Optimal Irrigation Management Research Articles

IoT and Machine Learning based Precision Agriculture through the Integration of Wireless Sensor Networks

In this study, the integration of IoT technology and machine learning (ML) algorithms with precision agriculture are studied, with an emphasis on optimizing irrigation management through wireless sensor networks. Data are collected in real time from various sensors that measure temperature, humidity, moisture in the soil and water levels. Such data is used to predict the demands for water and control pump operations based on them. They employed different machine learning (ML) models, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF) and Naive Bayes (NB). The accuracy of pump operation predictions is then evaluated. SVM presents the highest overall accuracy. Followed closely by DT and RF. Confusion matrices can be used to glean insights into the misclassification patterns of each model, guiding the selection of the most effective ML approach for precision agriculture applications. IoT and ML technologies together enable real-time monitoring, adaptive control, and data-driven decision-making in agriculture, all of which can help towards efficiently using water resources in dealing with climate change and rapidly growing food demands.

Optimizing deficit drip irrigation to improve yield,quality, and water productivity of apple in Loess Plateau of China

ContextDeficit drip irrigation is an advanced agricultural technology widely promoted and applied around the world to maintain/increase yield and quality while simultaneously minimizing water consumption. However, there is still a lack of comprehensive research on the effects of deficit drip irrigation at different stages on apple yield, water productivity, and quality. ObjectiveThe aim was to investigate the effects of deficit drip irrigation treatments (DDITs) on apple yield, quality, and water productivity (WP), and optimize the deficit drip irrigation strategy for apples in the Loess Plateau of China. Methodsa two-year field experiment was conducted with 17 irrigation treatments, including a control treatment (CK, full irrigation (FI)) and four DDITs (D15%, D30%, D45%, D60%) during the bud burst to leafing stage (I), flowering to fruit set stage (II), fruit expansion stage (III), and fruit maturation stage (IV). The optimal irrigation management was determined using the fuzzy Borda combined model, which was based on four different single evaluation models of the TOPSIS method, principal component analysis (PCA), grey correlation analysis (GCA), and membership function analysis (MFA). ResultsThe DDITs at stage IV decreased midday leaf water potential (Ψs) by 2.57–20.00% compared with CK, and the difference of Ψs between IV-D30%, IV-D45%, IV-D60%, and CK were significant in two years (P<0.05). DDITs at stages I, II, and IV improved fruit weight (FW), fruit shape index (FSI), and fruit firmness (FN), but did not significantly affect fruit water content (FWC). The DDITs at stages III and IV significantly enhanced total soluble solids (TSS), soluble sugars (SS), and vitamin C (Vc) in 2021 and 2022, but a notable decrease in titratable acidity (TA) was observed. ConclusionsDDITs at stage IV exhibited the highest comprehensive benefit of apple, followed by DDITs at stages I and II, and DDITs at stage III had the lowest values. The DDITs of D15%, D30%, CK, and D15% at stages I, II, III, and IV were identified as the optimal irrigation strategy to improve apple yield, quality, and WP. ImplicationsOur findings can guide the development of appropriate irrigation strategies for apple cultivation in Loess Plateau of China, promoting efficient and sustainable apple production.

Assessing the impact of irrigation and nitrogen management on potato performance under varying climate in the state of Florida, USA

Optimizing irrigation and nitrogen (N) fertilizer management in irrigated potato crops grown on sandy soils in subtropical regions such as in northeastern Florida, USA is essential to sustain a high yield and to minimize leaching. N applications in this region typically occur at approximately 25–30 days prior to planting (Npre), at emergence (Neme), and at tuber initiation (Nti). However, recent studies suggest that applying N near planting (Npl) enhances fertilizer N use efficiency (FNUE). We combined experimentation with modeling to assess irrigation and N management options for potato in northeastern Florida. We first aimed to evaluate the DSSAT/CSM-SUBSTOR-Potato model using two-year irrigated field experiments conducted on sandy soils with variable N rates and application timings. CSM-SUBSTOR-Potato accurately simulated aboveground plus tuber dry weight [Relative root mean squared error (RRMSE) = 26.4%, Willmott’s index (d) = 0.98] and N accumulation (RRMSE = 28.6%, d = 0.97). Soil moisture and mineral N were captured well overall, but they were often underestimated due to a water table influence that is currently not considered in DSSAT. Subsequently, CSM-SUBSTOR-Potato was applied to simulate tuber yield, N leaching, and FNUE under scenarios of irrigation scheduling and N-fertilizer application (rate/timing) strategies, focusing on Npre versus Npl aiming to improve resource use efficiency. The simulations indicated that a target of 60% and 70% of the available soil water can be safely used as an irrigation strategy to achieve a high yield, while reducing irrigation water applied and N leached to the environment. Overall Npl increased crop N uptake by 10%, tuber yield by 7%, reduced N leached by 13%, and consequently increasing FNUE by 9%, compared to Npre across the irrigation treatments. Thus, Npl should be preferred in sandy soils and climate-risky subtropical environments, along with Neme and Nti as key timings to synchronize N supply with potato growth.

Sub-seasonal soil moisture anomaly forecasting using combinations of deep learning, based on the reanalysis soil moisture records

Sub-seasonal drought forecasting is crucial for early warning in estimating agricultural production and optimizing irrigation management, as forecasting skills are relatively weak during this period. Soil moisture exhibits stronger persistence compared to other climate system quantities, which makes it especially influential in shaping land-atmosphere feedback, thus supplying a unique insight into drought forecasting. Relying on the soil moisture memory, this study investigates the combination of multiple deep-learning modules for sub-seasonal drought indices hindcast in the Huai River basin of China, using long-term ERA5-Land soil moisture records with a noise-assisted data analysis tool. The inter-compared deep-learning models include a hybrid model and a committee machine framework. The results show that the performance of the committee machine framework can be improved with the help of series decomposition and the forecasting skill is not impaired with the lead time increases. Overall, this study highlights the potential of combining deep-learning models with soil moisture memory analysis to improve sub-seasonal drought forecasting.

Spatiotemporal Absorption Features of Yellow Willow Water Usage on the Southern Edge of the Semi-Arid Hunshandak Sandland in China

The improvement of water usage efficiency and productivity, as well as the development of effective water management plans, necessitates a comprehensive understanding of how water utilization patterns in different soil layers within arid and semi-arid climates impact the capacity of plant roots to absorb water. However, there is currently no knowledge regarding the water use strategies employed by artificial yellow willow. So, we conducted a study on the hydrogen and oxygen isotopic composition of rainfall in yellow willow (Salix gordejevii) from the semi-arid region located at the southern edge of the Hunshandak Sandland in China. This study utilized measured data on xylem water, groundwater, soil moisture, and rainfall. By employing a combination of the direct comparison method and the MixSIAR model, we investigated the water uptake strategies employed by yellow willow throughout its growing season. The findings revealed that the mean δ D was highest in precipitation and lowest in groundwater, whereas the mean δ18O was highest in stem water and lowest in groundwater. The δ D and δ18O fluctuated significantly in precipitation but were steady in groundwater. Because precipitation was significantly less than evaporation, the slope and intercept were lower for the local than global atmospheric precipitation line. Water availability steadily declined with increasing depth. Lower δ18O values were caused by precipitation diluting the soil water. The MixSIAR results indicated that the primary source in May, September, and October was utilized at 19%, 18%, and 18%, respectively. In contrast, the utilization rate of each source varied considerably in June, July, and August (the primary source was utilized at 19%, 18%, and 18%, respectively). Comparatively high rates of water absorption and utilization were observed in June (19% of the total water source), July (18%), and August (23%). Therefore, the vertical distribution of the root system and variations in the soil water content regulate water usage for the yellow willow. To prevent excessive water usage and promote ecosystem restoration with artificial yellow willow plantations in water-limited desert settings, policy makers should consider the patterns of plant water use and soil water availability. By selecting drought-adapted plant species and optimizing irrigation management, it is possible to reduce water wastage and ensure that water is used efficiently for revegetation and ecosystem restoration, avoiding overuse of water and maintaining the sustainability of revegetation in water-stressed desert areas.

Enhancing productivity, economics and energy efficiency through precision nitrogen and water management in conservation agriculture-based maize (Zea mays) in the Indo-Gangetic Plains

Present study focuses on improving maize productivity, economics, and energy efficiency in the Indo-Gangetic Plains through the integration of CA, precision nitrogen and water management. Maize grain yield significantly differed among treatments, with CA outperforming CT by 13.3%, recording the highest yield with optimal N application (N3) and irrigation at 25% DASM. The CA incurred 23.7% lower cultivation costs (₹30,421/ha) compared to CT. Gross returns and net returns were higher under CA (₹1,16,007/ha and ₹85,586/ha) with a net benefit ratio of 2.78, showcasing its economic viability. Energy efficiency was a crucial aspect considered, with CA proving to be 33.1% more energy-efficient than CT. In different irrigation regimes, CA with W2 treatment exhibited superior energy parameters. The study also highlighted the significance of optimal N scheduling (N3) in achieving higher economic returns (₹97,927/ha) compared to conventional N splits (N1) and its integration. The most effective integration involved combining CA with precision N management (75% basal, GreenSeekerTM-guided top dressing) and irrigation at 25% DASM, resulting in higher grain yield (7.21 t/ ha), gross returns (₹132,497/ha), and impressive energy output (230,831 MJ/ha). In conclusion, CA, especially when combined with optimal irrigation and nitrogen management, not only enhances maize yield and economic returns but also proves to be more energy-efficient, promoting sustainable and resource-efficient agricultural practices. The study recommends this integrated approach for enhancing maize productivity, energy efficiency and economic returns.

ESTIMATION OF SUGARCANE YIELD USING MULTI-TEMPORAL SENTINEL 2 SATELLITE IMAGERY AND RANDOM FOREST REGRESSION

Abstract. Advancements in remote sensing techniques have greatly enhanced crop monitoring and yield estimation, with spectral vegetation indices (VIs) serving as a key component. Our study investigates the use of Sentinel-2 data, notable for its red-edge bands, in estimating sugarcane yield in Ethiopia's Awash Basin. Utilizing 22 VIs from S2 imagery, our approach combines Random Forest (RF) regression with the Recursive Feature Elimination (RFE) algorithm to improve the accuracy of sugarcane yield predictions. The results demonstrate the superior performance of the RF-RFE method over traditional RF with full datasets and Stepwise Multiple Regression (SMR). Particularly, VIs focusing on the red-edge spectral bands of S2 - such as NDVIre1n, NDVIre2n, NDVIre3n, NDRE1 and NDRE2 - were crucial in enhancing prediction precision. These indices from the red-edge and NIR narrow bands consistently influenced yield estimations in both the Wonji-Shoa and Metehara estates. The study underscores the critical role of the RFE algorithm in optimal variable selection, reinforcing earlier findings that precise variable choice can substantially boost model accuracy. The enhanced performance of the RF model when paired with the RFE algorithm was evident, emphasizing the importance of variable selection in accurate yield predictions. Employing the Out-of-Bag RMSE (OOB_RMSE) error estimate for evaluation, we observed variations in OOB_RMSE performance with different RF parameters, identifying the ntree value of 500 as optimal for the studied regions. The RF-RFE model’s estimations showed lower errors and higher correlation coefficients, proving its efficacy over a full dataset approach, which faced challenges with traditional VIs' saturation. Our findings align with earlier studies, highlighting the efficiency of S2’s red-edge bands in diverse estimation tasks and a shift towards using freely available broadband images like S2, over hyperspectral imagery, due to reduced data redundancy and processing costs. In conclusion, our findings reveal that RF regression, particularly when integrated with the RFE algorithm, is a powerful tool in remote sensing applications. The S2 imagery is optimal VIs, predominantly from the red-edge bands, exhibit significant potential for sugarcane yield estimations. The impressive results of the RF-RFE method, evident in metrics like MAE, MAPE, RMSE, Mean percentage, and R2, advocate its invaluable role in sugarcane yield prediction, highlighting its potential for optimizing irrigation management strategies and broad-spectrum agricultural planning.

Assessment of using ponds as a nature-based solution on the optimal irrigation management of paddy fields

Abstract The water availability to supply agricultural water requirements plays an important role in the performance of agricultural products. Paddy fields depend heavily on water during cultivation, making this issue particularly important. In such cases, nature-based solutions, such as ponds, can be a suitable method to address water shortages for irrigation purposes. This study aims to develop a water allocation optimization model to evaluate the impact of two scenarios in ponds. In the scenario of increasing the ponds' depth, the hydraulic connection between the ponds and the groundwater level was taken into consideration. Based on this perspective, the scenario of increasing the depth resulted in a 0.33 million m3 increase in the volume of ponds, leading to an average percentage increase of 0.23% in the supply of irrigation requirements. The optimized model results in the pond dredging scenario showed a 2.03 million m3 increase in the volume of available water compared to the existing conditions, and a 1.70 million m3 increase compared to the pond deepening scenario. The results show that dredging ponds have a greater impact on increasing available water volume. It is also important to consider the hydraulic connection between ponds and groundwater levels in the pond-deepening approach.

Effects of Different Irrigation Management and Nitrogen Rate on Sorghum (Sorghum bicolor L.) Growth, Yield and Soil Nitrogen Accumulation with Drip Irrigation

Sorghum (Sorghum bicolor L.) has emerged as a pivotal global food crop. Consequently, it is imperative to explore sustainable and eco-friendly strategies to achieve sustainable sorghum production with a high yield. This study aimed to reveal the effects of irrigation management and nitrogen rates and their interactions on sorghum growth traits, yield and soil nitrate-N and ammonium-N accumulation to improve irrigation and nitrogen practices under drip irrigation. A 2-year (2021 and 2022) field experiment was conducted on drip-irrigated fertilized sorghum in Heilongjiang Province to investigate the effects of three lower levels of soil moisture (80% (HI), 70% (NI), and 60% (LI) of field capacity) with four nitrogen rates at 225, 150, 75 and 0 kg/ha (designated as HN, NN, LN and WN, respectively) on sorghum growth, yield and soil nitrogen accumulation. The results indicated that irrigation management and nitrogen rate interaction had a significant effect on sorghum growth (plant height, stem diameter, leaf area index (LAI), and SPAD value), yield, aboveground biomass and 0~60 cm soil nitrogen accumulation (p &lt; 0.05). The NNHI treatment demonstrated the highest plant height (120.9 and 121.8 cm) and LAI (2.738 and 2.645) in 2021 and 2022, and there was a significant positive correlation between plant height, LAI, and yield (p &lt; 0.01). However, the NNNI treatment exhibited the highest yield (7477.41 and 7362.27 kg/ha) in 2021 and 2022, sorghum yield increased and then decreased with an increase in irrigation management and nitrogen rate. In addition, soil nitrate-N and ammonium-N accumulation were significantly affected by the interaction of irrigation management and nitrogen rate (p &lt; 0.05) while irrigation management had no significant effect on the accumulation of nitrate-N and ammonium-N. Soil nitrate-N and ammonium-N accumulation increased with the increasing nitrogen rate. Although yield differences between the NNNI and HNNI treatments were not significant, the NNNI treatment with a lower soil moisture limit of 70% field capacity and a nitrogen rate of 150 kg/ha accumulated 10.4% less nitrate-N in soil than the HNNI treatment, reduced risk of nitrate nitrogen leaching. The regression analysis indicated that the optimal irrigation management and nitrogen rate management practices of 71.93% of the soil moisture lower limit and 144.58 kg/ha of nitrogen rate was an optimal strategy for favorable sorghum growth, high-yielding and low soil nitrate-N accumulation of sorghum. This study provides a scientific reference for precise water and fertilizer management in sorghum.

Optimizing irrigation management sustained grain yield, crop water productivity, and mitigated greenhouse gas emissions from the winter wheat field in North China Plain

Climate change caused by increasing greenhouse gas (GHG) emissions has led to frequent extreme weather events, which seriously threaten sustainable agricultural production. Therefore, it is essential to optimize proper irrigation management to improve the grain yield, crop water productivity (WPc), economic crop water productivity (EWPc), and lower global warming potential (GWP) and GWP Intensity (GWPI). The effect of irrigation scheduling and irrigation methods on GHG emissions remains largely unknown, even though this knowledge is essential to optimize the irrigation management. To address this knowledge gap, a field experiment was carried out in the North China Plain (NCP) for three winter wheat seasons to measure the influence of different irrigation methods and irrigation scheduling on WPc, EWPc, GWP, and GWPI. Irrigation scheduling including 50%, 60%, and 70% of the field capacity (FC) were kept in the main plots and irrigation methods, including sprinkler, drip, and flood irrigation methods in the sub-plots. The results revealed that relative to sprinkler irrigation at 60% FC, drip irrigation at 60% FC significantly (p < 0.05) improved the yield 4.89–7.52%, WPc 1.0–5.4%, EWPc 1.1–5.49%, lower GWP 7.47–9.34%, and GWPI 10.92–15.23%. Compared with flood irrigation at 60% FC, drip irrigation at 60% FC increased grain yield 5.34–6.81%, WPc 5.65–15.1%, EWPc 5.73–15.12%, lower GWP 10.36–15.16%, and GWPI 16.22–19.40%. Technique for order preference by similarity to an ideal solution (TOPSIS) presented that compared with sprinkler and flood irrigation at 60% FC irrigation scheduling, drip irrigation at 60% FC irrigation scheduling provides the best results for improved the yield, WPc, and EWPc, and optimal balance in GWP and GWPI. Therefore, the irrigation scheduling of 60% FC combined with drip irrigation is suggested for sustained wheat yield, improved WPc and EWPc, and mitigated GWP in the NCP.

Toward Optimal Irrigation Management at the Plot Level: Evaluation of Commercial Water Potential Sensors.

Proper irrigation practice consists of applying the optimum amount of water to the soil at the right time. The porous characteristics of the soil determine the capacity of the soil to absorb, infiltrate, and store water. In irrigation, it is not sufficient to only determine the water content of the soil; it is also necessary to determine the availability of water for plants: water potential. In this paper, a comprehensive laboratory evaluation-accuracy and variability-of the world's leading commercial water potential sensors is carried out. No such comprehensive and exhaustive comparative evaluation of these devices has been carried out to date. Ten pairs of representative commercial sensors from four different families were selected according to their principle of operation (tensiometers, capacitive sensors, heat dissipation sensors, and resistance blocks). The accuracy of the readings (0 kPa-200 kPa) was determined in two soils of contrasting textures. The variability in the recordings-repeatability and reproducibility-was carried out in a homogeneous and inert material (sand) in the same suction range. The response in terms of accuracy and value dispersion of the different sensor families was different according to the suction range considered. In the suction range of agronomic interest (0-100 kPa), the heat dissipation sensor and the capacitive sensors were the most accurate. In both families, registrations could be extended up to 150-200 kPa. The scatter in the readings across the different sensors was due to approximately 80% of the repeatability or intrinsic variability in the sensor unit and 20% of the reproducibility. Some sensors would significantly improve their performance with ad hoc calibrations.

Timely monitoring of soil water-salt dynamics within cropland by hybrid spectral unmixing and machine learning models

Soil salinization and water scarcity are main restrictive factors for irrigated agriculture development in arid regions. Knowing dynamics of soil water and salt content is an important antecedent in remediating salinized soils and optimizing irrigation management. Previous studies mostly used remote sensing technologies to individually monitor water or salt content dynamics in agricultural areas. Their ability to asses different levels of crop water and salt management has been less explored. Therefore, how to extract effective diagnostic features from remote sensing images derived spectral information is crucial for accurately estimating soil water and salt content. In this study, Linear spectral unmixing method (LSU) was used to obtain the contribution of soil water and salt to each band spectrum (abundance), and endmember spectra from Sentinel-2 images. Calculating spectral indices and selecting optimal spectal combination were individually based on soil water and salt endmember spectra. The estimation models were constructed using six machine learning algorithms: BP Neural Network (BPNN), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Gradient Boost Regression Tree (GBRT), and eXtreme Gradient Boosting tree (XGBoost). The results showed that the spectral indices calculated from endmember spectra were able to effectively characterize the response of crop spectral properties to soil water and salt, which circumvent spectral ambiguity induced by water-salt mixing. NDRE spectral index was a reliable indicator for estimating water and salt content, with determination coefficients (R2) being 0.55 and 0.57, respectively. Compared to other models, LSU-XGBoost model achieved the best performance. This model properly reflected the process of soil water-salt dynamics in farmland during crop growth period. This study provided new methods and ideas for soil water-salt estimation in dry irrigated agricultural areas, and provided decision support for governance of salinized land and optimal management of irrigation.

Assessing Soil Dynamics and Improving Long-Standing Irrigation Management with Treated Wastewater: A Case Study on Citrus Trees in Palestine

Irrigation with Treated Wastewater (TWW) is a well-known agricultural practice in Palestine. The long-term use of irrigation with TWW, a source of water and nutrients, can affect plant development, soil, and groundwater quality. Consequently, the frequency and the intervals of irrigation events should be adequately scheduled, especially when nutrients (TWW-N) cannot be separated from the water. Achieving good water quality implies its immediate reuse in irrigated agriculture. In contrast, long-term soil and groundwater quality conservation is marked by the complex mechanisms that correlate the soil, water, plant, and atmosphere. Therefore, monitoring and modeling (MMA) are combined to retrieve the soil water and nitrate fluxes and identify a proper irrigation management plan in a case study in Beit Dajan-Palestine, where a schedule adapted to conventional water was applied to a 6-year-old citrus orchard continuously irrigated with TWW. Soil nitrogen concentration and water content data were collected from March to August 2021 to calibrate the Hydrus-1D model under the (1) farmer demand (F) scenario, where irrigation volumes are delivered according to the farmer experience, and to define an optimal irrigation management strategy with TWW according to the (2) model demand (M) scenario, based on the irrigation frequency. The latter respects the allowable thresholds of soil solution electrical conductivity, σe, assuming an average soil salinity profile and estimated leaf nitrogen concentrations tolerance as reference; 2021 was taken as a calibration year to retrieve water and nitrate fluxes for 2019 and 2020. In 2021, the measured soil electrical conductivity, σe, showed no salinity risk with an average value of 1.07 dS m−1 (low salinity &lt; 2 dS m−1) but with a leaf nitrogen deficit. Although an acceptable level of available soil nitrogen was observed (ranging between 10 and 35 mg kg−1, whereas the standard value is 10–40 mg kg−1), critical concentrations were observed in the leaves (below 1%) in scenario (F) compared to scenario (M) (ranging between 1.7 and 1.9%). The latter also showed a decrease in nitrate leaching by 33% compared to the former. Overall, the comparison between the simulated and measured soil variables shows that the 1D-Hydrus model could follow the temporal variation in the monitored data, with some overestimation of the measured data during the simulation period. The simulations demonstrate that by modulating the salt tolerance threshold, the M scenario achieved better results in terms of root water and N uptake despite the stress inevitably experienced by citrus with long-term TWW irrigation. Moreover, the optimum threshold values used to assess the soil quality and citrus response under conventional water irrigation were inadequate for TWW practices. Therefore, MMA could be an alternative strategy to schedule proper TWW irrigation.

Assessing the water conservation potential of optimized surface irrigation management in Northern Italy

The effects of climate change on water availability affect the performance of surface irrigation, which is the oldest and most common method of water application to row crops worldwide. A paradigm shift towards strategies aimed at increasing flexibility of irrigation scheduling and improving the design and management of field layouts and irrigation practices should be explored to promote water conservation at the farm scale. In this study, we investigate how by adopting a more flexible irrigation scheduling and optimizing irrigation management variables and field layout it is possible to increase the efficiency of border irrigation and thus achieve water conservations and improve quality of crop production. The analysis of the actual performance of border irrigation was carried out on two maize fields located in the Padana Plain (Northern Italy) in 2 years characterized by different rainfall patterns (i.e. 2021 and 2022). Based on this information, continuous monitoring of soil moisture status combined with the AquaCrop-OS agro-hydrological model was used to manage flexible irrigation scheduling over the experimental fields, while the optimization of irrigation management (flowrate per unit width and cutoff time) and field geometries (border width and slope) was studied using WinSRFR 5.1 USDA software, which was properly calibrated by measures of waterfront advance and recession. The results show that with flexible irrigation scheduling and proper irrigation management and field layout, significant water conservation can be achieved. Specifically, in the case study, seasonal water conservation of about 10% was obtained just by scheduling irrigation based on actual crop water needs in a very dry agricultural season, while water conservation reached up to 60% in a wetter season. On average, an additional 7% of water conservation was achieved over the agricultural season when the irrigation duration was correctly applied to each border of the experimental plots, while approximately 20% of water was conserved when the border width was correctly designed based on inflow availability. These results provide useful information for improving the management of border irrigation in practice, both under current conditions and in prospective of increasing freshwater scarcity in the future.

Assessment of the sustainability of groundwater utilization and crop production under optimized irrigation strategies in the North China Plain under future climate change

Over-exploitation of groundwater due to intensive irrigation and anticipated climate change pose severe threats to the water and food security worldwide, particularly in the North China Plain (NCP). Limited irrigation has been recognized as an effective way to improve crop water productivity and slow the rapid decline of groundwater levels. Whether optimized limited irrigation strategies could achieve a balance between groundwater pumping and grain production in the NCP under future climate change deserves further study. In this study, an improved Soil and Water Assessment Tool (SWAT) model was used to simulate climate change impacts on shallow groundwater levels and crop production under limited irrigation strategies to suggest optimal irrigation management practices under future climate conditions in the NCP. The simulations of eleven limited irrigation strategies for winter wheat with targeted irrigations at different growth stages and with irrigated or rainfed summer maize were compared with future business-as-usual management. Climate change impacts showed that mean wheat (maize) yield under adequate irrigation was expected to increase by 13.2% (4.9%) during the middle time period (2041–2070) and by 11.2% (4.6%) during the late time period (2071–2100) under three SSPs compared to the historical period (1971–2000). Mean decline rate of shallow groundwater level slowed by approximately 1 m a−1 during the entire future period (2041–2100) under three SSPs with a greater reduction for SSP5–8.5. The average contribution rate of future climate toward the balance of shallow groundwater pumping and replenishment was 62.9%. Based on the simulated crop yields and decline rate of shallow groundwater level under the future climate, the most appropriate limited irrigation was achieved by applying irrigation during the jointing stage of wheat with rainfed maize, which could achieve the groundwater recovery and sustainable food production.

Effect of Magnetic Water Treatment on the Growth, Nutritional Status, and Yield of Lettuce Plants with Irrigation Rate

Climate change is causing an increase in dry spells, altering rainfall patterns and soil moisture, and affecting water and nutrient plant uptake, which inevitably affects vegetable production. To mitigate this issue, some technologies that allow the maintenance of the ideal soil moisture for the uptake process are being investigated. Considering this, we hypothesize that the use of water treated with a magnetic field can increase water use efficiency in lettuce crop production. Thus, the present study aimed to evaluate the effect of the irrigation rate of magnetically treated water on biomass accumulation and nutrient uptake by lettuce plants. An experiment was conducted in a randomized block design with a 2 × 5 factorial arrangement of two water sources (conventional water and magnetically treated water) and five irrigation application rates to replace crop evaporation: 25, 50, 75, 100, and 125%, with five replicates. The use of magnetically treated water increased the concentrations of nitrogen and phosphorus in leaves, meaning that it induced higher nitrogen assimilation, leading to increases in agronomical characteristics (leaf number, fresh and dry shoot weight, fresh and dry root weight). The conclusions of this study showcase that magnetically treated water has beneficial effects on lettuce plants, improving their nutritional status and yield. Moreover, the results presented can lead to an increase in water use efficiency, thus optimizing irrigation management.

Tomato leaf color as predictor of soil moisture value using machine learning techniques

Fresh water supplies for irrigation purposes must be used sparingly and judiciously, as water is an invaluable natural resource that is in short supply in much of the Earth. Soil moisture in fields is not uniform everywhere, and deploying thousands of sensors is unnecessarily expensive. The purpose of this publication is to model and predict the relationship between tomato plants leaf color, soil moisture, and thus manage the irrigation process in an optimal manner. The research was conducted using generally accepted methods, the field method, and the method of statistical evaluation of results. Machine learning algorithms (MLA) and data mining are utilized in this paper to model the relationship between RGB color values from tomato leaves and soil moisture and temperature. The color of the leaves of open field tomato plantations grown without stakes is the focus of this study. Three main tasks are fulfilled: to prove that there is a relationship between leaf color and soil moisture, to study its supposedly nonlinear type and to model this relationship with MLA. First, a classifier is trained, and then a model is created and saved. Finally, the efficiency of the chosen model is tested using a different test data set. The name “12-9-6-3” for the methodology of measurements is fgiven. It is proven that the young leaves are more informative about the need for watering. As a result, there is less than a 1% error in predicting soil moisture using the color of tomato leaves considering also soil temperature, using M5P regression model. This predictive model can be used in creation of automated systems for optimal irrigation management and water saving

Performance of a Multi-Sensor Capacitance Probe in Estimating Soil Water Content and Field Capacity

Highlights Among six manufacturer calibrations, the default calibration resulted in the largest errors. Sensor performance was negatively affected by higher clay content and salinity. Sensor-based approaches to estimating field capacity were inconsistent and spatially variable. Abstract. Maintaining the economic and environmental sustainability of crop production requires optimizing irrigation management using advanced technologies such as soil water sensors. In this study, the performance of a commercially available multi-sensor capacitance probe was evaluated under irrigated field conditions across western Oklahoma. The effects of clay content and salinity on sensor performance were investigated too. In addition, the field capacity (FC) of soil cores collected at study sites was determined in the laboratory. These laboratory FC values were used to assess the performance of two sensor-based approaches for estimating FC: the days to reach laboratory FC after major watering events and the percentile of collected sensor readings that represented laboratory FC. The results showed that among the six calibrations provided by the manufacturer, the default and silty clay loam calibrations produced the largest and smallest soil water content errors, respectively. Errors generally increased with clay and salinity, except for the heavy clay calibration, which showed improved performance with increasing clay content. The default and sand calibrations were more sensitive to increases in clay and salinity compared to other calibrations. In the case of sensor-based FC, on average, one to three days were required to reach laboratory FC, with a large range of one to nine days. The percentiles representing laboratory FC had an average of 56% and a range of 3%-97%. Overall, the sensor-based approaches produced inconsistent and highly variable estimates of FC. Keywords: Calibrations, Clay content, Irrigation scheduling, Salinity, Sensor accuracy, Soil water threshold.

Assessing the Perspectives of Ground Penetrating Radar for Precision Farming

The United Nations 2030 Agenda for Sustainable Development highlighted the importance of adopting sustainable agricultural practices to mitigate the threat posed by climate change to food systems around the world, to provide wise water management and to restore degraded lands. At the same time, it suggested the benefits and advantages brought by the use of near-surface geophysical measurements to assist precision farming, in particular providing information on soil variability at both vertical and horizontal scales. Among such survey methodologies, Ground Penetrating Radar has demonstrated its effectiveness in soil characterisation as a consequence of its sensitivity to variations in soil electrical properties and of its additional capability of investigating subsurface stratification. The aim of this contribution is to provide a comprehensive review of the current use of the GPR technique within the domain of precision irrigation, and specifically of its capacity to provide detailed information on the within-field spatial variability of the textural, structural and hydrological soil properties, which are needed to optimize irrigation management, adopting a variable-rate approach to preserve water resources while maintaining or improving crop yields and their quality. For each soil property, the review analyses the commonly adopted operational and data processing approaches, highlighting advantages and limitations.

Application of water-energy-food nexus approach for optimal tillage and irrigation management in intensive wheat-maize double cropping system

Demand for water, energy and food is increasing driven by a rising global population, changing diets, warming climate and economic growth. Wheat-maize double cropping system, as one of the best trials in ensuring sustainable food security, is experiencing various optimizing irrigation management and tillage practices in the North China Plain. However, previous studies mainly focused on their influences on crop yield and water use efficiency. Comprehensive analysis of water-energy-food for the practices was limited. Therefore, a life cycle assessments based water–energy–food (WEF) nexus analysis was conducted using data from a 7-year two-factor split-plot field experiments with four tillage (NT, no-tillage; RT, rotary tillage; PT, plow tillage; CT, local current tillage) and three irrigation managements (W1, pre-planting irrigation for wheat + pre-planting irrigation for maize; W2, pre-planting + jointing irrigation for wheat + pre-planting irrigation for maize; and W3, pre-planting + jointing + anthesis irrigation for wheat + pre-planting irrigation for maize; 75 mm each time). The results indicated that irrigation, fertilizer and diesels input were the top three items for water and energy consumption. Irrigation managements significantly altered wheat and annual yield but not for maize yield. Tillage practices primarily altered diesel input to affect energy mass and energy economic productivity. During wheat season, W3PT obtained the highest yield at 8.69 t ha−1 and highest net income at 8.27 ¥ ha−1. With the same tillage practice, W1 had the best performance in water and energy consumption and mass productivity, while W2 had the highest water and energy economic productivity. In terms of different tillage practices with the same irrigation amount, NT consumed lowest energy, but its lowest wheat yield offset its benefits as well. During maize season, the yields were insensitive for irrigation and tillage practices. NT and CT were substantially beneficial regarding energy input, which further resulted in good performance in terms of energy mass and economic productivity under the same irrigation treatment. Throughout the year, lower water and energy consumption and higher resource productivity offset the yield and income loss under W1CT to accomplish highest value of WEF nexus index. Thus it is the optimal tillage and irrigation management pattern in the wheat-maize double cropping system of the North China Plain achieving cleaner production. This study contributed a new case to using WEF nexus analysis and offered new reference data and effective suggestions for selecting proper filed management in the double cropping system to achieve sustainable development goals with carbon peaking and carbon neutrality.