Effective Irrigation Management Research Articles

A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning

Optimizing irrigation water usage is crucial for sustainable agriculture, especially in the context of increasing water scarcity and climate variability. Accurate estimation of evapotranspiration (ET), a key component in determining water requirements for crops, is essential for effective irrigation management. Traditional methods of measuring and estimating ET, such as eddy-covariance systems and lysimeters, provide valuable data but often face limitations in scalability, cost, and complexity. Recent advancements in machine learning (ML) offer promising alternatives to enhance the precision and efficiency of ET estimation and smart irrigation systems. This review explores the integration of machine learning techniques in optimizing irrigation water usage, with a particular focus on ET prediction and smart irrigation technologies. We examine various ML models, that have been employed to predict ET using diverse datasets comprising meteorological, soil, and remote sensing data. In addition to ET estimation, the review highlights smart irrigation systems that optimize irrigation schedules based on real-time data inputs. Through this review, we aim to provide a comprehensive overview of the state-of-the-art in ML-based ET estimation and smart irrigation technologies, contributing to the development of more resilient and efficient agricultural water management strategies.

Performance Evaluation of Rain Hose Irrigation System for Enhancing Water Productivity in Kerala, India

A field experiment was conducted to evaluate the performance of rain hose irrigation system in terms of discharge variation, moisture distribution, and uniformity coefficient at different operating pressures. The study revealed a linear relationship between operating pressure and discharge for rain hose irrigation system. Specifically, at pressures of 0.5 and 1.0 kg cm- ², increased pressure correlated well with higher soil moisture content along with 20% to 23% increase in moisture with greater water-infiltration depth. The distribution of moisture was found to be more uniform at higher pressures, achieving a uniformity coefficient of 74%. This uniformity was enhanced in the multilateral rain hose irrigation system with reduced spatial variation as compared to the single lateral system. As operating pressure increased, discharge per unit length also showed an increase, alongside the throw distance. The rain hose system is particularly advantageous for closely-spaced and shallow-rooted crops like onion, groundnut, and leafy green vegetables, ensuring consistent and high flow rates. The study suggested that regulating operating pressure is crucial for optimizing soil moisture levels, as higher pressures lead to deeper water infiltration. Notably, at a pressure of 1.5 kg cm- ², the system performed the best, yielding the optimum results for irrigation efficiency. In summary, the rain hose irrigation system proved effective at higher operating pressures, providing a reliable method for achieving uniform water distribution and adequate soil moisture for crops, which is essential for effective irrigation management.

Date Palm Waste-Derived Biochar for Improving Hydrological Properties of Sandy Soil Under Saturated and Unsaturated Conditions

Water conservation and effective irrigation management are vital for sustainable agriculture in arid regions. While organic soil amendments have been widely used to enhance water retention in sandy soils, research on the use of date palm waste-derived biochar remains limited. Thus, this study aimed to explore the innovative application of biochar produced from date palm waste, focusing on its effects on the hydrological properties of sandy soil. Biochars of varying particle sizes (0.5, 1, and 2 mm) and pyrolysis temperatures (300 °C, 450 °C, and 600 °C) were produced and their impacts were assessed under both saturated and unsaturated conditions on soil hydrological properties. The biochar was incorporated into soil columns at application rates of 0%, 1%, 3%, and 5% (w/w) within a 10 cm layer on top of 35 cm deep soil columns. The soil columns were placed vertically into water basins for saturation. Evaporation, infiltration, and saturated hydraulic conductivity were measured. The findings revealed that the application of 1%, 3%, and 5% biochar significantly increased soil water retention by 36.80%, 34.18%, and 29.66%, while cumulative evaporation decreased by 7.30%, 2.00%, and 1.35%, respectively, as compared to the control. Water retained at the end of the experiment was increased by 100.63%, 112.29%, and 101.68%, while unsaturated hydraulic conductivity decreased by 21.27%, 26.15%, and 26.17% after amending the soil with 1%, 3%, and 5% biochar, respectively, as compared to the control. The water retention ranged between 30.34 and 42.51%, 22.59 and 43.20%, and 22.48 and 38.81% for biochar produced at 300 °C, 450 °C, and 600 °C, respectively. Water infiltration rate and pore size was decreased with the increased pyrolysis temperature. Overall, the application rates of 3% and 5% with particle sizes of 1 and 0.5 mm and low pyrolysis temperature were most efficient for improving soil properties such as water retention, reducing unsaturated hydraulic conductivity, reducing the rate and volume of infiltration, and enhancing the micro-porosity reduction of sandy soils. In a nutshell, this study highlights the potential of date palm waste-derived biochar as an effective soil amendment, significantly enhancing water retention by up to 112.29% and reducing evaporation. By optimizing irrigation management in sandy soils, these findings contribute to more sustainable agricultural practices.

Soil moisture retrieval from dual-polarized Sentinel-1 SAR data over agricultural regions using a water cloud model.

The accurate retrieval of soil moisture plays a pivotal role in agriculture, particularly in effective irrigation water management, as it significantly affects crop growth and yield. The present study mainly focuses on the robustly investigated capability of dual-polarized Sentinel-1 SAR-derived vegetation descriptors in the water cloud model (WCM) in surface soil moisture (SSM) retrieval over wheat crops. The vegetation descriptors used in the study are radar vegetation index (RVI), backscattering ratio, polarimetric radar vegetation index (PRVI), dual polarization SAR vegetation index (DPSVI), and dual polarimetric radar vegetation index (DpRVI). The results of the WCM model illustrate that all the models show acceptable results, which confirms that this vegetative descriptor can be useful to estimate the accurate soil moisture over the wheat crop in the study area, except for DPSVI. Furthermore, the results revealed that model performances gradually decrease as the crop enters the complex stages. In summary, the overall finding demonstrates that PRVI outperformed other models in terms of statistical indicators value for calibration (R2 = 0.728, NSE = 0.727, PBIAS = - 2.67%, and RMSE = 2.985%) and validation (R2 = 0.728, NSE = 0.684, PBIAS = - 13.666%, and RMSE = 4.106%). Thus, overall results proved that the WCM model has considerable potential to retrieve SSM over wheat crops from Sentinel-1 satellite data. This study will be beneficial for regional water resources managers for proper allocation of irrigation water, effective irrigation management, and enhancedirrigation efficiency within the regions.

Comparative analysis of deep neural network and artificial neural network for predicting wheat water content using UAV multispectral and thermal imagers

Water is a vital component for the growth of wheat and the quality of its grains. The measurement of plant water content (PWC) serves as a crucial indicator for assessing the water status of crops, thereby guiding effective irrigation management practices. Recent advancements in technology, particularly the use of multispectral and thermal imaging from unmanned aerial vehicles (UAVs), offer the capability to capture a comprehensive view of PWC variability across agricultural fields. This study aimed to create predictive models for PWC utilizing artificial neural networks (ANN), deep neural networks (DNN), and traditional stepwise regression techniques, all based on the analysis of multispectral and thermal imagery. To effectively evaluate the protein content in wheat, we combined high-resolution thermal and multispectral imaging techniques with machine learning approaches. This assessment was carried out through three distinct experiments, which included one conducted in a rainout shelter and two performed under rainfed conditions. The findings demonstrated that UAV-derived multispectral imagery, when coupled with machine learning models, can effectively predict wheat plant water content with remarkable precision. Notably by considering all the dataset, DNN model exhibited superior performance (R2=0.96, ENS=0.98, RMSE=1.37%, MAE=0.98%) compared to both the ANN (R2=0.95, ENS=0.95, RMSE=1.88%, MAE=1.46%,) and the stepwise regression model (SRM) (R2=0.67, ENS=0.51, RMSE=10.79%, MAE=9.03%). Across all machine learning approaches, both the DNN and ANN models significantly outperformed the stepwise regression model in predictive accuracy. The statistical outcomes derived from the calibration phase indicate that both the trained Artificial Neural Networks (ANN) and Deep Neural Networks (DNN) serve as effective instruments for accurate prediction of PWC. Notably, the DNN network demonstrates superior accuracy compared to ANN.

Effect of Varying Irrigation Levels on Growth and Yield of Serengeti and Theresa French Bean Cultivars: A Case Study in Huye District, Rwanda

Effective irrigation management is essential for maximizing crop yield and quality while conserving water resources. This study evaluated the impact of varying irrigation levels on the growth and yield of two French bean cultivars, Serengeti and Theresa, conducted in 2024 at the Agricultural Research Center of Rwanda Polytechnic–Huye College. Key objectives included determining crop water requirements, establishing irrigation schedules based on daily soil water balance, and examining the effects of three irrigation regimes (Full irrigation (100% ETc), Deficit irrigation( 50% ETc), and Rainfed as Control on yield parameters across both cultivars. The experiment followed a split-plot design in a Randomized Complete Block Design with three replications. Soil moisture levels were monitored using PR2- Moisture Sensors. The results of conducted field experiments revealed that the seasonal irrigation water requirements for full irrigation, deficit irrigation, and rainfed treatments were 111.38 mm, 99.5 mm, and 24.5 mm, respectively. Seasonal evapotranspiration rates were measured at 160.4 mm for full irrigation, 143.9 mm for deficit, and 80.84 mm for rainfed. The fresh pod yield ranged between 5.9 – 11.7 tons ha⁻¹ for Serengeti and 5.3 – 20.3 tons ha⁻¹ for Theressa, while dry matter yields varied from 1.0 – 1.66 tons ha⁻¹ for Serengeti and 0.8 – 2.88 tons ha⁻¹ for Theressa. Plant height ranged from 22 cm to 40.1 cm, and pod lengths for Serengeti and Theressa were between 9.3 – 17.64 cm and 8.8 – 15.8 cm, respectively. The mean number of grains per pod was 7. Yield response factors (ky) for Serengeti and Theressa were 1.029 and 1.20, respectively. The highest crop evapotranspiration productivity (WPET) and water productivity (WP-irrigation) were recorded for Theressa at 127.2 kg ha⁻¹ mm⁻¹ and 135.6 kg ha⁻¹ mm⁻¹, respectively. Monitoring soil moisture balance highlights the economic advantages of efficient irrigation techniques tailored to local conditions.

Impact of Salinity Stress on Soybean Growth and Yield under Saturated Soil Culture in Tidal Lands: A Comparative Study of Tolerant Varieties

Salinity stress, intensified by climate change events such as El Niño and drought, presents a significant challenge to soybean production in tidal lands. This study evaluated soybean varieties’ growth, tolerance, and yield under varying salinity conditions within a saturated water cultivation system. The experiment was conducted from February to May 2024 at the IPB Experimental Station in Leuwikopo, Bogor, Indonesia, using soil samples collected from type B tidal lands in Mulyasari Village, Banyuasin, South Sumatra. A completely randomized design (CRD) was employed with three factors and three replications each. The first factor was soybean variety (“Demas-1” and “Detap-1”), the second was soil salinity (0 and 2000 ppm NaCl), and the third was irrigation salinity at different growth stages (control, 2000 ppm NaCl before/during flowering, and 2000 ppm NaCl after flowering). The results demonstrated that the “Demas-1” variety exhibited superior growth characteristics, including higher leaf greenness, dry weight of root nodules, and number of filled pods per plant. Exposure to soil salinity of 2000 ppm NaCl led to a significant reduction in plant height (29.38%), leaf number (38.01%), leaf greenness (28.67%), dry weight (49.90%-60.80%), and filled pods per plant (55.51%), while increasing plant toxicity (108%). Irrigation with 2000 ppm NaCl further exacerbated these negative impacts, resulting in decreased leaf greenness (15.42%-18.06%) and filled pods per plant (17.84%-23.94%). The interaction between soybean variety, soil salinity, and irrigation salinity significantly influenced the number of filled pods per plant. The combination of any soybean variety with 2000 ppm NaCl resulted in a reduction of filled pods per plant. Moreover, applying saline irrigation after flowering to saline soil decreased the number of filled pods per plant by 64.68%. These findings highlight the critical importance of selecting tolerant soybean varieties and implementing effective irrigation management strategies to mitigate the adverse effects of salinity on soybean production in tidal lands.

Evaluating the impact of weather forecasts on productivity and environmental footprint of irrigated maize production systems

Integrating weather forecasts into decision support systems empowers farmers to optimise irrigation schedules, thereby boosting crop yields and conserving water. However, inaccurate forecasts can jeopardise productivity and irrigation efficiency. This study combines a crop model with a stochastic pseudo-weather forecast algorithm to: (1) determine the reliability needed in a weather forecast algorithm for effective irrigation management; and (2) assess the impact of weather forecast reliability on the productivity and environmental footprint of various maize cropping systems across diverse climates. It employs the Next Generation of Agricultural Production Systems sIMulator (APSIM NextGen) to simulate maize growth at eleven locations representing diverse climates globally. Various planting schedules, soil types, irrigation systems, and nitrogen availability levels were considered to examine the effects of perfect and imperfect weather forecasts. The findings underscore the potential of integrating weather forecasts into irrigation management for enhanced productivity and sustainability. High-confidence forecasts and longer lead times increase yields (up to 11 %) and improve sustainability outcomes, particularly in wetter climates and for conditions with low nitrogen availability. Conversely, when the accuracy of forecasts is low, forecast-driven irrigation management may lead to yield reductions compared to a baseline system, especially in drier climates (up to 26 % reduction), necessitating tailored management strategies. Soil type and farmer's risk tolerance further influence the effectiveness of forecast-driven irrigation management, emphasising the need for context-specific approaches. By understanding and leveraging the interconnected impacts of weather forecasts on yield, water use efficiency, nitrogen loss, and greenhouse gas emissions, farmers can optimise productivity while minimising environmental impacts.

Cultivating sustainability: a multi-assessment of groundwater quality and irrigation suitability in the arid agricultural district of Dzira (Ksour Mountains, Algeria).

Groundwater serves a range of essential functions such as supplying drinking water, facilitating agricultural practices, and supporting industrial processes. This study examines with multiple methods the quality of groundwater in the agricultural region of Dzira, Algeria. By collecting 38 groundwater samples of different wells and boreholes, valuable awareness of the aptness of groundwater for irrigation in this arid landscape was gained. Most wells met Food and Agriculture Organization (FAO) criteria for the total dissolved solids (TDS) and the potential of hydrogen pH, but some areas had higher mineral content and electrical conductivity. Results show significant TDS variations, with 10.81% of wells exceeding limits and acceptable pH levels. Elevated EC values in 67.57% of wells show high salinity, affecting soil and plant growth. Major ions such as Mg2+ and SO4- exceeded FAO standards in 43.24% and 64.86% of wells, respectively, highlighting substantial mineral content in the groundwater. Suitability indices reveal that most wells pose low sodium hazards and are generally suitable for irrigation, though some areas face moderate to high restrictions. The irrigation water quality index (IWQI) ranged from 45.36 to 96.30, averaging 80.77, with 54.04% classified as "low restriction," suitable for sandy soils with good permeability but requiring caution on salt-sensitive soils. Hydrogeochemical analysis using principal component analysis (PCA) and hierarchical cluster analysis (HCA) identifies rapid evaporite dissolution from Triassic saline formations, with a correlation matrix showing associations between TDS and Ca2⁺, Mg2⁺, Na⁺, Cl⁻, and SO₄2⁻. This mineralization is likely from gypsum and halite. Zoning maps based on IWQI and other parameters depicted spatial variations in groundwater quality, guiding effective irrigation management strategies. Overall, the study underscores the importance of comprehensive water quality assessment for sustainable agriculture and emphasizes the need for targeted interventions to mitigate potential challenges associated with soil salinity and sodicity. Therefore, these findings can be useful to decision-makers and stakeholders in order to optimize water use and protect this vital resource.

IMAGE PROCESSING-DRIVEN DEEP LEARNING MODEL FOR PLANT DISEASE DETECTION TO ENHANCE IRRIGATION EFFICIENCY IN SMART AGRICULTURE

In the era of smart agriculture, efficient irrigation management is crucial for optimizing crop yield and resource use. Traditional methods of plant disease detection often rely on manual inspection, which is time-consuming and prone to errors. Smart agriculture leverages technology to improve agricultural practices. Accurate and timely plant disease detection is vital for effective irrigation management and overall crop health. Current methods are limited by their manual nature and inability to process large volumes of data quickly. Manual plant disease detection is labor-intensive and may not provide timely information, leading to inefficient irrigation practices. This inefficiency can result in reduced crop yield and wasted resources. LeNet integrates advanced image processing techniques with a deep learning architecture tailored for plant disease detection. The model utilizes convolutional neural networks (CNNs) to analyze plant leaf images, identifying disease patterns with high precision. LeNet incorporates preprocessing steps such as image normalization and augmentation to enhance model robustness. The network is trained on a comprehensive dataset of plant disease images, employing transfer learning to leverage pre-trained weights for improved accuracy. Evaluation of LeNet on a test dataset comprising 10,000 images demonstrated an impressive accuracy of 92.5%, with a precision of 90.3% and recall of 94.1%. The model significantly outperforms traditional methods, reducing disease detection time by 60% and enhancing irrigation efficiency by 30%. The reduction in water usage and increased crop yield were observed in practical trials.

Determination of threshold crop water stress index for sub-surface drip irrigated maize-wheat cropping sequence in semi-arid region of Punjab

A thorough understanding of how crops respond to water stress is essential for effective irrigation management under changing climate conditions and declining water resources. Amongst the various methods of irrigation, crop water stress index (CWSI)-based irrigation is popular due to its non-invasive techniques that require less data and can precisely indicate the severity of crop water deficiency. The present study focuses on establishing the upper- and lower-baselines of canopy-air temperature difference (Tc-Ta) for the estimation of the CWSI and to determine the CWSI threshold for maize and wheat crops. The upper- and lower-baseline of (Tc-Ta) was established using the relationship between (Tc-Ta) and vapor pressure deficit (VPD) for different irrigation treatments such as 60 %ETc, 70 %ETc, 80 %ETc, 90 %ETc, and 100 %ETc with irrigation interval of 1-day, 2-days, and 3-days. The study showed that the baselines varied with the growth stages of the crop and with irrigation treatments and reached the maximum in Anthesis and Silking growth stage for wheat and maize, respectively. The threshold of CWSI was estimated by developing a relationship between the normalized values of grain yield (GY) and irrigation water productivity (WPI). For the Rabi wheat, a quadratic correlation was identified between CWSI and WPI (r2 = 0.89), and GY (r2 = 0.93). Similarly, for Kharif maize, a quadratic relationship was noted between CWSI and both WPI (r2 = 0.75) and GY (r2 = 0.85). From the study, it was found that the CWSI thresholds of Rabi wheat and Kharif maize are 0.29 and 0.31, respectively. Based on the study, it can be concluded that by utilizing the thresholds for a winter wheat-summer maize cropping system, high GY and WPI can be achieved.

Soybean (Glycine max L.) Leaf Moisture Estimation Based on Multisource Unmanned Aerial Vehicle Image Feature Fusion.

Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021-2022), soybean (Glycine max L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients (p < 0.05) were selected as input variables for the model (combination 1: vegetation indices; combination 2: texture features; combination 3: randomly extracted texture indices in combination; combination 4: combination of vegetation indices, texture features, and randomly extracted texture indices). Subsequently, extreme learning machine (ELM), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN) were utilized to model the leaf moisture content. The results indicated that most vegetation indices exhibited higher correlation coefficients with soybean leaf moisture compared with texture features, while randomly extracted texture indices could enhance the correlation with soybean leaf moisture to some extent. RDTI, the random combination texture index, showed the highest correlation coefficient with leaf moisture at 0.683, with the texture combination being Variance1 and Correlation5. When combination 4 (combination of vegetation indices, texture features, and randomly extracted texture indices) was utilized as the input and the XGBoost model was employed for soybean leaf moisture monitoring, the highest level was achieved in this study. The coefficient of determination (R2) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth.

Soil-Specific Calibration Using Plate Compression Filling Technique and Monitoring Soil Biomass Degradation Based on Dielectric Properties

Accurate estimation of soil water content (SWC) is crucial for effective irrigation management and maximizing crop yields. Although dielectric property-based SWC measurements are widely used, their accuracy is still affected by soil variability, soil–sensor contact, and other factors, making the development of convenient and accurate soil-specific calibration methods a major challenge. This study aims to propose a plate compression filling technique for soil-specific calibrations and to monitor the extent of soil biomass degradation using dielectric properties. Before and after biodegradation, dielectric measurements of quartz sand and silt loam were made at seven different water contents with three different filling techniques. A third-order polynomial fitting equation explaining the dependence of the dielectric constant on the volumetric water content was obtained using the least-squares method. The suggested plate compression filling method has a maximum mean bias error (MBE) of less than 0.5%, according to experimental results. Depending on the water content, silt loam’s dielectric characteristics change significantly before and after biodegradation. The best water content, measured in gravimetric units, to encourage the decomposition of biomass was discovered to be 24%. It has been demonstrated that the plate compression filling method serves as a simple, convenient, and accurate alternative to the uniform compaction method, while the dielectric method is a reliable indicator for evaluating biomass degradation. This exploration provides valuable insights into the complex relationship between SWC, biomass degradation, and soil dielectric properties.

Growth And Yield Of Tomato As Influenced By Water Stress At Different Phenological Stages

The study investigates the effect of water stress at different phenological stages on tomato growth and yield. Tomato is a vital vegetable crop in Nigeria, and water scarcity poses challenges to its productivity. The study was carried out in a screen house, water was applied daily with 100% potential evapotranspiration rate except during the ten days water stress applied at every phenological stage. The parameters assessed include number of leaves, plant height (cm), stem diameter (mm), and root and shoot dry weights (g), in addition, the water use efficiency. The results were subjected to Analysis of Variance and the means were compared using Least Significant Difference at 5% level of significance. The results show that there was no significant difference (p≥0.05) in the water use efficiency of the tomato plants subjected to water stress at the phenological stages. Notably, the study highlights that the vegetative stage is particularly sensitive to water stress, leading to reduced shoot dry weight and compromised overall biomass. Similarly, water stress during the flowering stage diminishes root dry weight. However, the fruiting stage exhibits relatively better yields under water stress than other stages. The findings emphasize the importance of effective irrigation management, particularly during the vegetative phase, to promote optimal plant development. Moreover, the research underscores the significance of providing adequate water during reproductive phases to enhance fruit production and overall plant performance.&#x0D;

Estimation of ET and Crop Water Productivity in a Semi-Arid Region Using a Large Aperture Scintillometer and Remote Sensing-Based SETMI Model

With the increasing water scarcity and the demand for sustainable agriculture, precise estimation of crop evapotranspiration (ET) is crucial for effective irrigation management, crop yield assessment, and equitable water distribution, particularly in semi-arid regions. In this study, a large aperture scintillometer (LAS) was used to validate the remote sensing-based ET model SETMI (Spatial Evapotranspiration Modeling Interface) in an irrigated maize-wheat cropping system in a semi-arid region at the ICAR-Indian Agricultural Research Institute, New Delhi. Results obtained by the SETMI model depicted modeled surface energy fluxes compared well with LAS field data, showing a very high R2 (0.83–0.95) and NRMSE (8–29%). The SETMI model performed better in the case of the maize crop than the wheat crop in field experiments. Further, the SETMI model was employed at the regional level using high-resolution Sentinel-2 to estimate the regional water productivity of wheat crops over a semi-arid region in India. The estimated regional, seasonal wheat actual ET mainly ranged between 101 mm and 325 mm. The regional wheat water productivity varied from 0.9 kg m−3 to 2.20 kg m−3. Our research reveals that the SETMI model can give reliable estimates of regional wheat water productivity by examining its spatial and temporal fluctuations and facilitating the creation of regional benchmark values.

Reducing cadmium accumulation and increasing grain yield with proper timing of flood irrigation in the second season of ratoon rice

Context or problemThe second season of ratoon rice (ratoon crop, RC) exhibits a high risk of cadmium (Cd) contamination in grains, requiring effective irrigation management strategies to reduce its grain Cd concentration. Objective or research questionThis study aimed to determine the crucial growing period for Cd accumulation and provide irrigation recommendations for reducing grain Cd concentrations with limited water input in the ratoon season. MethodsA two-year field experiment was conducted in Hubei province, central China. The entire growth duration of RC was divided into three short periods: from harvest of the main crop (HM) to initial heading of RC (IH), from IH to full heading of RC (FH), and from FH to maturity of RC (MR). During each growth period of RC, two water regimes (flooded and rainfed) were established. Therefore, there were a total of eight water treatment combinations in this experiment, using a randomized complete block design. Two hybrid varieties were planted in this study. ResultsAcross years, varieties and treatments, the grain Cd concentration of RC ranged from 27.8 to 314.1 µg kg−1, with 37.5% of rice samples exceeding the national food safety standard for Cd (200 µg kg−1). RC grown under continuous flooding conditions produced the lowest grain Cd concentration and highest grain yield. Compared with rainfed conditions, maintaining flooded conditions from HM to IH, from IH to FH, and from FH to MR resulted in decreases of 28.4%, 62.7%, and 12.0% in grain Cd concentration and decreases of 25.5%, 49.9%, and 16.3% in total Cd accumulation at maturity, respectively. RC took up the most Cd during the period from IH to FH under rainfed conditions; however, maintaining flooded conditions reduced Cd uptake by 95.4% during this period. Moreover, flooding from IH to FH had the most positive impact on the grain yield of RC, with an increase of 8.1% compared with rainfed conditions. ConclusionsMaintaining flooded conditions from initial to full heading for 15–18 days was not only effective in reducing grain Cd concentration but also beneficial for improving grain yield in the ratoon season. Implications and significanceThis study clarified the effects of water management during different growing periods on Cd accumulation and grain yield in the ratoon season. We emphasized the necessity of flooding during heading period to control Cd accumulation in RC grains, especially in areas with limited water resources and high soil Cd concentration.

Predicting tomato water consumption in a hydroponic greenhouse: contribution of light interception models

In recent years, hydroponic greenhouse cultivation has gained increasing popularity: the combination of hydroponics’ highly efficient use of resources with a controlled environment and an extended growing season provided by greenhouses allows for optimized, year-round plant growth. In this direction, precise and effective irrigation management is critical for achieving optimal crop yield while ensuring an economical use of water resources. This study explores techniques for explaining and predicting daily water consumption by utilizing only easily readily available meteorological data and the progressively growing records of the water consumption dataset. In situations where the dataset is limited in size, the conventional purely data-based approaches that rely on statistically benchmarking time series models tend to be too uncertain. Therefore, the objective of this study is to explore the potential contribution of crop models’ main concepts in constructing more robust models, even when plant measurements are not available. Two strategies were developed for this purpose. The first strategy utilized the Greenlab model, employing reference parameter values from previously published papers and re-estimating, for identifiability reasons, only a limited number of parameters. The second strategy adopted key principles from crop growth models to propose a novel modeling approach, which involved deriving a Stochastic Segmentation of input Energy (SSiE) potentially absorbed by the elementary photosynthetically active parts of the plant. Several model versions were proposed and adjusted using the maximum likelihood method. We present a proof-of-concept of our methodology applied to the ekstasis Tomato, with one recorded time series of daily water uptake. This method provides an estimate of the plant’s dynamic pattern of light interception, which can then be applied for the prediction of water consumption. The results indicate that the SSiE models could become valuable tools for extracting crop information efficiently from routine greenhouse measurements with further development and testing. This, in turn, could aid in achieving more precise irrigation management.

Evapotranspiration and Yield Impact Tools for More Water-Use Efficient Alfalfa Production in Desert Environments

Drought and climate change have decreased water availability for agriculture, especially in the desert of southwestern USA. Efficiency enhancements in irrigation management aimed at conserving water are key to adjust to limits in water supply, improve profitability and sustainability of alfalfa production in arid and semiarid areas. This study intended to conduct a field-scale analysis to develop yield and ET estimation tools for the effective use of irrigation water in a desert alfalfa production system. Extensive data collection and trials were carried out over three years in nine fully irrigated commercial alfalfa fields in the low deserts of California. The seasonal crop water consumption measured using the residual of energy balance method varied from 1381 mm to 1596 mm across the experimental sites and crop seasons. Variable seasonal dry mater (DM) yields ranged from 23.01 Mg ha−1 to 29.90 Mg ha−1. The results indicated that the first five cuttings each year were the most productive cuttings with a mean DM value ranged between 3.29 (cut 1) and 4.21 (cut 4) Mg ha−1 but declined in later cuttings. An average annual water productivity (WP) value of 17.0 kg ha−1 mm−1 was determined across the sites varying from 15.5 to 18.9 kg ha−1 mm−1. The findings suggested that one may lose up to 1.44 Mg ha−1 alfalfa yields with moderate summer deficit irrigation strategies, using 40% less water applied than full irrigation practices over the summer period of July–September. A more severe summer water deficit, with no irrigation event over the summer period of July–September may result in a potential water savings of 0.234–0.246 (ha·m) ha−1 and 19–21% seasonal yield losses in the desert environment. This study describes the seasonal yield pattern, the crop water use-production function, and the crop coefficient values for various harvest cycles over the crop season. These tools may assist farmers to quantify water savings and estimate yield losses for more accurate and effective irrigation management strategies to meet water conservation objectives and for the resiliency of alfalfa production in the desert region.

The Interactive Effects of Deficit Irrigation and Bacillus pumilus Inoculation on Growth and Physiology of Tomato Plant.

The effects of inoculating plant growth promoting rhizobacteria (PGPR) and soil water deficits on crop growth and physiology remain largely unknown. Here, the responses of leaf gas exchange, growth, and water use efficiency (WUE) of tomato plants to Bacillus pumilus (B.p.) inoculation under four irrigation strategies (I1-I4) were investigated in a greenhouse. Results showed that soil water deficits, especially at I4 (20%, v/v), significantly decreased leaf stomatal conductance (gs), transpiration rate (Tr), and photosynthetic rate (An), and the decrease of gs and Tr were more pronounced than An. Reduced irrigation regimes significantly lowered dry matter and plant water use both in the non-B.p. control and the B.p. plants, while reduced irrigation significantly increased plant WUE, and B.p. inoculation had little effect on this parameter. Synergistic effects of PGPR and deficit irrigation on leaf gas exchange, leaf abscisic acid content, and stomatal density were found in this study, and specifically, B.p. treated plants at I4 possessed the highest WUE at stomatal and leaf scales, suggesting that B.p. inoculation could optimize water use and partly alleviate the negative effects of soil water deficit. These findings provide useful information for effective irrigation management and the application of PGPR in agriculture in the future.

Comprehensive evaluation of the optimal rates of irrigation and potassium application for strawberry

Accurate and effective management of irrigation and fertilization is essential for efficient greenhouse strawberry production. Here, the effects of the combined application of water and potassium on strawberry growth were evaluated by experimenting with 12 treatments, including four transpiration (ETC)-based irrigation levels (W1: 100% ETC, W2: 85% ETC, W3: 70% ETC, and W4: 55% ETC) and three potassium levels according to the target yield (K1: 369.5 kg/hm2, K2: 307.9 kg/hm2, and K3: 246.3 kg/hm2). Various indexes of strawberry yield, fruit quality, water and fertilizer utilization efficiency, and soil nutrient residues were measured. Irrigation and potassium application had significant effects on strawberry production. Higher irrigation levels increased yield (Y), and moderate water and potassium application increased single fruit weight (SFW), water utilization efficiency (WUE), total soluble sugar (TSSC), and the sugar-acid ratio (SAR). While low-application of potassium increased the soluble protein content (SP), partial factor productivity of potassium (PFPK), and reduce nutrient residues in soil effectively. A comprehensive system for evaluating strawberry growth and its benefits to the soil environment was developed. The FAHP and CRITIC methods were used to calculate the subjective weight and objective weight of each index, respectively. The largest subjective weight was observed for Y (0.200), and the largest objective weight was observed for soil-available potassium (0.101). The final weight was determined using Game theory; Y had the highest weight (0.185), and free amino acids (FAA) had the lowest weight (0.047). Grey relational analysis (GRA) was used to evaluate the optimal irrigation and potassium scheme for accomplishing multiple objectives. The response of the comprehensive score of strawberries to irrigation and potassium exhibited a negative parabolic relationship, and the effect of irrigation was greater than the effect of potassium application. There was a significant interaction between irrigation and potassium application, and an irrigation amount of 2053-2525 m3/hm2 with a potassium application rate of 288.1-334.2 kg/hm2 was optimal for promoting strawberry yield, fruit quality, and efficiency and reducing soil nutrient residues. The results of this study provide new insights that could aid the development of sustainable approaches for enhancing agricultural production. Keywords: strawberry, irrigation, potassium, FAHP, CRITIC, nutrient residues DOI: 10.25165/j.ijabe.20231602.7591 Citation: Yang X Q, Du R C, Zhang M C, Feng H W, Wang Z Q, Zhang Z. Comprehensive evaluation of the optimal rates of irrigation and potassium application for strawberry. Int J Agric & Biol Eng, 2023; 16(2): 65-72.