Search Results (25)

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

This study investigates the effects of γ-aminobutyric acid (GABA) and proline, both individually and in combination, on the growth of oilseed rape under drought stress and following the resumption of irrigation. The goal was to determine whether the exogenous application of these compounds enhances the plants response to prolonged water deficit and, if so, to identify the biochemical processes involved in the plant tissue. The experiment was conducted under controlled laboratory conditions. After 21 days of plant cultivation, at the 3–4 leaf stage, seedlings were sprayed with aqueous solutions of GABA (0.1 mM) and proline (0.1 mM). The plants were then subjected to 8 days of severe drought stress, after which irrigation was resumed, and recovery was assessed over 4 days. The results showed that both amino acids alleviated the drought-induced stress as indicated by higher relative water content (RWC), increased levels of endogenous proline and photosynthetic pigments in leaves, and enhanced survival and growth recovery after drought. GABA-treated plants maintained membrane integrity and preserved plasma membrane (PM) ATPase activity during prolonged drought stress while reducing ethylene, H₂O₂, and MDA levels. Proline also influenced these biochemical responses, though to a lesser extent. The combination of GABA and proline facilitated better recovery of oilseed rape compared to the drought control group following rewatering. Notably, GABA treatment resulted in a significant increase in gene expression compared to the untreated control. Molecular analysis of drought-responsive genes revealed that the gene expression in plants treated with both proline and GABA was typically intermediate between those treated with proline alone and those treated with GABA alone. Based on these findings, we propose that GABA application could serve as an alternative to proline for improving oilseed rape’s drought tolerance, potentially increasing both crop yield and quality. Full article

The leaf area index (LAI) and leaf chlorophyll content (LCC) are key indicators of crop photosynthetic efficiency and nitrogen status. This study explores the integration of UAV-based multispectral (MS) and thermal infrared (TIR) data to improve the estimation of maize LAI and LCC across different growth stages, aiming to enhance nitrogen (N) management. In field trials from 2022 to 2023, UAVs captured canopy images of maize under varied water and nitrogen treatments, while the LAI and LCC were measured. Estimation models, including partial least squares regression (PLS), convolutional neural networks (CNNs), and random forest (RF), were developed using spectral, thermal, and textural data. The results showed that MS data (spectral and textural features) had strong correlations with the LAI and LCC, and CNN models yielded accurate estimates (LAI: R² = 0.61–0.79, RMSE = 0.02–0.38; LCC: R² = 0.63–0.78, RMSE = 2.24–0.39 μg/cm²). Thermal data reflected maize growth but had limitations in estimating the LAI and LCC. Combining MS and TIR data significantly improved the estimation accuracy, increasing R² values for the LAI and LCC by up to 23.06% and 19.01%, respectively. Nitrogen dilution curves using estimated LAIs effectively diagnosed crop N status. Deficit irrigation reduced the N uptake, intensifying the N deficiency, while proper water and N management enhanced the LAI and LCC. Full article

In arid areas, water shortage has become a major bottleneck limiting the sustainable development of agriculture, necessitating improved water use efficiency and the full development of innovative water-saving irrigation management technologies to improve quality. In the present study, tomato (Solanum lycopersicum cv. Micro Tom) fruits were used as materials, and different irrigation frequencies were set during the fruit expansion stage. The normal treatment (CK) was irrigated every three days, while the water deficit treatments were irrigated at varying frequencies: once every 4 days (T1), 5 days (T2), 6 days (T3), 7 days (T4), and 8 days (T5). These corresponded to 80%, 70%, 60%, 50%, and 40% of the maximum field moisture capacity (FMC), respectively, with CK maintaining full irrigation at 90% of the maximum FMC. The water deficit treatment T3, with less stress damage to plants and the most significant effect on fruit quality improvement, was selected based on plant growth indices, photosynthetic characteristics, chlorophyll fluorescence parameters, and fruit quality indices, and its effects on carotenoids, glycolic acid fractions, and volatile compounds during tomato fruit ripening were further investigated. The outcome indicated that moderate water deficit significantly increased the carotenoid components of the tomato fruits, and their lycopene, lutein, α-carotene, and β-carotene contents increased by 11.85%, 12.28%, 20.87%, and 63.89%, respectively, compared with the control fruits at the ripening stage. The contents of glucose and fructose increased with the development and ripening of the tomato fruits, and reached their maximum at the ripening stage. Compared to the control treatment, the moderate water deficit treatment significantly increased the glucose and fructose levels during ripening by 86.70% and 19.83%, respectively. Compared to the control conditions, water deficit conditions reduced the sucrose content in the tomato fruits by 27.14%, 18.03%, and 18.42% at the mature green, turning, and ripening stages, respectively. The moderate water deficit treatment significantly increased the contents of tartaric acid, malic acid, shikimic acid, alpha ketoglutaric acid, succinic acid, and ascorbic acid, and decreased the contents of oxalic acid and citric acid compared to the control. The contents of total soluble sugar and total organic acid and the sugar–acid ratio were significantly increased by 48.69%, 3.71%, and 43.09%, respectively, compared with the control at the ripening stage. The moderate water deficit treatment increased the fruit response values to each sensor of the electronic nose, especially W5S, which was increased by 28.40% compared to the control at the ripening stage. In conclusion, during the ripening process of tomato fruit, its nutritional quality and flavor quality contents can be significantly improved under moderate (MD) deficit irrigation treatment. The results of this experiment can lay the foundation for the research on the mechanism of water deficit aiming to promote the quality of tomato fruit, and, at the same time, provide a theoretical basis and reference for tomato water conservation and high-quality cultivation. Full article

Addressing agricultural water scarcity poses a current challenge of growing concern, exacerbated by climate change. This is particularly relevant for stone fruit trees, such as apricot, cultivated in semi-arid zones, where regulated deficit irrigation (RDI) strategies are gaining attention to tackle the challenge. The RDI method involves optimizing various factors based on how the plant responds physiologically to indicators of its water needs. Among these indicators, water potential is considered the most reliable and influential measure. For numerous apricot varieties and diverse geographic locations, research consistently shows that implementing water reduction strategies during non-critical developmental stages of floral bud development or fruit growth does not significantly impact crop yield. However, it does lead to reduced vegetative growth, which could offer additional benefits in crop management. Furthermore, the implementation of RDI strategies leads to advantageous improvements in fruit quality, particularly storage capacity and morphometric and chemical fruit characteristics, such as total soluble solids content. This scoping review study suggests that RDI is a feasible strategy to address water scarcity in apricot cultivation; however, further studies focused on continuous water monitoring alternatives are necessary to optimize RDI techniques. Future research should prioritize optimizing RDI for different growth stages, exploring advanced technologies for precise implementation, and assessing environmental impacts, while addressing research gaps including the influence of climate variability and the interaction with other agronomic practices, to refine RDI strategies and enhance apricot orchard sustainability and productivity. Full article

With the changing global climate, drought stress will pose a considerable challenge to the sustainable development of agriculture in arid regions. The objective of this study was to explore the resistance and water demand of cotton plants to water stress during the flowering and boll setting stage. The experimental plot was in Huaxing Farm of Changji city. The plots were irrigated, respectively, at 100% (as the control), 90%, 85% and 80% of the general irrigation amount in the local area. The relationship between the various measured indexes and final yield under different deficit irrigation (DI) treatments was studied. The results showed that deficit irrigation impacted the growth and development processes of cotton during the flowering and boll setting stage. There was a high negative correlation (R² > 0.95) between the maximum leaf area index and yield. Similarly, there was a high correlation between malondialdehyde content and yield. Meanwhile, 90% of the local cotton irrigation contributed to water saving and even increasing cotton yield. Furthermore, based on the results, the study made an initial optimization to the local irrigation scheme by utilizing the DSSAT model. It was found that changing the irrigation interval to 12 days during the stage could further enhance cotton yield and conserve resources. Full article

The scarcity of water resources affects tomato production. Deficit irrigation may optimize water management with only a low reduction in yield. Deficit irrigation scheduling based on applied water presented no clear conclusions. Water stress management based on plant water status, such as water potential, could improve the scheduling. The aim of this work was to evaluate the physiological and yield responses of different tomato cultivars to deficit irrigation. Three experiments were carried out in 2020 and 2022 at the University of Seville (Spain). “Cherry” and “chocolate Marmande” cultivars with an indeterminate growth pattern were grown in a greenhouse. Treatments were: Control (full irrigated) and Deficit. Deficit plants were irrigated based on water potential measurements. Moderate water stress did not significantly reduce the yield, although it affected other processes. Fruit size and total soluble solids were the most sensitive parameters to water stress. The latter increased only when persistent water stress was applied. However, truss development and fruit number were not affected by the level of water stress imposed. Such results suggest that moderate water stress, even in sensitive phenological stages such as flowering, would not reduce yield. Deficit irrigation scheduling based on plant water status will allow accurate management of water stress. Full article

Drought stress disrupts the molecular-level water balance in plants, and severe water deficiency can be fatal for cotton plants. However, mild water deficits or short-term drought stress may enhance crop resilience, increasing yields. The present study aims to determine the optimal watering time and irrigation amount to induce drought tolerance in cotton seedlings during drought training. Specifically, the investigation focuses on identifying the ideal day for watering and the corresponding irrigation volume that effectively triggers the transition of cotton plants into a state of enhanced resistance to drought stress during the seedling stage. In this study, the CSM-CROPGRO-Cotton model was utilized, and our objectives were to (i) evaluate the predictive capability of CSM-CROPGRO-Cotton for yield estimation in field experiments in Xinjiang and (ii) simulate and assess the range of time during the seedling stage when cotton plants can withstand drought stress without reducing yields, identifying irrigation strategies that induce drought training while maintaining yield under mild water deficiency. The model was validated using yield data from field experiments conducted in 2023. The validation criteria included a normalized root mean square error $\left(nRMSE\right)>10\%$ and a coefficient of determination $\left(r^2\right)>85\%$ for yield; for the leaf area index (LAI), the criterion was $\left(r^2\right)>90\%$, with a degree of agreement of $\left(d\right)>75\%$. The results demonstrated the accuracy of the CSM-CROPGRO-Cotton model in predicting cotton yield. Based on the validated CSM-CROPGRO-Cotton model, this study employed the LINUX crop model batch-processing technique to efficiently simulate 357 different irrigation strategies by adjusting the amount of “first irrigation” and timing. The findings revealed that in the irrigation scheme for cotton during the seedling stage, when the amount of first irrigation was in the lower range of 10 mm to 15 mm, the cotton plants underwent drought training during the early growth stage, and their yields did not exhibit drastic fluctuations due to reduced amounts of first irrigation. The suitable period for first irrigation for drought training was from 25 June to 6 July, and the amount of first irrigation could save approximately 57.14% in irrigation water. This implies that subjecting cotton plants to a certain level of drought training can enhance their stress tolerance and increase yields. This finding holds great significance for cotton cultivation in drought-prone regions. Full article

Accurate estimation and effective portioning of actual evapotranspiration (${\mathrm{ET}}_{\mathrm{a}}$) into soil evaporation (E) and plant transpiration (T) are important for increasing water use efficiency (WUE) and optimizing irrigation schedules in croplands. In this study, E/T partitioning was performed on ${\mathrm{ET}}_{\mathrm{a}}$ rates measured using the eddy covariance (EC) technique in three winter wheat growing seasons from October 2020 to June 2023. The variation in the crop coefficients (${\mathrm{K}}_{\mathrm{c}}$, α, and ${\mathrm{K}}_{\mathrm{Hc}}$) were quantified by combining the ${\mathrm{ET}}_{\mathrm{a}}$ and reference evapotranspiration rates using the Penman–Monteith, Priestley–Taylor, and Hargreaves equations. In addition, the application of models based on the modified crop coefficient (${\mathrm{K}}_{\mathrm{c}}$, α, and ${\mathrm{K}}_{\mathrm{Hc}}$) was proposed to estimate the ${\mathrm{ET}}_{\mathrm{a}}$ rates. According to the obtained results, the average cumulative ${\mathrm{ET}}_{\mathrm{a}}$, T, and E rates in the three winter wheat growth seasons were 471.4, 265.2, and 206.3 mm, respectively. The average T/${\mathrm{ET}}_{\mathrm{a}}$ ratio ranged from 0.16 to 0.72 at the different winter wheat growth stages. Vapor pressure deficit (VPD) affected the ${\mathrm{ET}}_{\mathrm{a}}$ rates at a threshold of 1.27 KPa. The average ${\mathrm{K}}_{\mathrm{c}}$, α, and ${\mathrm{K}}_{\mathrm{Hc}}$ values in the middle stage were 1.34, 1.54, and 1.21, respectively. The measured ${\mathrm{ET}}_{\mathrm{a}}$ rates and ${\mathrm{ET}}_{\mathrm{a}}$ rates estimated using the adjusted ${\mathrm{K}}_{\mathrm{c}}$, α, and ${\mathrm{K}}_{\mathrm{Hc}}$ showed regression slope coefficients of 0.96, 0.99, and 0.96, and coefficients of determination (R²) of 0.92, 0.93, and 0.90, respectively. Therefore, the Priestley–Taylor-equation-based adjusted crop coefficient is recommended. The adjusted crop-coefficient-based models can be used as valuable tools for local policymakers to effectively improve water use. Full article

The adaptive potential of plants is commonly used as an indicator of genotypes with higher breeding program potential. However, the complexity and interaction of plant metabolic parameters pose a challenge to selection strategies. In this context, this study aimed to explore phenotypic plasticity within the germplasm of Hybrid Timor coffee. Additionally, we assessed the utility of the multivariate phenotypic plasticity index (MVPi) as a promising tool to predict genotype performance across diverse climatic conditions. To achieve this, we evaluated the performance of seven accessions from the Hybrid Timor germplasm in comparison to the Rubi and IPR 100 cultivars, known for their susceptibility and resistance to drought, respectively. The experiment took place in a greenhouse under two conditions: one with normal soil moisture levels near maximum capacity, and the other with a water deficit scenario involving a period of no irrigation followed by rehydration. Data on physiological and biochemical factors were collected at three stages: before applying the water deficit, during its imposition, and after rehydration. Growth data were obtained by the difference between the beginning and end of the experimental period Furthermore, field evaluations of the productivity of the same genotypes were carried out over two consecutive seasons. Based on physiological and biochemical assessments, the MVPi was computed, employing Euclidean distance between principal component multivariate analysis scores. Subsequently, this index was correlated with growth and productivity data through linear regressions. Our findings reveal that the plastic genotypes that are capable of significantly altering physiological and biochemical parameters in response to environmental stimuli exhibited reduced biomass loss in both aerial and root parts. As a result, this positively influenced their productivity. Enhanced plasticity was particularly prominent in accessions from the MG Germplasm Collection: MG 311—Hybrid Timor UFV 428-02, MG 270—Hybrid Timor UFV 377-21, and MG 279—Hybrid Timor UFV 376-31, alongside the Rubi MG 1192 cultivar. The MVPi emerged as a valuable instrument to assess genotype adaptability and predict their performance under varying climatic scenarios. Full article

Intensive irrigation and nutrient management practices in agriculture have given rise to serious issues in aquifer water depletion and groundwater quality. This review discusses the effects of irrigation and nitrogen management practices on potato growth, yield, and quality, and their impacts on water and nitrogen use efficiencies. This review also highlights the economics and consequences of applying deficit irrigation strategies in potato production. Many researchers have demonstrated that excessive irrigation and nitrogen application rates negatively impact potato tuber yield and quality while also increasing nitrate leaching, energy consumption, and the overall costs of production. An application of light-to-moderate deficit irrigation (10–30% of full irrigation) together with reduced nitrogen rates (60–170 kg/ha) has a great potential to improve water and nitrogen use efficiencies while obtaining optimum yield and quality in potato production, depending on the climate, variety, soil type, and water availability. There is an opportunity to reduce N application rates in potato production through deficit irrigation practices by minimizing nitrate leaching beyond the crop root zone. The best irrigation and nitrogen management techniques for potato production, as discussed in this review, include using sprinkle and drip irrigation techniques, irrigation scheduling based on local crop coefficients, soil moisture content, and crop modeling techniques, applying slow-release nitrogenous fertilizers, split nitrogen application, and applying water and nitrogenous fertilizers in accordance with crop growth stage requirements. Full article

Irrigated agriculture is facing a serious problem of water scarcity, which could be mitigated by optimizing the application of regulated deficit irrigation (RDI) strategies. For this reason, the aim of our study was to determine irrigation thresholds based on direct water status indicators of apricot trees under RDI to maximize water productivity. Three treatments were tested: (i) Control (CTL), irrigated at 100% of the crop evapotranspiration (ET_c) during the entire crop cycle; (ii) RDI₁, irrigated as CTL, except during fruit growth stages I–II when irrigation was reduced by 20% of CTL, and during late post-harvest, with an irrigation threshold of a moderate water stress of −1.5 MPa of stem water potential (Ψ_s); and (iii) RDI₂, irrigated as RDI₁, but during late post-harvest using a severe water stress threshold of −2.0 MPa of Ψ_s. As the irrigation scheduling of RDI₁ and RDI₂ did not affect yield and fruit quality, the crop water productivity was increased by 13.2 and 25.6%, respectively. This corresponded to 1124 and 2133 m³ ha⁻¹ of water saved for RDI₁ and RDI₂. A water stress integral of 30.2 MPa day during post-harvest could be considered optimal since when 41 MPa day was accumulated, vegetative growth was reduced by 35%. The non-sensitive periods to water deficit were delimited by the accumulation of growing degree days (GDD) from full bloom, the end of fruit growth stages I–II corresponded to an accumulation of 640 °C GDD, and the beginning of the late post-harvest to an accumulation of 1840 °C GDD. Full article

Drought is a severe threat to agriculture production that affects all growth stages of plants, including corn (Zea mays L.). Any factor affecting early seedling growth and development will significantly impact yield. Despite the recurrence of low rainfall during the growing seasons, corn responses to different early-season soil moisture content levels have not been investigated. In this study, we investigated how corn morpho-physiological and biomass traits responded to varied soil moisture content during the early vegetative stage. Two corn hybrids were grown in a pot-culture facility under five different soil moisture treatments (0.15, 0.12, 0.09, 0.06, and 0.03 m³ m⁻³ volumetric water content, VWC) to assess the growth and developmental responses to varied soil moisture content during early-season growth (V2 to V7) stage. Sub-optimal soil moisture content limited plant growth and development by reducing physiological and phenotypic expression. Stomatal conductance and transpiration were decreased by an average of 65% and 59% across stress treatments relative to optimum conditions. On average, soil moisture deficit reduced the total leaf area by 71% and 72% compared to the control in ‘A6659VT2RIB’ and ‘P1316YHR’, respectively. Shoot and root dry weights were reduced by 74% and 43% under 0.03 m³ m⁻³ VWC. An increase in the root-to-shoot ratio was noticed under low VWC conditions compared to the control. Based on the stress tolerance index, the physiology and leaf growth parameters were more sensitive to soil moisture deficit. Our results highlight the impact of sub-optimal soil moisture on physiology and morphological traits during early-season growth. ‘P1316YHR’ demonstrated better physiological performance under stress conditions, while ‘A6659VT2RIB’ produced relatively better root growth. The findings suggest that biomass partitioning between shoot and root components is dynamic and depends on stress intensity. The current findings can help to prioritize traits associated with the early-season drought tolerance in corn. The functional relationships developed between soil moisture content and growth and developmental responses can be integrated into corn crop modeling to allow better irrigation management decisions. Full article

Crop water production function models (WPFMs) are required methods to study the relationships between yield and water consumption under regulated deficit irrigation (RDI). In this study, a pot experiment was established to study the effect of water deficit during both individual growth stages and across two consecutive growth stages of rice on yield, water consumption, and water use efficiency (WUE) in 2017 and 2018. Light, medium, and severe water deficits were set as 80~90%, 70~80%, and 60~70% of saturated soil moisture content, respectively. The accuracies of five WPFMs were tested based on the experimental results. The results showed that yields and WUE of a light water deficit were higher than those of medium and severe water deficits at each growth stage. The yields and WUE of light drought stress treatments in the flowering and milky stages were higher than the saturated soil moisture control by 4~7.4% and 5.3~20.6%, respectively. Water consumption decreased with increasing water deficit across two consecutive growth stages. The Minhas model had the highest simulation accuracy of the five WPFMs, with relatively lower AE, RMSE, C_v, CRM, and higher R², which were 0.0002, 0.0634, 6.9965, 0.0002, and 0.9951 in 2017 and 0.0110, 0.0760, 8.9882, 0.0131, and 0.9923 in 2018, respectively. The sensitivity indices for the Minhas model more accurately reflected the sensitivity of rice yield to water deficit at different growth stages in 2017 and 2018, compared with the Jensen model, Stewart model, Blank model, and Singh model. Rice yield was most sensitive to water deficit at the jointing and booting stage. The results indicate that the Minhas model is the most suitable WPFM for guiding rice irrigation practices in cold and black soil regions of China. Full article

The major global challenge for the coming decades will be increasing crop production with less water consumption. Precision agriculture (PA) and variable deficit irrigation (VDI) are management strategies that help farmers to improve crop production, fertilizer’s efficiency, and water use efficiency (WUE). The effects of irrigation (IR1 = variable deficit irrigation; IR2 = farmers’ irrigation common practices) under three fertilization (Ft1, Ft2, Ft3) treatments were studied on a cotton yield, on various indicators for more efficient water and fertilizer use, and on plant growth characteristics by applying a number of new agrotechnologies (such as TDR sensors; soil moisture (SM); PA; remote-sensing NDVI (Sentinel-2 satellite sensors); soil hydraulic analyses; geostatistical models; and SM root-zone modelling 2D GIS mapping). The reference evapotranspiration was computed based on the F.A.O. Penman–Monteith method. The crop (ETc) and actual (ETa) evapotranspiration were computed using crop coefficients obtained from the remote-sensing NDVI vegetation index (R² = 0.9327). A daily soil–water–crop–atmosphere (SWCA) balance model and a depletion model were developed using sensor data (climatic parameters’ sensors, as well as soil and satellite sensors) measurements. The two-way ANOVA statistical analysis results revealed that irrigation (IR1 = best) and fertilization treatments (Ft2 = best) significantly affected the cotton yield, the plant height, the plant stem, the boll weight, the above-ground dry matter, nitrogen and fertilizer efficiency, and WUE. VDI, if applied wisely during critical growth stages, could result in a substantial improvement in the yield (up to +28.664%) and water savings (up to 24.941%), thus raising water productivity (+35.715% up to 42.659%), WUE (from farmers’ 0.421–0.496 kg·m⁻³ up to a VDI of 0.601–0.685 kg·m⁻³), nitrogen efficiency (+16.888% up to +22.859%), and N-P-K fertilizer productivity (from farmers’ 16.754–23.769 up to a VDI of 20.583–27.957). Full article