Crop Irrigation Scheduling Research Articles

Precision Agriculture using ML for Soil and Weather Prediction

Agriculture is the backbone of many economies and plays a critical role in global food security. However, with growing challenges posed by climate change, erratic weather patterns, and soil degradation, the need for precise and predictive techniques in agriculture has never been more urgent. This paper focuses on the application of machine learning (ML) techniques in soil fertility prediction and weather forecasting, two critical components that can significantly impact agricultural productivity. By analysing soil properties, such as moisture, pH, and nutrient levels, and combining them with accurate weather predictions, our system aims to help farmers make informed decisions regarding crop selection, irrigation scheduling, and pest control. Leveraging algorithms like Naive Bayes for soil classification and Long Short-Term Memory (LSTM) networks for weather prediction, we provide a comprehensive solution for precision agriculture. This system not only enhances productivity but also promotes sustainable agricultural practices by optimizing resource use and reducing wastage

Quantifying energy fluxes in Tarai region of India during post-monsoon season: Insights from METRIC model, ET station and remote sensing

Accurate evapotranspiration (ET) assessment is crucial for agricultural water management, encompassing crop water requirements, irrigation scheduling, water budgeting and drought monitoring. This study integrates remote sensing-based surface energy balance model with in-situ ET measurements to evaluate surface energy fluxes and ET in Pantnagar, Tarai region. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model, using high-resolution remote sensing data, was validated against observations from an ET station equipped with large aperture scintillometer and micrometeorological tower, situated in sugarcane farm at Govind Ballabh Pant University of Agriculture & Technology (GBPUA&T), Pantnagar. On November 13, 2021, METRIC and Landsat-9 satellite data estimated an instantaneous ET of 7.39 mm day-1, closely aligned with the observed value of 6.72 mm day-1 recorded by the ET station. The findings confirm the METRIC model's high accuracy for spatial ET estimation and its associated micrometeorological variables. This study underscores the utility of the METRIC model, ET station and remote sensing in determining ET and energy flux which may be further utilised in the estimation of crop water requirement, energy fluxes and irrigation water management for sugarcane cultivation in the Tarai region.

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.

Water Requirement and Irrigation Scheduling of Selected Vegetable Crops Grown in Welmera District, Central Highland of Ethiopia

Accurate quantification of irrigation water requirement at different physiological growth stages of crops is important to prevent over or under irrigation. A study was carried out to determine the crop water requirement and irrigation scheduling of the selected vegetable crops grown under irrigated conditions at Holeta, Central Highland of Ethiopia. The crops include, cabbage, and tomato. By using the 30-year climatic data, the crop evapotranspiration (ETc), reference crop evapotranspiration (ETo), and irrigation water requirement for each crop were determined using the CROPWAT model. The study shows that the total CWR of cabbage, and tomato was estimated to be 426.6mm, and 497.7mm respectively, and the total GIR of cabbage, and tomato was found to be 598.3 mm, and 691.7 mm respectively. for early January planted cabbage, irrigation should be given twelve times ( 1-Jan, 9-Jan, 16-Jan,24-Jan, 2-Feb, 11-Feb, 21-Feb, 5-Mar,15-Mar, 25-Mar, 2- Apr and last irrigation on 12-Apr) with a gross irrigation water amount of 59.8mm, 32.5mm, 31.5mm, 35.2mm,42.2mm,43.3mm,48.1mm, 52mm, 51.3mm, 49.5mm, 51mm, and 51.7mm depth respectively. Similarly for early January planted tomato, irrigation should be given twelve times (1-Jan, 5-Jan, 10-Jan, 16-Jan, 21-Jan, 29-Jan, 6-Feb, 15-Feb, 26-Feb, 12-Mar, 26-Mar, and the last irrigation on 10-Apr) with a gross irrigation water amount of 53.9mm, 19.3mm, 25.5mm, 30.7mm, 30.6mm, 36.4mm, 47.8mm, 58.5mm, 73.4mm, 83.5mm, 80.9mm, 80.7mm irrigation depth respectively. This study might be useful to prevent over or under irrigation which will help in effectively planning and management of irrigation water in the district.

Assessment of Crop water requirement using FAO CROPWAT 8.0 for Groundnut in South Odisha

Water has a major impact on crop growth, output, and quality and increased production with less water consumption is crucial due to increased demand from domestic and industrial sectors. With evaporative demand-based irrigation scheduling the water consumption gets reduced and improves water productivity. The type of soil, growing season, weather, and crop type affect specific crops' water needs. To compute crop water requirement, irrigation requirement, and its schedule for groundnut cultivated during the rabi season in south Odisha, India, the FAO CROPWAT 8 program was used, with the meteorological data of Centurion University, Paralakhemundi, and NASA power. The water requirement for groundnut was 362.1 mm, the irrigation requirement for groundnut crops was 323.7 mm, and crop evapotranspiration (ETc) was 373.4 mm during rabi season. At 50% critical depletion of soil moisture and soil replenishment to field capacity, the yield did not decrease. These results indicate that irrigation schedules can be practiced for different regions' crops without experimental results by estimating crop water requirements and irrigation schedules obtained through the FAO CROPWAT 8 model.

Irrigation scheduling for high value vegetable crops grown under protected cultivation in the hilly ecosystem of north-east India

Irrigation scheduling plays a vital role in the efficient utilization of water to obtain optimum yield, particularly for high value crops grown under protected conditions. The present experiment was conducted during 2020–22 at Horticulture Experimental Farm, ICAR-Research Complex for North Eastern Hill Region, Umiam, Meghalaya to standardize the irrigation scheduling (IS) (5, 7, and 10-days interval) in high-value vegetable crops [tomato (Solanum lycopersicum L.), capsicum (Capsicum annuum L.) and king-chilli (Capsicum chinense Jacq.)] grown under naturally ventilated low-cost polyhouse in the mid-hill conditions of Meghalaya. The results revealed a significant effect of IS on plant growth (reduction in plant height, leaf area) and yield attributes (fruit setting, fruit size, fruit weight and total yield) of the crops with an increase in level of moisture stress. The highest yield of tomato (82.7 t/ha), capsicum (40.8 t/ha) and king-chilli (11.8 t/ha) was recorded in the 5-day interval of IS and on increasing the IS interval to 7-days, the yield decreased by 28.7, 36.1 and 35.7%, respectively. Among the crops, capsicum was found to be more sensitive to water stress, followed by king-chilli and tomato. Among the crops, water productivity with respect to irrigation water was maximum in tomato (60.0 g/kg) followed by capsicum (17.2 g/kg) and king-chilli (10.0 g/kg). Among the varieties grown, Megha Tomato-3 of tomato, hybrid Mahabharat of capsicum and landrace Red Long of king-chilli were found superior for growth and yield attributes at different levels of moisture regime. The farmers of the region can get higher yield using identified varieties with irrigation at 5-days intervals.

Crop Suggestive System through Soil Property Characterization using IoT

The emergence of Internet of Things (IoT) technology has brought about a significant transformation in multiple sectors, including agriculture, by facilitating data- driven decision-making and real-time monitoring. In this work, we present a novel Internet of Things (IoT) agricultural suggestion system that uses soil property characterization to enable accurate crop management and selection. Our technology seeks to help farmers optimize agricultural yields while minimizing resource usage and environmental impact by combining IoT sensors with modern data analytics. Our system's primary feature is the installation of Internet of Things (IoT) sensors throughout fields to continuously collect data on important soil attributes. These characteristics include salinity, pH values, temperature, moisture content, nutrient concentrations, and organic matter content. This data is wirelessly transmitted by the sensors to a cloud platform or centralized database, where it is thoroughly analyzed. One of the many difficulties facing modern agriculture is choosing crops optimally to maximize yields while minimizing environmental effect. Conventional crop selection techniques sometimes rely on manual soil testing or broad suggestions, which produces less than ideal results and inefficiencies. A novel method using the Internet of Things (IoT) is suggested to address this: a Crop Suggestive System using Soil Property Characterization (CSS-SPC). Using Internet of Things sensors, this system continuously monitors and analyzes soil parameters in real-time, giving farmers customized crop recommendations depending on the unique conditions of their property. Crop rotations, soil amendments, and irrigation schedule optimization are made possible for farmers through smooth planning and decision-making that ismade possible by integration with current farm management systems. Farmers' ongoing input on the effectiveness ofsuggested crops guarantees the system's accuracy and continued applicability

Sensitivity of daily reference evapotranspiration to weather variables in tropical savanna: a modelling framework based on neural network

Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting ETo. The Gamma test technique was adopted to select the suitable input combination of meteorological variables. The performance of the models was quantitatively and qualitatively evaluated using several statistical criteria. The study showed that the ANN-10 model performed superior to the ANFIS-06, WANN-11 and MNLR models. The proposed ANN-10 model was more appropriate and efficient than the ANFIS-06, WANN-11 and MNLR models for predicting daily ETo. Solar radiation was found to be the most sensitive input variable. In contrast, actual vapour pressure was the least sensitive parameter based on sensitivity analysis.

Spatial mapping of soil moisture content using very-high resolution UAV-based multispectral image analytics

Assessing soil moisture content (SMC) is necessary for managing water at a spatial scale. Remote sensing technologies provide a robust approach for detecting the spatial-temporal fluctuations of SMC. The aim of this study was to estimate SMC at different soil depths using very high-resolution unmanned aerial vehicle (UAV)-based multispectral (MS) images and machine learning algorithms and generate spatial maps of SMC using the best-performed machine learning (ML) algorithm. The UAV-based multispectral images of bare soil were captured at 40 m altitude with a very high spatial resolution (2.89 cm) during the rabi 2021/22 season. At the same time, the soil samples were collected from different soil depths, and the gravimetric SMC was measured. Five machine-learning algorithms (Linear Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), and Support Vector Regression (SVR)) were used to train the model between SMC and MS data (MS band reflectance and vegetation indices). The soil with high SMC has low spectral reflectance and soil with low SMC shows high spectral reflectance. For the prediction of surface SMC, the linear regression (R2 = 0.89; RMSE = 2.80 %) and 5 cm depth SMC, the SVR (R2 = 0.64; RMSE = 3.03 %) were performed well compared to other ML algorithms. For surface SMC, blue band reflectance, and 5 cm depth SMC, the Ratio Vegetation Index (RVI) correlated well compared to others. All models failed to predict the SMC at the deeper soil depths. The spatial SMC mapping described the visual color variations in SMC within the field. Crop irrigation scheduling can be significantly improved through the insights this spatial SMC estimation approach provides, making it a valuable tool for farmers and irrigation planners.

Irrigation Schedule Optimization for Wheat and Sunflower Intercropping under Water Supply Restrictions in Inner Mongolia, China

Precise water management is essential for the efficient development of irrigated agricultural crops in the Hetao Irrigation Area of Inner Mongolia. Given the severe water scarcity in the region and the significant use of intercropping as a cropping method, the development of rational irrigation scheduling is crucial. The objective of this work was to combine the ISAREG model with wheat–sunflower intercropping crops in order to enhance the effectiveness of irrigation scheduling in intercropping systems. This was achieved by changing and verifying crucial parameters for simulating irrigation patterns in intercropping. We conducted an assessment of nine irrigation schedules for a wheat–sunflower intercropping system in order to provide a range of irrigation scenarios that effectively fulfill the water requirements of the system. In light of this, we suggested implementing restrictions on the dates and volumes of irrigation based on the demand for agricultural irrigation. This approach aimed to establish irrigation schedules that are highly efficient and tailored to the specific crops in the area. As a result, we achieved a water use efficiency rate of 100%, saved 28.78% of water resources, optimized crop irrigation schedules, and enhanced crop economics by 6.7%. This study presents a novel and efficient method to optimize agricultural irrigation schedules, boost agricultural water use efficiency, and maximize crop yields in order to promote sustainable agricultural development.

Crop water requirement and irrigation scheduling under climate change scenario, and optimal cropland allocation in lower kulfo catchment

Crop water requirement and irrigation scheduling in Lower Kulfo Catchment of southern Ethiopia have not assessed under climate change scenarios, and the allocation of crop land also not optimal that signifcantly challenges to crop productivity.Therefore, this study was conducted to evaluate the effects of climate change on future crop water requirements, and irrigation scheduling, and to allocate cropland optimally. Bias of projected precipitation and temperature were corrected by utilizing Climate Model data with the hydrologic modeling tool (CMhyd). Alongside, crop water requirements and irrigation scheduling were assessed using Crop Water Assessment Tool. After estimating crop water requirement, crop land were allocated optimally using General Algebraic Modeling System programming with non-negativity constraints (scenario 1), and non-negativity constraints based on farmers adaptation (scenario 2). Average reference evapotranspiration from 2030 to 2050 and 2060 to 2080 was increased by 11.9 %, and 16.2 %, respectively compared with the reference period (2010–2022). The total seasonal crop water requirements were 4,529 mm, 4866.7 mm, and 5272.2 mm under 2010 to 2022, 2030 to 2050, and 2060 to 2080 climate change scenarios, respectively. The meean irrigation interval in 2010–2022, 2030 to 2050, and 2060 to 2080 climate change scenarios were 8 days, 7 days, and 5 days, respectively. This irrigation interval was decreased by 14 % (2030–2050), and 34 % (2060–2080) compared with the reference period. In 2030 to 2050 and 2026 to 2080 climate change scenarios, the required irrigation water at the inlet of main canal increased by 6.8 %, and 18 %, respectively. The optimal allocated area for tomato (60.4 %), maize (20.8 %), and watermelon (18.8 %) in scenario 1 with net benefit of 1.47*108 Ethiopian Birr. The allocated areas in scenario 2 were (48 %) for maize, (31.6 %) for tomato, and (20.4 %) for watermelon with 1.34*108 Ethiopian Birr net benefit it was reduced by 19.1 % compared with the net benefit in scenario 1. Fruit crops alone may not suffice for local food needs and to address this, small farmers should grow maize, tomato, and watermelon. This research aids policymakers in encouraging climate-resilient agriculture and improving small-scale farmers' awareness through conducting workshops and training.

Impact of different water regimes on maize grown at two distinctive pedo-climatic locations in Bosnia and Herzegovina

Aim of study: A two-year experiment (2021-2022) was conducted to assess the response of a local maize hybrid BL-43 to different water regimes (full irrigation, deficit irrigation and rainfed) at two distinguished pedo-climatic locations (Aleksandrovac and Butmir) in Bosnia and Herzegovina (BiH).
 Area of study: The field experiment was located in Aleksandrovac (near Banja Luka) and Butmir (near Sarajevo) in BiH.
 Material and methods: A randomized block design was adopted at both experimental locations with three replicates. An Excel-based irrigation tool was used to manage crop water requirements and irrigation scheduling.
 Main results: Crop response to water was affected by site-specific agronomic management, the duration of phenological stages and their interconnection with precipitation events. At both locations, the effect of the water inputs on grain yield was statistically significant confirming the beneficial impact of irrigation. The effect of water stress on yield was particularly pronounced at Aleksandrovac, which was under water and temperature stresses during flowering time. During both seasons and for all water regimes, the total average grain yield was greater at Butmir than at Aleksandrovac for 38% and 27%, respectively.
 Research highlights: This is the first experimental study conducted in BiH on the effect of irrigation on maize grain production under different pedoclimatic conditions. The study emphasizes the need for knowledge regarding the impacts that climate change is having on the productivity of one of the region's most important crops.

ASCD: Automatic sensing and control device for crop irrigation scheduling

Traditional agricultural irrigation systems suffer from wasting a lot of water allocated for irrigation through evaporation and drainage. To address water waste and increase crop productivity, irrigation scheduling is used, as irrigation scheduling depends on four elements: soil moisture, soil characteristics, weather conditions, and plant type. This paper proposes a new design for monitoring agricultural conditions and controlling the amount of soil moisture by determining the required irrigation water ratios and the ideal time period for irrigating crops. An intelligent control and monitoring algorithm was created using experimental data for a wide range of plants (for soil moisture from 21 to 80 kPa), where plants were classified into three groups according to their common irrigation starting point. Based on the proposed plant classification, three models were provided, where irrigation ratios as the required parameter, soil moisture, plant type, and time interval as input parameters. This algorithm was used to program a custom-made automatic sensor and control device (ASCD). The ASCD is equipped with two input ports to communicate with two types of soil moisture sensors (a resistive sensor and a capacitive sensor) and with three output ports to drive three types of electronic irrigation valves. The ASCD can read from one sensor and drive one electronic valve at a time, the choice of which is made by the farmer. ASCD based the new algorithm showed high accuracy compared to a number of experimental results. The average absolute relative deviation (AAPD) of the new models and experimental data is 5.46 %. The application of the new algorithm shows a reduction in the amount of water used for crop irrigation during the day versus irrigation at night. On the other hand, ASCD has proven its success in sensing and controlling, and it works automatically and independently.

Assessment of crop water requirement and irrigation scheduling of potato using weather models under sub temperate climatic condition of North-Western Himalaya

To optimize irrigation water and enhance yield, appropriate amount of irrigation water and its scheduling according to real time weather data can help in reducing crop water foot prints. The evapotranspiration (ET) based irrigation scheduling using present weather conditions reflecting changed climatic scenarios in the study regions in the past few decades has proved to be sagacious use of water. To study the ET based irrigation scheduling, field experiment conducted with the treatments comprising four ET based weather models irrigation scheduling methods viz. no irrigation or rainfed, Thornthwaite, Hargreaves method, Temperature and modified Penman Monteith under sowing windows of 20th December and 20th January were conducted at research farm, of Department of Agronomy, CSK HPKV, Palampur, Himachal Pradesh during Rabi season of 2015-16. The estimated crop evapo-transpiration (ETc) with different ET estimation methods under North Western Himalayas showed variation during crop season. The highest estimated ET was observed in December as compared to January sown potato crop with Hargreaves method (376.8 mm) followed by Temperature (354 mm), Thornthwaite (329.5 mm) and the lowest in modified Penman Monteith methods (241.9 mm). The estimated ETc from pan evaporation observed to be the highest to the tune of (382.3 mm) compared to other methods of ETc estimation. The crop coefficients modeled at different pheno-phases were used for estimating water requirement of potato crop. It was found that irrigation scheduling based on modified Penman Monteith of ET estimation saved one irrigation to the tune of 60 mm during crop period without affecting significantly growth, and tuber yield of the potato as compared to other methods, viz., However, the irrigation schedule based on Thornthwaite method has recorded highest total potato tuber yield (171.76 q ha-1) being at par with irrigation schedules worked out from other ET estimation methods. The total water footprints were the lowest in modified Penman Monteith to the tune of 300 liters per kg of potato yield compared to other methods.

Assessment of crop water requirement and irrigation scheduling for selected crops in the Kohima district, Nagaland, India

Abstract This study was done to examine agricultural water and irrigation schedules for key crops grown in Kohima area of Nagaland, India, which has a humid subtropical climate. Climatic data over 16 years (2006–2021) were used to estimate crop water requirements using CROPWAT 8.0 model. Rice, maize, soybean, potato, cabbage, dry bean, ginger, and naga chili are the most common crops. ET0 was calculated using the FAO Penman–Monteith method and ranged from 1.41 to 3.65 mm/day. The USDA SCS approach calculated effective rainfall. The average annual ETC for rice, maize, ginger, soybean, bean (dry), potato, cabbage, and naga chili were 537.1, 305.7, 342.7, 292.2, 288.1, 364.3, 190.6, and 141 mm, respectively. Rice, maize, ginger, soybean, bean (dry), potato, cabbage, and naga chili require an average of 251.7, 54.9, 26, 73.1, 21.3, 21.9, 121.9, and 14.5 mm of irrigation each year, respectively. The crop irrigation schedules were created with 70% efficiency. The study emphasizes that irrigation must be adjusted to each crop's individual needs, whether through strategic scheduling or modifying net and gross irrigation volumes, to enhance water management and maximize crop production in the region.

Estimating irrigation requirement and scheduling for major crops: Case study in Kampong Thom, Cambodia

The situation is the outcome of both increased water demand and climate change, the shortage of water resources is increasing in Cambodia. In this regard, efficient irrigation water management for vegetable production is a crucial need for farmers. The main crops farmed in Chroab commune, Santuk district, Kampong Thom province, Cambodia, are cabbage, potato, and tomato. This article uses the CROPWAT model to predict the water requirement. The pressure plant was used to measure the soil’s soil water capacity, and the findings ranged from 45.65 to 234.159 mm/m. The analysis showed that the overall CWR for crop water for the entire growing season for cabbage, potato, and tomato was 589.4 mm/165 days; 442.5 mm/130 days; and 585.4 mm/145 days, respectively. The findings of the study contribute to investigating the irrigation water requirements with scientific soil and climate inputs.

Comparative Analysis of Climate Change Impacts on Climatic Variables and Reference Evapotranspiration in Tunisian Semi-Arid Region

Systematic biases in general circulation models (GCM) and regional climate models (RCM) impede their direct use in climate change impact research. Hence, the bias correction of GCM-RCMs outputs is a primary step in such studies. This study compares the potential of two bias correction methods (the method from the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3) and Detrended Quantile Matching (DQM)) applied to the raw outputs of daily data of minimum and maximum air temperatures and precipitation, in the Cap-Bon region, from eight GCM-RCM combinations. The outputs of GCM/RCM combinations were acquired from the European branch of the coordinated regional climate downscaling experiment (EURO-CORDEX) dataset for historical periods and under two representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Furthermore, the best combination of bias correction/GCM-RCM was used to assess the impact of climate change on reference evapotranspiration (ET0). Numerous statistical indicators were considered to evaluate the performance of the bias correction/historical GCM-RCMs compared to the observed data. Trends of the Hargreaves–Samani_ET0 model during the historical and projected periods were determined using the TFPMK method. A comparison of the bias correction methods revealed that, for all the studied model combinations, ISIMIP3 performs better in reducing biases in monthly precipitation. However, for Tmax and Tmin, the biases are greatly removed when the DQM bias correction method is applied. In general, better results were obtained when the HadCCLM model was used. Before applying bias correction, the set of used GCM-RCMs projected reductions in precipitation for most of the months compared to the reference period (1982–2006). However, Tmin and Tmax are expected to increase in all months and for the three studied periods. Hargreaves–Samani ET0 values obtained from the best combination (DQM/ HadCCLM) show that RCP8.5 (2075–2098) will exhibit the highest annual ET0 increase compared to the RCP4.5 scenario and the other periods, with a change rate equal to 11.85% compared to the historical period. Regarding spring and summer seasons, the change rates of ET0 are expected to reach 10.44 and 18.07%, respectively, under RCP8.5 (2075–2098). This study shows that the model can be used to determine long-term trends in ET0 patterns for diverse purposes, such as water resources planning, agricultural crop management and irrigation scheduling in the Cap-Bon region.

Determination of Irrigation water requirement and Scheduling of onion at Low land area of Wag-himra, Northern Ethiopia

Irrigation that saves water is important for satisfying the economic and environmental sustainability of commercial agriculture and improving the living standard of the people. Precision irrigation scheduling is critical to improving irrigation efficiency. A field experiment was conducted in two consecutive years at Abergelle irrigation schemes. The aim of the study was to determine crop water requirement and irrigation schedule of onion (when and how much to irrigate) for most market-oriented crops. The treatments were arranged with a factorial arrangement randomized complete block design with three replications. Three Levels of CROPWAT, fixed application depth (125%, 100%, and 75%ETc) and, three irrigation intervals (3, 4, and 5 days) and 1-farmer practice were used as control. The study result showed that 75% CROPWAT fixed depth at 3-day intervals achieved high water productivity, and saved 2873m3ha-1 of irrigation water compared with farmers’ method of irrigation. It provides additional irrigated land of 0.84 ha with a yield gain of 10.44 t ha-1. Therefore, a 3-day irrigation interval with 75% CROPWAT irrigation depth was recommended for the optimum yield and water productivity of the crop.

Unveiling the Thirst: Revealing the Water Requirements of Gujrat's Thriving Crops using CROPWAT 8.0

Water plays an essential role in agriculture, serving as a primary reserve that directly impacts crop production and food security. Water conservation is critical for sustainable agriculture and the well-being of communities and ecosystems, chiefly due to the increasing global demand and rising water dearth concerns. The water needs of crops and irrigation scheduling in the district Gujrat, Pakistan, are currently missing enough information. Therefore, this study was conducted to determine the crop water requirements and establish irrigation schedules for the major crops in district Gujrat, Pakistan. The crop water requirement (CWR) and irrigation schedules were determined by CROPWAT 8.0 by using the input climate data obtained from the POWER-NASA website from 1991-2020. Other than climatic data, crops (wheat, rice, sugarcane, and maize) and soil information were added from CROPWAT 8.0 pre-existing data. The crop planting dates were estimated by Ayub Agriculture Research Institute (AARI). The gross and net crop water requirements are 624.3 mm and 491.3 mm for wheat, 1805.2 mm and 1768.3 mm for rice, 4135.1 mm and 3537.2 mm for sugarcane, and 1203.5 mm and 1051.4 mm for maize. The deployment of CROPWAT 8.0 proves to be valuable in precisely assessing crop water requirements and computing irrigation scheduling. The study's outcomes show the potential to boost food production and aid in well-organized water reserve management.

Surface soil moisture estimation in bare agricultural soil using modified Dubois model for Sentinel-1 C-band SAR data

Surface soil moisture has vital role in water energy balance, climate change and agriculture mainly for crop water requirements and irrigation scheduling. Microwave remote sensing with its unique characteristics of high penetration and sensitivity towards dielectric constant, has enabled the researchers to explore various techniques for soil moisture estimation. With the launch of Sentinel-1 (A&B) Synthetic Aperture Radar (SAR) satellites, the hindrance in accessing high spatial and temporal resolution data is eliminated. The current study focuses on surface soil moisture estimation for bare agricultural fields in the semi-arid region. Field soil moisture up to 5 cm depth using HydraGo Probe sensor and surface roughness synchronizing with satellite pass dates were collected from total 102 locations spanning four dates. Volumetric and sensor-based soil moisture are well correlated with R2 = 0.85. The Modified Dubois Model (MDM) was applied to obtain the relative permittivity of the soil for the backscattering coefficient (σ◦) for VV polarization, which is used as one of the inputs in universal Topp’s model for soil moisture calculation. Model derived soil moisture is well correlated with ground-based soil moisture for the entire range of the soil moisture (0.02-0.18 m3m-3) with R2 = 0.85 and RMSE=0.005. The entire soil moisture was categorized in three soil moisture ranges to evaluate the sensitivity. The highest correlation was observed for 0.06-0.1 m3m-3 with R2 = 0.73 and RMSE=0.003 followed by 0.015-0.6 m3m-3 with R2 = 0.81 and RMSE=0.001 and 0.11-0.18 m3m-3 with R2 = 0.48 and RMSE=0.019 which is significantly low. Performance accuracy of MDM is encouraging for bare soil moisture estimation for even the lower range of surface soil moisture.