Irrigation Requirements Research Articles

Application of Hydrological and Hydrogeological Models for Evaluating Groundwater Budget in a Shallow Aquifer in a Semi-Arid Region Under Three Pumping Rate Scenarios (Tavoliere di Puglia, Italy)

We analyze the variation in groundwater budget by modeling an aquifer in a semi-arid region in southern Italy, using different good pumping scenarios. This aquifer is overexploited due to the agricultural vocation of the area. We propose an integrated method to assess the distribution of hydrogeological parameters and the recharge rates. The hydrogeological parametrization is performed through a hydrostratigraphic approach using the geostatistical tool. Recharge rates are computed through a soil water balance application, using different monitoring stations over the area for the whole period of interest. Integrating the results of this analysis with pumping scenarios based on the water irrigation requirement of the main crops in the area, different water budgets are estimated. The results show how different pumping scenarios affect the availability of water resources and thus underline the importance of management. This integrated hydrogeological model can be applied to other areas with similar hydrogeological characteristics, and it can be considered a valuable tool for evaluating sustainable groundwater management strategies, considering land use practices and socio-economic factors.

Nexus of climate change, irrigation requirement and water balance for paddy production in Chandra Canal Irrigation System, Nepal

Nexus of climate change, irrigation requirement and water balance for paddy production in Chandra Canal Irrigation System, Nepal

Optimization of border irrigation variables based on a correction factor for irrigation quota

The optimization of surface irrigation variables, i.e., the selection of the optimal combination of the inflow rate per unit width (q) and cutoff time (tco), is essential for obtaining high performance. The main objective of this study was to optimize irrigation variables by considering different irrigation requirements and the total loss model, which includes the irrigation water loss and crop yield loss. A correction factor for the irrigation quota (Cf) was introduced to achieve this objective. Cf considers different irrigation requirements, as it represents the ratio of the actual irrigation quota for certain irrigation to the designed irrigation quota (Dreq). Uniform design theory was used to determine random combinations of q and Cf from a selected range. The q value ranged from 3 to 9 L.m-1. s-1. Because the actual irrigation quota does not greatly deviate from the design irrigation quota, Cf should reach approximately 1.00. The selected Cf ranged from 0.80 to 1.38. To obtain higher crop yields, lower economic losses, and more reasonable design variables for surface irrigation, a total loss model for uneven border irrigation was established, which was defined as the objective function to optimize border irrigation design—the total loss model was combined with uniform design theory and the WinSRFR model to analyze different scenarios. The results showed that Cf has a clear meaning and exerts a favorable application effect on the irrigation performance evaluation indicators and can be used to design border irrigation systems. Based on the total loss model for uneven border irrigation, the optimal irrigation variables q and Cf for design irrigation quotas of 60, 80, and 100 mm are q=6.22 L.m-1. s-1 and Cf=1.17; q=4.60 L.m-1. s-1 and Cf=1.14; and q=3.80 L.m-1. s-1 and Cf=1.10, respectively. Compared with the conventional design results, q in the optimal design results based on the loss model decreased, Cf increased, and the total loss significantly decreased. Optimization of irrigation variables based on the border irrigation loss model could ensure favorable irrigation performance evaluation indicators, improve the water use efficiency, provide higher crop yields, and minimize the total economic losses caused by uneven irrigation.

Response of Green Faba Bean Yield: As Nexus of Water Deficit, P-Fertilizers, and Phosphorus Recovery

Water and fertilizer optimum management in agriculture is important in improving crop productivity, two field experiments were conducted in El-Qanater Horticultural Research Station, Qalubiya Governorate, Egypt, during 2022/2023 and 2023/2024 to study the response of (Vicia faba L.,) Giza 716 faba bean cultivar to three irrigation water levels 100 % of potential evapotranspiration, ET crop (I1), 80 % ET crop (I2) and 60 % ET crop (I3) of irrigation requirements, the effect of applying P-fertilizers no P-fertilization (P0); powdered calcium super phosphate (P1); granular calcium superphosphate (P2) and powdered rock phosphate (P3) all P-sources inoculated by B. megaterium. The obtained results could be summarized as follows: Increasing the applied amount of irrigation water from 3172 (I3) to 4229 m3 ha-1 (I2) and (P3) gave the highest values of all growth characters, except plant height and amount of pods/ plant in average values of both seasons. Moreover, the 80% ET crop (I2) and rock phosphate sources treatment increased yield values attributes, and NPK contents of seeds. Also, the highest values of Irrigation Water productivity (IWP) kg m-3 faba bean green pods and seed quality (protein %) occurred with 80% ET crop (I2) of irrigation water level and powdered rock phosphate (P3).

Water Requirements, Soil Moisture Availability, and Their Effects on Quinoa (Chenopodium quinoa Willd.) Development and Yield

Throughout the world, about 70% of the total water is used for irrigation. Reports show that there will be a significant increase in irrigation requirements, as irrigation is an important factor affecting water footprint and productivity worldwide [45]. The experiment described in this article was carried out at the Agricultural Research Station, Ministry of Agriculture, Iraq, in 10 kg pots to evaluate the effects of soil moisture availability on water requirements, water productivity, and quinoa crop productivity under different irrigation treatments. These treatments maintained the soil at different levels of soil water tension (-0.05, -0.10, -0.15, -0.30, and -0.40 MPa) during the vegetative and/or reproductive periods of growth. The study analyzed water consumption, plant growth, yield components, and water productivity of the quinoa crop. The data showed that evapotranspiration decreased with increasing soil moisture tension during both the vegetative and reproductive periods of development. The maximum water productivity was consistently recorded at the lowest soil moisture tension, highlighting moisture efficiency. It was concluded that quinoa is relatively tolerant to soil moisture stress during the vegetative period compared to the reproductive period. The number of grains per ear was identified as a limiting factor for grain yield.

Construction of a quantitative model for ski resort water demand and preliminary exploration of drainage irrigation pathways

In China, natural snowfall is insufficient, and ski resorts often require artificial snowmaking in winter and turf management in summer, which results in high overall water consumption and considered as one of the high water-consuming service industries, with an urgent need for theoretical foundations related to water management. Based on life cycle theory, we examined the characteristics of the water systems of ski resorts in winter and summer. A Monte Carlo simulation was used to construct a stochastic model to quantify the theoretical water consumption for snowmaking at ski resorts and to investigate the mechanisms by which various factors influence this consumption. We summarize the necessity of summer turf management and irrigation requirements at ski resorts, explore the interaction between ski resorts and agricultural irrigation. The results show that the theoretical water demand of ski resorts is basically consistent with the actual water consumption of ski resorts, that the constructed model is feasible, and that snowmaking water is the most important water use of ski resorts. Exploring the collection and reuse of meltwater or rainwater from ski resorts for agricultural irrigation in the spring and summer could provide more possibilities for the sustainable management of regional water resources. The research findings can provide a theoretical basis for scientifically and reasonably setting the norm of water intake for ski resorts and for developing comprehensive water resource management plans for ski resorts and agricultural irrigation.

Improving sustainability of rice-canola rotation through water and nitrogen management in a humid region

Intensive cropping systems face significant environmental and economic challenges due to current water and nutrient management practices. This study addresses the limited research on optimizing these practices in rice-canola rotations, focusing on enhancing sustainability through improved water and nitrogen (N) management. Utilizing the DSSAT crop growth model, we evaluated three irrigation scenarios for rice- I1 (100 % irrigation requirement: IR), I2 (80 % IR), and I3 (60 % IR)-alongside three N application levels: N1 (30 % below RAN: regional average N), N2 (RAN), and N3 (30 % above RAN). Key sustainability indicators, including water productivity (WP), nitrogen use efficiency (NUE), and economic productivity, were analyzed. The DSSAT model demonstrated strong predictive accuracy, with yield differences of only 236 kg ha⁻¹ for rice and 121 kg ha⁻¹ for canola between observed and simulated values. A 40 % reduction in water use increased rice yield to a maximum of 5654 kg ha⁻¹ under I3N2 conditions. Additionally, this reduction in water consumption resulted in a 27 % decrease in production costs, significantly enhancing WP, net benefits, and gross benefits of rice by 46.7 %, 43.1 %, and 47.9 %, respectively. Conversely, a 30 % increase in N application for canola correlated with a 9 % yield increase, underscoring the importance of tailored N strategies. Furthermore, N3 raised WP, NUE, net benefits, and gross benefits of canola by 6.7 %, 1.4 %, 9.1 %, and 9.2 %, respectively, compared with current N application level. Overall, this study illustrates the potential for substantial water savings in rice cultivation while recommending stable or reduced N application levels. These management strategies enhance farm performance, lower production costs, and contribute to the sustainability of rice-canola cropping systems, promoting both agricultural productivity and environmental stewardship.

Effects of Restoration Strategies on Soil Health after Lycium chinense Removal in the Qaidam Basin

Ecological restoration of arid land plays a pivotal role in maintaining ecological sustainability and enhancing the resilience of local communities. As an ecologically significant arid land, the Qinghai Qaidam Basin has been severely impacted by human activities such as the widespread planting of Lycium chinense, leading to considerable degradation of vegetation and soil carbon and nutrients. Currently, this vital area is undergoing extensive ecological restoration through the employment of a variety of strategies, but the impact remains inadequately understood. This study seeks to compare the effects of different restoration strategies in the Qinghai Qaidam Basin, focusing on soil properties across five scenarios: a controlled desert area, natural restoration after the removal of L. chinense, continued planting of L. chinense, restoration through the planting of Haloxylon ammodendron, and mixed reseeding after four years of restoration. Our findings indicate that mixed reseeding significantly improved soil water storage to 4.26%, especially in the deep soil layer. The planting of H. ammodendron strategy efficiently reduced soil pH in such an alkaline environment. Soil nutrients, including total nitrogen (TN), total phosphorus (TP), and total potassium (TK), were predominantly concentrated in the top soil layer, with reduced concentrations observed in the medium and deep soil layers. Although soil organic matter remained relatively stable across all restoration strategies, its content was notably lower in the deeper layers. Overall, mixed reseeding proved to be the most efficient strategy for enhancing soil water retention and nutrient levels. In contrast, despite achieving high vegetative coverage to 62.6%, planting of L. chinense was less ecologically beneficial due to its extensive irrigation requirements and adverse effects on soil structure. These findings suggest that restoring degraded areas to an ideal ecological state cannot be achieved within a few years, underscoring the importance of sustained restoration efforts. This study offers valuable insights and practical guidance for the ecological restoration of arid lands, contributing to the development of sustainable land management practices in similar regions.

Quantifying Solar Radiation Over Northern India and Validating with Cropwat for Agricultural and Renewable Energy Applications

Precise computation of global solar energy is crucial for many fields of investigation such as renewable energy, meteorology, and agriculture especially crop water requirement and irrigation requirement. This paper presents the quantification of solar radiation using weather data during the years from 2020-21. The results obtained were validated with the solar radiation estimated with the help of CROPWAT during the year. Statistical and visual approaches were adopted to validate the quantified results. Analysis revealed the high degree of similarity between simulated and CROPWAT determined daily solar radiation during the year 2020-21 over the study area. Very high values of Willmot index of agreement (1.0), Nash-Sutcliffe efficiency (0.999), and Pearson correlation coefficient (0.998), and low values of mean absolute error (0.138), mean bias error (-0.024), and root mean square error (0.182) between them was observed during the year 2020-21 in the area. The minimum solar radiation reaching over the region were observed to be around 5.71 MJ/m2/day during winter season (January) with a normal value of 13.50 MJ/m2/day, while the maximum value of 24.5 MJ/m2/day was observed during the summer season (June) with a normal value of 24.13 MJ/m2/day over the region. The use of these data for further study is recommended as it is worthful quantification over the region.

Improving yield and irrigation water productivity of green beans under water stress with agricultural solid waste-based material of compacted rice straw as a sustainable organic soil mulch

AbstractThis research aimed at water saving in irrigation by applying deficit irrigation using two strategies, standard drip and partial root drying (PRD), while applying organic and plastic mulch over two growing seasons of green beans. A field experiment was conducted in 2022 and 2023, using four irrigation treatments supplying 100% of the irrigation requirement (IRg), 75% IRg, 50% IRg, and 50% IRg—PRD, and four soil mulching treatments: uncovered soil (UC), plastic mulch (PM), rice straw mulch (RSM), and compacted rice straw mulch (CRSM). The combined effect of deficit irrigation strategies and soil mulching showed that the maximum irrigation water productivity (IWP) of 5.56 kg m−3 was achieved under 50% IRg—PRD & CRSM for both growing seasons, followed by 50% IRg—PRD & RSM and 50% IRg—PRD & PM, with 5.19 and 4.96 kg m−3, respectively. The highest yield of 8936 kg ha−1 was achieved with 50% IRg—PRD & CRSM, followed by 8914 kg ha−1 and 8898 kg ha−1 with 100% IRg & CRSM and 75% IRg & CRSM, respectively. The lowest yield of 6009 kg ha−1 was obtained with 50% IRg & UC. The highest soil moisture content was observed under 100% IRg & CRSM. The application of organic mulches was found to be particularly effective in conserving soil moisture due to enhanced infiltration, improved retention capacity, and suppression of weed growth, ultimately fostering optimal crop development and higher yield. The results of soil temperature variations beneath soil mulches showed that CRSM is effective in alleviating plant water stress, lowering the temperature below the cover and reducing water loss through evaporation from the soil surface. The combination of 50% IRg—PRD & CRSM produced plants with enhanced plant height, fresh and dry weight, leaf area, pod length, and green bean weight, as well as the highest vegetative growth indices. Generally, the organic mulching increased soil temperature, soil moisture, IWP, and green bean production.

Optimizing Soil Moisture, Mulching, and Spacing for Yield and Water Productivity in Irrigated Elephant Foot Yam (Amorphophallus paeoniifolius)

Aims: To standardize crop spacing and irrigation requirements under different mulching situation for better yield and water productivity. Study Design: The experiment was designed in RBD with two replications. Place and Duration of Study: The experiment was conducted at Agronomic Research Station, Kerala Agriculture University (KAU), Chalakudy for three consecutive years from 2016 -17, 2017 – 18 and 2018 – 2019. Methodology: Study conducted with 18 treatments, includes three levels of irrigation, three types of mulching and with two different crop spacing. Observations on weed count and weed dry matter production were also taken to study the effect of different mulches on weed growth. Biometric observations of crop growth and yield were recorded. Results: Observations on biometric characters revealed that both the height and diameter of the crop canopy were significantly influenced by mulching and found to be highest in the plot where leaf was used as the mulching material and the yield of Amorphophallus during 2016 - 2017 showed that corm weight was greatly influenced by irrigation levels, mulching and spacing. Corm yield was lowest in the plot where irrigation frequency was once in three days (21.02 t/ha). Yield in leaf mulched plot was 38.11 t/ha while in plastic mulched and no mulched plots were 23.50 and 19.50 t/ha. Effect of treatments on water productivity and BC ratio showed that leaf mulching of the crop has significant effect. In leaf mulched plot, water productivity in 2016-17 was 2.03 while it was only 1.854 and 0.886 in 2017-18 and 2018-19. Similarly, BC ratio was highest for leaf mulched plot followed by no mulch and plastic mulch plot. Conclusion: The experimental results from 2016 -17, 2017 – 18 and 2018 – 2019, showed that Amorphophallus planted at a spacing of 90 cm x 90 cm, irrigated at a frequency of once in three days along with leaf mulching can increase the yield and water productivity along with effective control of weed population.

Remote sensing-based green and blue agricultural water footprint estimation at the river basin scale

Since the development of Water Footprint environmental indicator, significant research on blue and green crop water use and the respective water footprint estimations has been published. Such research is commonly approached using different methodologies that leverage tabulated values for crop development characterisation, while studies based on remote sensing data are less abundant, despite crop monitoring using remote sensing-based vegetation indices having demonstrated great capabilities and operability. To help fill this gap, we present a methodology that uses a remote sensing vegetation index time series from Sentinel-2 satellite near infra-red and red spectral bands data to derive basal crop coefficient time series to subsequently be used under the Remote Sensing-based Soil Water Balance approach that follows the globally operative FAO56 procedure. It provides pixel-based temporal and spatially distributed estimations of net irrigation requirements and adjusted crop evapotranspiration, with the aim being to divide up the latter and estimate the remote sensing-based green and blue crop water use and the subsequent green and blue water footprint. This is all done under the Agricultural Water Footprint Assessment framework for a growing crop or tree. This methodology was applied over a large, crop-diverse Spanish river basin district (Júcar) and across two different climatological years (humid vs. dry). Its feasibility was demonstrated by the acceptable behaviour of the remote sensing-based blue crop water use estimation for different herbaceous and woody crops, against the official dataset for irrigation water accounting at two water management scales (of a relative mean absolute error of 15.4 % in the case of the largest water user association and of 17.1 % in the case of the river basin water authorities’ own estimations). The proposed approach, which we call Remote Sensing-based Agricultural Water Accounting and Footprint, aims to provide reliable and accurate spatially and temporally distributed thematic cartography about the remote sensing-based blue and green crop water use and water footprint. This information is essential for water managers with the goal of generating transparent and complementary information to incorporate into their own working scales.

Effect of Irrigation Depth and Nitrogen Fertilizer Rates on Pepper (Capsicum annum L.) Yield and Water Use Efficiency in Tselemty District, Tigray, Ethiopia

Optimizing agricultural crop production involves utilizing proper irrigation and fertilization techniques. A two-year experiment conducted in the Tselemty district during the off seasons of 2019 and 2020 aimed to assess the impact of varying irrigation levels and nitrogen fertilizer application rates on the growth, yield, and crop water productivity of pepper. The study included three irrigation levels (75%, 100%, and 125% of the required irrigation) and three nitrogen fertilizer application rates (75%, 100%, and 125% of the recommended amount). Analysis of the results using Gen-Stat software revealed that most pepper yield attributes were not significantly affected by the different irrigation and fertilizer levels. However, the marketable yield showed significant variation based on the combined application rates. The research indicates that, under ideal circumstances, the optimal approach for pepper growers is a combination of meeting 100% of the irrigation requirement and applying 100% of the recommended nitrogen fertilizer rate. Nevertheless, in scenarios where water resources are limited and fertilizer expenses are high, a reduced irrigation level of 75% of the requirement coupled with 75% of the recommended nitrogen fertilizer rate could be a viable alternative that does not lead to a substantial decrease in yield.

Responses of potential double cropping areas expansion and appropriate crop management practices to climate change in northern China

IntroductionGlobal climate change has led to increases in the temperature and decreases in the number of frost days in northern China, facilitating a shift from a single cropping system (SCS, spring maize) to a double cropping system (DCS, winter wheat-summer maize rotation).MethodsTherefore, under the current climate conditions, DCS expansion should be evaluated, and new planting schemes should be explored. In this paper, we identified the areas with potential for DCS in northern China considering an annual accumulated temperature of >0°C. The World Food Studies simulation model was used to simulate the yield, irrigation requirement (IR), and net income under various crop management conditions when considering the maximum yield and water use efficiency (WUE) of crops.ResultsOur results indicated that the potential DCS area increased by approximately 31.51 × 104 km2 in northern China, with the primary DCS areas being located in the provinces of Gansu, Shaanxi, Shanxi, Hebei, and Liaoning. Regarding variety selection, winter wheat and summer maize varieties with early and mid-early maturation were found to be favored for the potential DCS areas. The sowing dates corresponding to the maximum WUEs of the crops were later than those corresponding to the maximum yields. In the potential DCS areas, under the maximum yield condition, the average unit total yield, IR, and net income increased to 2700 kg ha−1, 305 mm, and 607 USD ha−1, respectively, whereas under the maximum WUE condition, increases of 2862 kg ha−1, 284 mm, and 608 USD ha−1, respectively, were observed. The average unit total yield of the DCS was 15927 and 13793 kg ha−1 under the maximum yield and maximum WUE condition, respectively.DiscussionOur findings may clarify the effects of climate change on agricultural production patterns and indicate suitable crop management practices.

Comparative study of reservoir operations using TLBO, PSO and DE optimization techniques: an experiment on the Hirakud reservoir, Odisha, India

ABSTRACT With the enduring progression of high population density, social economy and requirements of water supply, irrigation and hydropower, the water resource scarcity problem has been exacerbated in Odisha. Hence, the judicial operation of the Hirakud reservoir, which is considered the lifeline of Odisha, has become appreciably essential. Among various optimization techniques, metaheuristic algorithms are advanced techniques which can be applied for the optimal operation of a water reservoir. In this work, three metaheuristic algorithm-based optimization techniques, the Particle Swarm Optimization (PSO), Differential Evolution (DE) and Teaching–Learning-Based Optimization (TLBO) algorithms, are applied for optimal water management of the Hirakud reservoir. From the result, it was found that the efficiency of TLBO for irrigation release during the non-monsoon period was 99.45% compared with PSO with an efficiency of 97.3% and DE with an efficiency of 95.6%. The efficiency of TLBO for hydropower generation was 99.6%, whereas for PSO it was 98.8% and for DE it was 98.5%. It is found from the above experiment that TLBO showed a better performance than PSO and DE optimization techniques for the water management of the Hirakud reservoir. These metaheuristic techniques will provide suitable guidance for reservoir operations at times when the presence of experts is a must but they may not be available.

The impact of green roofs’ composition on its overall life cycle

Green roof systems have been developed to improve the environmental, economic, and social aspects of sustainability. Selecting the appropriate version of the green roof composition plays an important role in the life cycle assessment of a green roof. In this study, 10 compositions of an intensive green roof for moderate zone and 4 green roof compositions for different climatic conditions were designed and comprehensively assessed in terms of their environmental and economic impacts within the “Cradle-to-Cradle” system boundary. The assessment was carried out over a 50-year period for a moderate climate zone. The results showed that asphalt strips and concrete slab produced the highest total emissions. It was found that most greenhouse gases emissions were released in the operational energy consumption phase and in the production phase. The energy consumption phase (48.78%) for automatic irrigation and maintenance caused the highest Global Warming Potential (GWP) value (758.39 kg CO2e) in the worst variant, which also caused the highest life cycle cost (878.47€). On the contrary, in the best variant, planting more vegetation and lower maintenance and irrigation requirements led to a reduction in GWP (445.0 kg CO2e), but in terms of cost (506.6€) this composition didn't represent the best variant. The Global Warming Potential Biogenic (GWP-bio) compared to the Global Warming Potential Total (GWP-total) represents a proportion ranging from 0.8% to 78% depending on the proposed vegetation. Overall higher biogenic carbon values (up to 1525 kg CO2e) were observed for the proposed tall vegetation of Magnolia, Red Mulberry, Hawthorne, Cherry, and Crab-apple Tree. Based on the results of the multicriteria analysis, which included core environmental & economic parameters, biogenic carbon emission levels, the outcome of this paper proposed optimal green roof composition. Optimal intensive green roof composition was subjected to a sensitivity analysis to determine the impact of changing climatic conditions on CO2 emissions and life cycle costs. The results of the sensitivity analysis show that the optimal variant of the green roof can be implemented in the cold and subtropical zone with regard to CO2 emissions, but not with regard to life cycle costs.

Machine Learning Monitoring Model for Fertilization and Irrigation to Support Sustainable Cassava Production: Systematic Literature Review

The manual and time-consuming nature of current agronomic technology monitoring of fertilizer and irrigation requirements, the possibility of overusing fertilizer and water, the size of cassava plantations, and the scarcity of human resources are among its drawbacks. Efforts to increase the yield of cassava plants > 40 tons per ha include monitoring fertilization approach or treatment, as well as water stress or drought using UAVs and deep learning. The novel aspect of this research is the creation of a monitoring model for the irrigation and fertilizer to support sustainable cassava production. This study emphasizes the use of Unnamed Aerial Vehicle (UAV) imagery for evaluating the irrigation and fertilization status of cassava crops. The UAV is processed by building an orthomosaic, labeling, extracting features, and Convolutional Neural Network (CNN) modeling. The outcomes are then analyzed to determine the requirements for air pressure and fertilization. Important new information on the application of UAV technology, multispectral imaging, thermal imaging, among the vegetation indices are the Soil-Adjusted Vegetation Index (SAVI), Leaf Color Index (LCI), Leaf Area Index (LAI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Green Normalized Difference Vegetation Index (GNDVI).

Analysing the Temporal Variation of \(ET_0\) and Water Requirement of Mustard (Brassica juncea L.) Over Different Agroclimatic Zones of Uttar Pradesh by Using CROPWAT

The agriculture sector is a major consumer of the water resource to fulfill the requirement of irrigation and its dependency on ground water is 70-90%. It is important to understand the crop water requirement to prepare a sustainable management of water resources. The Experiment was conducted for estimation of water requirement of Mustard crop and temporal variability of crop evapotranspiration for different Agroclimatic zones of Uttar Pradesh during time period (1992-2022). In this India Meteorological Department (IMD) Daily Gridded data (Maximum temperature, Minimum temperature and rainfall) is used and converted into Weekly, Monthly, Seasonally and Annually from Weather cock for run in the CROPWAT model which estimate the Reference evapotranspiration (ET0), Effective Rainfall, Crop water requirement and Irrigation water requirement. After that we analyze the trend by using Mann-Kendall test. The result of the experiment revealed that the Average maximum temperature was 32.13±0.37℃ and average minimum temperature was 18.96±0.35℃ average rainfall estimated 638.45±170.92mm. The monthly analysis of maximum temperature for 30 years data revealed that the month of January has lowest maximum temperature while May month observes highest maximum temperature for all the nine Agroclimatic zones of Uttar Pradesh and the lowest minimum temperature was in month of January in all the agroclimatic zones. The monthly analysis of rainfall for time period (1992-2022) showed that for overall Uttar Pradesh region July, August month receives the highest amount of rainfall, while November, December month receives lowest rainfall.

Determination of Water Requirements and Irrigation Scheduling for Major Cereal Crops Grown in Welmera District, Central Highland of Ethiopia

Determination of crop water requirements and appropriate irrigation scheduling is important to prevent over or under-irrigation. The study was conducted to determine the crop water requirement and irrigation scheduling of the selected cereal crops grown under irrigated conditions at Holeta, Central Highland of Ethiopia. The crops include wheat, maize, and barley. By using the 30-years climatic data, the crop evapotranspiration (ETc), reference crop evapotranspiration (ETo), and irrigation water requirement for each crop were determined by using the CROPWAT model which is based on FAO-Penman Monteith equation. The results indicated that, the CWR for the early January sown wheat, maize, and barley was 380.2mm, 433.2mm, and 399.2mm respectively. The seasonal gross irrigation requirement was estimated to be 633.67 mm, 722 mm, and 665.33 mm for wheat, maize, and barley respectively. for 1<sup>st</sup> January sown wheat, maize, and barley, irrigation should be given nine times for wheat (1-Jan, 10-Jan, 21-Jan,5-Feb, 16-Feb, 26-Feb, 11-Mar, 25-Mar, and last irrigation on 10-Apr), with GIR application of 50.3mm, 28.1mm, 34.6mm, 48.2mm,56.6mm,67.2mm,69.5mm, 66.2mm, and 72.3mm depth respectively. Seven times for maize (1-Jan, 19-Jan, 5-Feb, 19-Feb, 6-Mar, 21-Mar, and last irrigation on 10-Apr) with GIR of 54.1mm, 51.2mm, 73mm, 89.8mm,96.9mm, 97.7mm, and 100.8mm depth respectively and eight times for barley (1-Jan, 11-Jan, 25-Jan, 6-Feb, 16-Feb, 1-Mar, 15-Mar and last irrigation on 30-Mar) with GIR amount of 42.5mm, 29mm, 39.6mm, 54.1mm, 60mm, 61.6mm, 63.1mm, and 64.1mm depth at each irrigation date respectively. This study might be useful in preventing over or under-irrigation and planning water management strategies in the district for the selected crops.

Mapping of irrigated vineyard areas through the use of machine learning techniques and remote sensing

Effective and sustainable management of aquifers in regions with intensive groundwater use for irrigation requirements accurate mapping or irrigated areas to control water resource exploitation and plan rational water usage. This study proposes a cost-effective methodology based on satellite images to identify irrigated areas utilizing surface water and groundwater resources. The methodology integrates soil moisture estimations, environmental variables, and variables that affect to retention of water soil, that join a ground truth dataset, to estimate irrigated surface through a machine learning method during the irrigation period of 2021. Spectral data derived parameters and crop morphology, along with official data on agricultural parcels, were utilized to define vineyard irrigation areas at the plot scale within the Requena-Utiel aquifer in Eastern Spain. A machine learning classification technique was applied,yielding a remarkable precision of 91.8 % when compared to ground truth data.Discrepancies between the remote sensing-based irrigated area estimation and official data are highlighted. This study represents the most accurate plot-scale irrigation mapping of woody crops in the region to date.