnal pattern of hourly ELUE was found in all the fields, and daily ELUE varied with crop growth. The ELUE for the growing season of summer maize was highest with 0.92 ± 0.06 g C MJ−1, followed by soybean (0.80 ± 0.16 g C MJ−1), paddy rice (0.77 ± 0.24 g C MJ−1) and winter wheat (0.72 ± 0.06 g C MJ−1). Correlation analysis showed that ELUE positively correlated with air temperature (Ta), normalized difference vegetation index (NDVI), evaporative fraction (EF) and canopy conductance (gc, except for paddy rice sites), while it negatively correlated with the vapor water deficit (VPD). Besides, ELUE decreased in the days after a precipitation event during the active growing seasons. The path analysis revealed that the controlling variables considered in this study can account for 73.7%, 85.3%, 75.3% and 65.5% of the total ELUE variation in the rice, soybean, maize and winter wheat fields, respectively. NDVI is the most confident estimators for ELUE in the four ecosystems. Water availability plays a secondary role controlling ELUE, and the vegetation productivity is more constrained by water availability than Ta in summer maize, soybean and winter wheat. The results can help us better understand the interactive influences of environmental and biophysical factors on ELUE.

Accurate prediction of reference crop evapotranspiration (ET0) is important for regional water resources management and optimal design of agricultural irrigation system. In this study, three hybrid models (PSO-ELM, GA-ELM and ABC-ELM) integrating the extreme learning machine model (ELM) with three biological heuristic algorithms, i.e., PSO, GA and ABC, were proposed for predicting daily ET0 based on daily meteorological data from 2000 to 2019 at twelve representative stations in different climatic zones of China. The performances of the three hybrid ELM models were further compared with the standalone ELM model and three empirical models (Hargreaves, Priestley-Talor and Makkink models). The results showed that the hybrid ELM models (R-2 = 0.973-0.999) all performed better than the standalone ELM model (R-2 = 0.955-0.989) in four climatic regions in China. The estimation accuracy of the empirical models was relatively lower, with R-2 of 0.822-0.887 and RMSE of 0.381-1.951 mm/d. The R-2 values of PSO-ELM, GA-ELM and ABC-ELM models were 0.993, 0.986 and 0.981 and the RMSE values were 0.266 mm/d, 0.306 mm/d and 0.404 mm/d, respectively, indicating that the PSO-ELM model had the best performance. When setting T-max, T-min, and RH as the model inputs, the PSO-ELM model presented better performance in the temperate continental zone (TCZ), subtropical monsoon region (SMZ) and temperate monsoon zone (TMZ) climate zones, with R-2 of 0.892, 0866 and 0.870 and RMSE of 0.773 mm/d, 0.597 mm/d and 0.832 mm/d, respectively. The PSO-ELM model also performed in the mountain plateau region (MPZ) when only T-max and T-min, data were available, with R-2 of 0.808 and RMSE of 0.651 mm/d. All the three biological heuristic algorithms effectively improved the performance of the ELM model. Particularly, the PSO-ELM was recommended as a promising model realizing the high-precision estimation of daily ET0 with fewer meteorological parameters in different climatic zones of China.

Reference crop evapotranspiration (ETo) is a determinant factor in agricultural water resource management. Therefore, accurate ETo information is critical to quantify crop water requirements for precision agriculture management. This study coupled bio-inspired optimization algorithms with artificial neural network (ANN), i.e., ANN with bat algorithm (BA-ANN), ANN with cuckoo search algorithm (CSA-ANN), and ANN with whale optimization algorithm (WOA-ANN), and developed three hybrid ANN models for daily ETo modeling with limited inputs. The models were trained and evaluated using a k-fold test approach and long-term daily climatic data from 2001 to 2018 at six climatic stations in the Loess Plateau of north China. Three input scenarios were used, including temperature-based inputs, radiation-based inputs, and mass transfer-based inputs. The statistical comparison showed that the hybrid WOA-ANN offered better estimates than BA-ANN and CSA-ANN in all three input scenarios. In general, the radiation-based WOA-ANN provided the most accurate ETo estimations, with regional average relative root mean square error and Nash-Sutcliffe efficiency coefficient of 13.3% and 0.959, respectively. The temperature-based WOA-ANN offered acceptable and reasonable ETo estimates. Thus, it is a reliable tool for ETo modeling, given that air temperature is available in many regions. Overall, the bio-inspired optimization algorithms are robust tools for enhancing ANN performance in ETo simulation, and thus they are highly recommended to estimate ETo in the study region. Our study proposed powerful models for accurately estimating ETo with limited inputs, offering practical implications for the development of precision agriculture.