Optimal Irrigation Scheduling Research Articles

Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective water management. Through a comprehensive review of the literature and an in-depth analysis of various AI and ML techniques, this paper aims to put light on the cutting-edge approaches used in irrigation scheduling predictive modeling. The goal of the research is to determine the advantages, disadvantages, and future directions of AI and ML-based irrigation management systems by means of a methodical analysis of various algorithms, data sources, and applications. Additionally, the study seeks to demonstrate how data-driven methods can enhance irrigation systems' sustainability, accuracy, and precision. Stakeholders in agriculture, water resource management, and environmental conservation can make well-informed decisions to maximize irrigation scheduling techniques by having a thorough understanding of the theoretical underpinnings and practical applications of predictive models. The study also attempts to tackle issues like scalability, model interpretability, and lack of data when implementing AI and ML solutions for practical irrigation management. In final form, this review's conclusions advance our understanding of how to use AI and ML to improve agricultural systems' resilience and water use efficiency, supporting adaptive and sustainable water management strategies in the face of rising water scarcity concerns and climate change. Design/Methodology/Approach: In order to gather information for this review study, several research articles from reliable sources were analyzed and compared. Objective: To provide the current research gaps in prediction models for the best irrigation scheduling and water management, and suggest using AI and ML techniques to fill in these gaps. Results/ Findings: In response to the growing challenges of water scarcity and climate change, the paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies. Originality/Value: This paper's uniqueness and significance come from its thorough analysis of AI and ML approaches in predictive modeling for ideal water management and irrigation scheduling. It also provides insights into new methods and their possible effects on resource optimization and agricultural sustainability. Type of Paper: Literature Review.

Artificial Intelligence in Water Management for Sustainable Farming: A Review

Artificial Intelligence (AI) is capable of enhancing water management for sustainable farming. The growing demand for agricultural productivity and sustainability in the context of finite water resources and climate change drives the necessity for more efficient water management practices. AI technologies, through automated and precision irrigation systems, AI-based predictive models, and AI-driven water quality monitoring, offer significant improvements in water efficiency and agricultural output. These systems optimize irrigation scheduling based on real-time data, enhance the precision of water application, and ensure water quality, thus supporting sustainable agricultural practices. However, the implementation of AI in water management is not without challenges. Technical difficulties in adapting AI to diverse agricultural environments, data privacy and security concerns, ethical considerations, and barriers to adoption among small-scale farmers are critical issues that need addressing. This study addresses both the transformative impacts and the inherent challenges of integrating AI technologies. Furthermore, the review identifies a gap in research regarding AI’s adaptability to variable climates and its integration with socio-economic data, suggesting that future studies focus on these areas. Policy recommendations are also discussed, emphasizing the need for developing standards and best practices, increasing funding and incentives for AI research, promoting training and capacity building, and establishing robust regulatory frameworks for data management. By tackling these challenges and leveraging AI’s full potential, water management in agriculture can be significantly improved, leading to enhanced global water security and sustainability in farming practices. The review concludes that while AI presents a promising future for agricultural water management, strategic and thoughtful approaches are required to overcome obstacles and fully realize the benefits of this technology.

AI ENABLED WATER CONSERVATION FOR IRRIGATION USING IOT

This project introduces a smart irrigation system that utilizes Internet of Things (IoT) technology and machine learning algorithms to enhance water management in agriculture. The system employs a series of IoT sensors distributed across the field to consistently monitor key environmental factors such as soil moisture levels, temperature, humidity, and others. The collected data is analysed using machine learning Cat Boosting algorithm. This algorithm analyze the data to determine the optimal irrigation schedule based on crop water requirements, soil conditions, weather forecasts, and historical data. The system controls irrigation equipment such as pumps, valves, and sprinklers to deliver precise amounts of water to crops at the right time. Continuous feedback from the system allows for refinement of irrigation schedules, leading to improved water conservation, increased crop yield, and cost savings for farmers. This project discusses the benefits of such a system, including water conservation, increased crop yield, cost savings, environmental sustainability, and remote monitoring and control capabilities. Overall, the integration of IoT and machine learning technologies offers a powerful solution for sustainable agriculture, enabling data-driven decision-making processes to optimize water usage and maximize crop productivity. Keywords— Smart irrigation system, Internet of Things(IOT)technology, Water conservation, Environmental parameters, Soil moisture monitoring, Increased crop yield, Machine learning algorithms

Impact of Fruit Load on the Replenishment Dynamics of Internal Water Reserves in Olive Trees

Stem refilling has been studied in many forest species, but its impact on olive trees remains underexplored. This study aims to examine the effect of varying fruit loads on stem refilling rates in olive trees. The experiment was conducted in a commercial olive orchard over two years, characterized by a biennial bearing cycle with minimal fruit production in 2021 (“OFF” year) and maximal fruit production in 2022 (“ON” year). Sap flow sensors measured the water volume differences traversing the apex and base of main branches in two experimental trees. Tree water status was monitored using psychrometers, and soil moisture content was continuously recorded. Results suggest that alternate bearing significantly affects the stem refilling process, while soil moisture availability also plays a pivotal role. During the “ON” year, water reserve consumption increased to 63.6% compared to the “OFF” year to meet the water requirements of developing fruits. Replenishment occurred at various times throughout the 24 h period, including early morning, afternoon, and night when stomatal conductance measurements indicated stomatal closure. During the “ON” year, olive trees experienced intense nocturnal replenishment of reserves, regardless of soil moisture, water vapor pressure deficit, or fruit development stage. These findings offer novel insights into olive trees’ rehydration dynamics that can be used to optimize irrigation scheduling and improve water use efficiency.

Determination of Optimum Irrigation Scheduling for Onion (Allium Cepa L.) at Bena Tsemay District, Southern Ethiopia

The effective use of irrigation methods is crucial for increasing crop productivity and promoting thesustainable use of agricultural land. Therefore, increasing agricultural water productivity is one of the most importantways to counteract future water scarcity. A field experiment was conducted to determine the optimal irrigation regimefor onion cultivation in Bena Tsemay district for two consecutive years. The experiment was laid out in a randomizedcomplete block design with five irrigation treatments and four replications. The treatments included five levels ofirrigation regimes: 60%, 80%, 100%, 120% and 140% available soil moisture depletion levels. The results showed thatirrigation regimes had a significant effect on plant height, bulb diameter, marketable yield and total yield, with a p-value of less than 0.05. The highest marketable yield (30.72 kg/ha) was observed at 100%ASMDL, followed by (30.49kg/ha) at 80%ASMDL. However, irrigation at 140%ASMDL resulted in the lowest yields of both marketable and totalonion. The two-year combined analysis revealed that the highest (6.5 kg/m3) water productivity was recoreded at 100%ASMDL, followed by (6.45 kg/m3) at 80% ASMDL. In contrast, the lowest water productivity (3.21 kg/m3) wasrecorded at 140% ASMDL. Therefore, based on the results of the current trial, it is recommended that the use of 80%ASMDL for a furrow irrigation system, and in similar agro ecology is a crucial option to increase the yield and waterproductivity of onion

Investigating the compatibility of an irrigation decision support system with water rights and allocation in a selected irrigation network

AbstractIt is necessary to use different planning models, including decision support systems (DSSs), to allocate water resources. For this purpose, in this study, an irrigation decision support system (IDSS) was developed to improve irrigation management in the farming fields of Mahabad Plain located to the south‐east of Lake Urmia. Next, the compatibility of the IDSS with the conditions of the Mahabad irrigation and drainage network, water and soil resources, meteorological data and soil moisture (SM) were investigated. The statistical indices of coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (EF) and Wilmot agreement (d) were used to evaluate the adaptability of the IDSS. The results showed that the IDSS has reasonable compatibility with soil and water resources, crop yield and meteorological data. Irrigation scheduling provided by the IDSS led to a 13.9% reduction in water consumption and a 6.7% increase in crop yield. The IDSS estimated minimum and maximum temperature and sunshine hours to a satisfactory degree and relative humidity with an acceptable degree (NRMSE = 0.72–0.77) compared to regional synoptic station data. The performance of the IDSS in simulating SM is ranked from good to well (NRMSE = 0.75–0.83). The results indicate that the IDSS has a sufficient performance in estimating meteorological and soil moisture data with R2 = 0.90, RMSE = 4.65, NRMSE = 0.78, EF = 0.76 and d = 0.80. In addition, the IDSS provides the optimal irrigation schedule by considering the ability to deliver water from the irrigation and drainage network to the third‐grade canal and agricultural fields as the upstream condition.

Assessing zero‐till direct seeding at variable water stress levels compared to traditional puddled transplanting of rice under groundwater‐fed irrigation systems in north‐west India

AbstractIrrigation of rice using groundwater is considered one of the main contributors to north‐west India's declining water level. The present study hypothesizes that zero‐till direct seeding of rice (ZTDSR) with the optimum irrigation schedule may reduce irrigation compared to puddled transplanted rice (PTR). Crop growth stage‐dependent predefined soil matric potential (SMP), that is, −15, −30 and −45 kPa based irrigation schedules either during the entire growing period or their combinations during the vegetative phase in ZTDSR, were compared with PTR for two consecutive seasons. The results showed that irrigation in ZTDSR at lower SMP at any growth stage caused adverse effects on yield. Irrigation at −15 kPa during the entire crop season with straw mulch was found to be the best schedule for ZTDSR. ZTDSR with −15 kPa irrigation, however, saved 36.2 cm of water and recorded higher water productivity but produced 20% less grain yield over the prevailing PTR. A higher groundwater system loss (GWSL) was found in the PTR (29.2 cm) than in the best ZTDSR (23.6 cm) schedule, which indicates better groundwater management in the ZTDSR than in the PTR. Hence, the ZTDSR has the potential to save irrigation, achieve higher water productivity and manage the depletion of groundwater resources in rice–wheat dominant north‐west India.

Determination of Evapotranspiration and Crop Coefficients of Irrigated Legumes on Different Soil Textures Using the FAO56 Approach

Climates and locations specific crop coefficients (Kc) plays pivotal roles in effective water management and crop yield optimization. Research on Kc for many staple crops remains limited in many regions across Africa. This study examined the crop coefficients of two varieties of cowpea (Sampea 14 and Sampea 17) and a variety of soybean (TGX 1465-1D) across different soil textures for optimized water management and crop yield. This study ascertained the Kc of Sampea14 (V1), Sampea17 (V2), and TGX 1465-1D (V3) cultivated on sandy, sandy clay loam, and sandy loam soils, within Port Harcourt, Southern Nigeria. This formed a 3 by 3 factorial experiment in three replicates. Changes in soil water content and crops’ agronomic parameters were monitored regularly. Meteorological data were obtained from the National Aeronautic and Space Administration website and reference evapotranspiration were estimated from the meteorological data using the FAO Penman Monteith’s method. Kc were estimated from the actual crop water use and reference crop evapotranspiration using the FAO56 method. During the initial-growth stage, the average Kc for V1, V2, and V3 were computed as 0.55, 0.72, and 0.81, respectively. At the mid-growth stage, these coefficients increased to 1.48, 1.30, and 1.60 and at the late season, the average Kc were 0.80, 0.69, and 0.86, for the respective varieties. The findings from this study hold the potential to significantly contribute to proper water management practices in the studied area. The understanding of Kc for these specific legume varieties across different soil textures offers valuable insights for optimizing irrigation scheduling and ultimately enhancing crop yield and water productivity.

Effects of irrigation scheduling on the yield and irrigation water productivity of cucumber in coconut coir culture

Optimum irrigation scheduling is important for ensuring high yield and water productivity in substrate-cultivated vegetables and is determined based on information such as substrate water content, meteorological parameters, and crop growth. The aim of this study was to determine a precise irrigation schedule for coconut coir culture in a solar greenhouse by comparing the irrigation, evapotranspiration (ET), substrate water content (VWC), as well as the crop growth indices and yield of cucumber, and irrigation water productivity (IWP) under three irrigation schedules: the soil moisture sensor-based method (T-VWC), the accumulated radiation combined with soil moisture sensor-based method (Rn-VWC), and the crop evapotranspiration estimated method using the hourly PM-ETo equation with an improved calculation of Kc (T-ETc). The results showed that the daily irrigation and evapotranspiration amount were the highest under T-VWC treatment, while the lowest under T-ETc treatment. In different meteorological environments, the change in irrigation amount was more consistent with the ET,and the VWC was relatively stable in T-ETc treatment compared with that under T-VWC or Rn-VWC treatments. The plant height, leaves number, leaf area, and stem diameter of T-VWC and Rn-VWC treatments were higher than those of the T-ETc treatments, but there was no significant difference in cucumber yield. Compared with the T-VWC treatment, total irrigation amount under Rn-VWC and T-ETc treatments significantly decreased by 25.75% and 34.04%, respectively (P<0.05\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {P}<0.05$$\\end{document}). The highest IWP values of 25.07 kg m-3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-3}$$\\end{document} was achieved from T-ETc treatment with significantly increasing by 44.33% compared to the T-VWC treatment (17.37 kg m-3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-3}$$\\end{document}). In summary, the T-ETc treatment allowed more reasonable irrigation management and was appropriate for growing cucumber in coconut coir culture.

Climate-smart irrigation strategy can mitigate agricultural water consumption while ensuring food security under a changing climate

North China Plain suffers from the world’s most severe water scarcity and groundwater depletion due to intensive irrigation for agricultural production. It is imperative to reduce irrigation water consumption while safeguarding crop production and food security. This study conducted a quantitative analysis with deficit irrigation strategies for winter wheat using a water-driven AquaCrop model. After model calibration and validation with field experimental data, we analyzed the irrigation water demand, crop yield, and water productivity (WP) of winter wheat under various deficit irrigation scenarios. A set of optimal irrigation schedules were proposed for different climate years, which significantly mitigated irrigation water usage while sustaining high yields and WPs. The results indicated that despite the irrigation water demand of winter wheat under the future climate scenario was slightly higher than that in the historical period, their crop water sensitive periods (reviving, jointing, and flowering) remained the same. Therefore, we recommended adopting the same deficit irrigation schedules for the historical and future periods. In wet years, adopting a 50% deficit irrigation strategy only reduced crop yields by less than 5% compared with full irrigation, but it saved 1000–1100 m3 of water per hectare and contributed a WP higher than 1.88 kg/m3. While in normal and dry years, an optimal 25% deficit irrigation could sustain over 96% of the maximum yield, meanwhile it could save 650–800 m3/ha of water and achieve almost the same WP as full irrigation. These climate-smart irrigation strategies adapting to diverse climatic conditions largely mitigate agricultural water consumption while maximizing crop productivity and water use efficiency, which are essential for achieving precision irrigation and sustainable water management under a changing climate.

Soil-Matric-Potential-Based Irrigation Scheduling to Increase Yield and Water Productivity of Okra

A field experiment was conducted on okra (Abelmoschus esculentus L.) for assessing the sustainability of yield with optimum irrigation schedule based on soil moisture depletion. Four irrigation treatments: Irrigation at I1:20%, I2:30%, I3:40% and I4:50% of soil moisture depletion rate in main plots and three fertilizer treatments: Fertigation at F1:100%, F2:80% and F3:60% of recommended NPK (100:25:40 kg/ha) in subplots were tested. Soil matric potential was recorded continuously using electronic tensiometers. The soil moisture characteristics curve was derived for various soil matric potential value sand the soil water content. The irrigation controller triggered solenoid valves for irrigation when soil moisture depletion reached a prespecified level in each treatment. Soil moisture depletion values were significantly predicted based on a regression model calibrated for each treatment over the crop growing period. The model gave minimum prediction error (PE) for I1, followed by I2, I3 and I4, respectively. Plant growth and yield parameters were significantly influenced by the soil moisture availability under each treatment. It is recommended that irrigation be scheduled at 20% soil moisture depletion rate together with 100% NPK fertilizer application for attaining sustainable yield of okra (12.3 t/ha), apart from maximum WUE (3.5 kg/m3) and plant growth parameters under semiarid inceptisols.

Effective irrigation scheduling to improve corn yield, net returns, and water use

AbstractIrrigated cropland continues to increase in Tennessee to sustain yield when seasonal temperatures are high and water deficit occurs. Corn (Zea mays L.) production is directly related to water availability. Inefficient irrigation scheduling can result in excessive or inadequate water applications. The objective of this study was to optimize profitable corn yield using soil water sensors to improve irrigation scheduling. Three irrigation regimes plus a rainfed/check were evaluated in a field study at the University of Tennessee Milan Research and Education Center in 2020 and 2021. Soil water level was monitored with Meter Inc. Teros 21 matric potential sensors and ZL6 soil‐water loggers. Irrigation‐scheduling decisions were based on rainfall, plant growth stages, and soil water sensors. An optimal irrigation schedule was determined within this study that increased yield by 13.0% and 3.0%, respectively, in 2020 and 2021 while also having the lowest cost among irrigation treatments. Results indicate that irrigation scheduling can improve yield, conserve water, and increase economic returns.

Productivity of Bt Cotton under Different Levels of Irrigation and Nitrogen in Alfisols

Aims: To determine the optimum irrigation schedule and nitrogen level for Bt cotton in alfisols in Southern Telangana.&#x0D; Study Design: Split plot design.&#x0D; Place and Duration of Study: ARI, Rajendranagar, Hyderabad during kharif 2014.&#x0D; Methodology: The experiment was laid out with three irrigation schedules (I1- 0.8 IW/CPE, I2 - 0.4 IW/CPE and I3 - Rainfed) as main plots and four nitrogen levels (N1- 0 kg ha-1, N2 - 75 kg ha-1, N3 - 150 kg ha-1 and N4 - 225 kg ha-1) as sub plot treatments replicated thrice. Treatments imposed as per the schedule and data recorded on yield, yield attributes, nitrogen uptake by adopting standard procedures&#x0D; Results: Irrigation at 0.8 IW/CPE recorded significantly higher plant height (79 cm), drymatter at first picking (195 g plant-1), bolls plant-1 (16), seed cotton yield (1435 kg ha-1), lint yield (541 kg ha-1), stalk yield (2057 kg ha-1) and nitrogen uptake (63 kg ha-1) and was not differed significantly with 0.4 IW/CPE and these were significantly superior to rainfed cotton. Among nitrogen levels, significantly higher plant height (90 cm), drymatter at first picking (214 g plant-1) stage, days to reach boll development (90) stage, bolls plant-1 (15), boll weight (5.3 g), seed index (9.9 g), seed cotton yield (1435 kg ha-1), lint yield (547 kg ha -1) and stalk yield (2214 kg ha -1) were found with application of nitrogen at 225 kg ha-1 was comparable with 150 kg N ha-1 and were significantly superior over lower levels of nitrogen application. The substantial increase in yield and yield attributes might be due to favorable effect on growth attributes like plant height, increased bolls plant-1, drymatter accumulation plant-1 and its subsequent translocation towards sink improved the seed cotton yield.&#x0D; Conclusion: It can be concluded that, higher seed cotton yield can be obtained with the irrigation scheduled at 0.4 IW/CPE and application of nitrogen at 150 kg ha-1 in Bt cotton grown in alfisols.

Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate

The Food and Agriculture Organization recommends that the Penman–Monteith Method contains Equation 56 (PMF) as a widely accepted standard for reference evapotranspiration (ETo) calculation. Despite this, the PMF cannot be employed when meteorological variables are constrained; therefore, alternative models for ETo estimation requiring fewer variables must be chosen, which means that they perform at least as well as, if not better than, the PMF in terms of accuracy and efficiency. This study evaluated five machine learning (ML) algorithms to estimate ETo and compared their results with the standardized PMF. For this purpose, ML models were trained using monthly time series climatic data. The created ML models underwent testing to determine ETo under varying meteorological input combinations. The results of ML models were compared to assess their accuracy and validate their performance using several statistical indicators, errors (root-mean-square (RMSE), mean absolute error (MAE)), model efficiency (NSE), and determination coefficient (R2). The process of evaluating ML models involved the utilization of radar charts, Smith graphs, heatmaps, and bullet charts. Based on our findings, satisfactory results have been obtained using RBFFNN based on M12 input combinations (mean temperature (Tmean), mean relative humidity (RHmean), sunshine hours (Sh)) for ETo estimation. The RBFFNN model exhibited the most precise estimation as RMSE obtained values of 0.30 and 0.22 during the training and testing phases, respectively. In addition, during training and testing, the MAE values for this model were recorded as 0.15 and 0.17, respectively. The highest R2 and NSE values were noted as 0.98 and 0.99 for the RBFNN during performance analysis, respectively. The scatter plots and spatial variations of the RBFNN and PMF in the studied region indicated that the RBFNN had the highest efficacy (R2, NSE) and lowest errors (RMSE, MAE) as compared with the other four ML models. Overall, our study highlights the potential of ML models for ETo estimation in the arid region (Jacobabad), providing vital insights for improving water resource management, helping climate change research, and optimizing irrigation scheduling for optimal agricultural water usage in the region.

Enhancing Blackgram Yield through Optimized Irrigation Scheduling and Integrated Nutrient Management: Growth Characteristics and Yield Components

A field experiment was carried out at Soil Conservation and Water Management Farm of C S Azad University of Agriculture and Technology, Kanpur during Zaid seasons i.e. 2022 and 2023 to assess the effect of irrigation scheduling and integrated nutrient management on blackgram.The experiment consisted of three irrigation schedulesviz.I1:0.4 IW/CPE, I2:0.6 IW/CPE, I3:0.8 IW/CPE, along with five integrated nutrient management options viz.F1: 100% RDF, F2:125% RDF.F3: 75% RDF + 2.5 ton FYM/ha,F4: 50 % RDF + 5 ton FYM/ha.F5: 50% RDF + 2.5 ton FYM/ha + 1 tonvermi-compost. The experiment was conducted in Split Plot Design replicated thrice irrigation scheduling systems in main plots and integrated nutrient management in sub plots. The result clearly revealed that significantly higher growth attributes viz plant height (35.25 cm and 37.56 cm), number of branches plant-1 (8.87 and 9.21), number of plant leaves (19.96 and 20.29) at harvest and yield attributes viz number of pod per plant (27.80 and 30.10) no of seed per pod (6.62 and 6.91) and also higher seed yield (9.30 q ha-1 and 9.90 qha-1 ), stover yield(23.21 q ha-1 and 24.03 q ha-1) were recorded with I3: 0.8 IW/CPE compared to I1: 0.4 IW/CPE and I2: 0.6 IW/CPE. Among the integrated nutrient management significantly higher growth attributes viz plant height (36.12 cm and 38.07 cm), number of branches plant-1 (9.01 and 9.32), number of plant leaves (20.05 and 20.45) at harvest and yield attributes viz number of pod per plant (28.44 and 30.17) no of seed per pod (7.05 and 7.37) and also higher grain yield (9.73 q ha-1 and 10.27 qha-1 ), stover yield(23.55 q ha-1 and 24.76 q ha-1) were recorded with F5:50% RDF + 2.5 ton FYM/ha + 1 ton vermi-compost as compared to other integrated nutrient management protocol.

Irrigation scheduling for potatoes (Solanum tuberosum L.) under drip irrigation in an arid region using AquaCrop model.

Potato is one of the key food crops and China is the largest potato producer in the world. However water scarcity is the major constraint to increase the productivity of potato in the arid regions such as Ningxia in northwest China where this crop is extensively cultivated. The overall objective of this study was to optimize the irrigation for potato cultivated under the drip irrigation. To do this, the AquaCrop model was calibrated and validated using the data obtained from two years of field experiment. Then, the calibrated crop model was used to simulate growth and tuber yield of potato in response to 30 different irrigation schemes under two different irrigation scenarios. The crop model evaluation parameters namely, the root mean square error (RMSE), the index of agreement (d), the normalized root mean square error (NRMSE) and the coefficient of determination (R2) showed that the AquaCrop model could simulate the growth and yield of potato under the drip irrigation with different irrigation treatments with reasonable accuracy. Furthermore, yield of potato has increased with increasing amount of total irrigation under drip irrigation; however, yield begins to decline when the amount of total irrigation exceeds 2500 m3 ha-1. The study also found that the optimum irrigation schedule for potato was 20mm of irrigation quota at 7 days of irrigation cycle (i.e., 1800 m3 ha-1 or 180mm of total irrigation). The above irrigation scheduling has achieved 46.77t ha-1 of tuber yield with 15.74kg m-3 of water use efficiency. These findings may be evaluated in potato cultivation across different climate and soil conditions for wide applicability at different arid regions of the world.

Modeling of soil moisture and water fluxes in a maize field for the optimization of irrigation

Precision irrigation is becoming more and more important in agricultural crop production due to the limited water resources resulting from the negative effects of climate change. Modeling of irrigation can support decision makers in the maintenance of the quantitative and qualitative parameters of the cultivated crops, while improving the efficiency of water consumption. In the present paper, a 3D hydrodynamic model was created in the HYDRUS environment to support the modeling of the temporal changes and spatial variations in the water balance (WB) of a maize cultivated Hungarian study site, thereby providing inputs for irrigation scheduling and variable rate irrigation for stakeholders. Beside soil physical parameters, crop evapotranspiration (ETc) values were also considered as model inputs for the phenological crop development stages of the maize from the sowing to the harvesting. This period was between the 3rd of May and the 10th of September in 2020 and the 20th of May and 14th of September for the year 2021. The performance of the model for soil moisture conditions were validated by on-field soil moisture measurements. The overall performance (full vegetation period) of the model was good (r2 = 0.88 for 2020 and r2 = 0.91 for 2021), but slightly varied among different phenological stages. Based on the model, the water balance of the investigated area was determined without any irrigation, alongside the cumulative water fluxes (CWF) through the boundaries including the root water uptake (RWU) of the maize as well. The results revealed that the incoming precipitation was not sufficient to supplement the water content in the soil to the optimal soil moisture range, except when the precipitation level is high enough to balance it. It was concluded that the water balance was negative for the investigated time periods without any irrigation. The specific water deficit (WD) values were calculated considering the area of the study site, which is 1439 m3/ha for 2020 and 2068 m3/ha for 2021. From the obtained results, optimal irrigation schedules were presented to keep the soil moisture content in the optimal range (20.92–14.41 V/V%) in the modeled area for two different vegetation periods. It was found that due to the irrigation the crop water productivity (CWP) increased by 6% and 4% in 2020 and 2021. Overall, the model is able to cope with changing circumstances that could help to mitigate the negative effects of climate change with the reasonable use of limited water resources in the future.

Investigating high throughput phenotyping based morpho-physiological and biochemical adaptations of indian pennywort (Centella asiatica L. urban) in response to different irrigation regimes

Indian pennywort (Centella asiatica L. Urban; Apiaceae) is a herbaceous plant used as traditional medicine in several regions worldwide. An adequate supply of fresh water in accordance with crop requirements is an important tool for maintaining the productivity and quality of medicinal plants. The objective of this study was to find a suitable irrigation schedule for improving the morphological and physiological characteristics, and crop productivity of Indian pennywort using high-throughput phenotyping. Four treatments were considered based on irrigation schedules (100, 75, 50, and 25% of field capacity denoted by I100 [control], I75, I50, and I25, respectively). The number of leaves, plant perimeter, plant volume, and shoot dry weight were sustained in I75 irrigated plants, whereas adverse effects on plant growth parameters were observed when plants were subjected to I25 irrigation for 21 days. Leaf temperature (Tleaf) was also retained in I75 irrigated plants, when compared with control. An increase of 2.0 °C temperature was detected in the Tleaf of plants under I25 irrigation treatment when compared with control. The increase in Tleaf was attributed to a decreased transpiration rate (R2 = 0.93), leading to an elevated crop water stress index. Green reflectance and leaf greenness remained unchanged in plants under I75 irrigation, while significantly decreased under I50 and I25 irrigation. These decreases were attributed to declined leaf osmotic potential, increased non-photochemical quenching, and inhibition of net photosynthetic rate (Pn). The asiatic acid and total centellosides in the leaf tissues, and centellosides yield of plants under I75 irrigation were retained when compared with control, while these parameters were regulated to maximal when exposed to I50 irrigation. Based on the results, I75 irrigation treatment was identified as the optimum irrigation schedule for Indian pennywort in terms of sustained biomass and a stable total centellosides. However, further validation in the field trials at multiple locations and involving different crop rotations is recommended to confirm these findings.

Using Grapevine Water Status Measurements for Irrigation Scheduling of Table Grapes - A Review

Water is becoming an increasingly scarce resource, so agriculture competes with urban and industrial needs for water. The production of table grapes with high export potential is the objective of South African producers. Growth, production, ripening aspects and quality parameters of table grapes can potentially be manipulated by means of irrigation. Consequently, it is an important management practice to help ensure economically viable table grape production. The objective for optimum irrigation scheduling should be to combine soil and plant water status measurements to calibrate grapevine water potential against reliable soil water monitoring instruments. Considering previously reported literature, poorer vegetative growth was related to lower levels of leaf water potential (YL). Given that berry size is a crucial aspect for yield as well as quality, it was evident that low levels of water potential can restrict berry development, thereby reducing berry size. Bunch mass was lower where there were lower levels of YL, pre-dawn leaf water potential (YPD) and total diurnal water potential (YTot). Poorer yield was generally related to lower levels of YL experienced throughout the season. However, lower levels of YL in the post-veraison period did not affect grapevine yield. The juice TSS did not respond to levels of YL but juice total titratable acidity (TTA) was related to lower levels of YL. Grape colour was affected where wet soil conditions induced higher levels of YL as well as where dry soil conditions induced lower levels of YL.

State of the art of the optimisation of the selection and use of renewable energies for the agricultural environment

A combination of different energy generation sources, together with the possibility of installing batteries or other forms of energy storage systems, can enable the adaptation of the generated electricity to the needs of the farming sector. From optimal irrigation schedules for crops, to taking advantage of pruning waste to generate electricity at times when other sources are not able to generate it, this fitting can allow the farmers to focus solely on the wellbeing of the plants, without worrying about energy prices. On the other hand, it also provides the possibility of maximising energy production on days where the crop health is already ensured. In summary, this study is the first steps to work on what is missing to create hybrid energy systems that can adapt to the needs of the crops and their ecosystem. These hybrid energy systems applied on farms can improve the wellbeing of isolated areas, such as islands, industrial sites, or villages, helping the owners provide benefits for themselves and their communities.