Effect Of Rainfall Research Articles

Modelling coffee yield in Dak Lak province using machine learning algorithms

Application of machine learning algorithms in simulating crop yield has attracted more attention from plenty of scientists in recent years. The objective of this study is to estimate the coffee yields in Dak Lak province by using three machine learning algorithms, namely, artificial neural network (ANN), support vector regression (SVR), and random forest (RF), respectively. Input data in simulating processes includes maximum and minimum temperature, effective rainfall, reference evapotranspiration, and crop water requirement in the period 2000-2020. In which, the percentage of data in the training and testing phases is 70% and 30%, respectively. The results indicated that three machine learning models (i.e., SVR, ANN, and RF) have reasonable performance in simulating the coffee yield, out of which, the RF model performs best with NSE values of approximately 0.918 for the training phase and 0.818 for the testing phase.

The Field application efficiency Assessment of Paddy Field in Aceh Irrigation Scheme, Aceh, Indonesia

The efficiency of project irrigation is the main factor in measuring the irrigation system’s effectiveness in conveying and distributing the water to a paddy field. The efficiency of irrigation projects in Aceh adopted from Indonesian irrigation standards is 65% which is much higher than generally used in some countries in Asia, i.e., 35-46%. This high value is caused by neglecting water losses in the paddy field when determining the project’s irrigation efficiency. This results in insufficient water for the overall irrigable areas planned in the irrigation scheme in Aceh. The study is aimed to assess the field application efficiency by investigating the water losses in paddy fields. To conduct the research, the paddy field in Pandrah Irrigation Project is plotted specifically. A drum technique instrument was installed in the plot to measure the evapotranspiration, percolation, and effective rainfall based on water balance analysis in the drum in one paddy growing season. The results showed that the field application efficiency is 75.34%. meaning some of the water supply could not be effectively consumed by the paddy. Based on the investigation, most water losses in paddy fields are caused by preferential flow through macropores in vicinity bunds which were triggered by the movement of rats, paddy roots, and bad plow of the field. Therefore, it is recommended to investigate the water losses that will occur due to the above assumption by continuing research in the future, so the project efficiency might be stated accurately when planning irrigation projects in Aceh.

Performance evaluation of machine learning algorithms to assess soil erosion in Mediterranean farmland: A case‐study in Syria

AbstractThe development of new techniques, such as machine learning (ML), can provide better insight into the processes and drivers of soil erosion and runoff. However, the performance of these techniques to assess soil erosion in agricultural landscapes is poorly understood. The aim of this study was to evaluate the performance of four machine learning algorithms, generalized linear model (GLM), Random Forest (RF), elastic net regression (EN) and multiple adaptive regression splines (MARS), in predicting soil erosion and runoff in Syria. Soil erosion and runoff were measured on three experimental plots (2.25 m × 1.50 m × 0.50 m, 0.10 m depth in the soil), combined with three different slopes and land use types: RS (8%, olive), SS (12%, citrus), KS (20%, pomegranate). Both erosion and runoff were determined after rainfall events of >10 mm between October 2019 and April 2020. Based on 24 effective rainfall events, the average soil erosion was 0.18 ± 0.14 kg m−2 per event in KS, 0.14 ± 0.11 kg m−2 per event in SS, and 0.12 ± 0.10 kg/m2 per event in RS. Regression analysis indicated strong relationship between the rainfalls and the runoff, the highest connection was recorded in the KS plot (r2 = 0.85; p < 0.05 n = 24). The analysis of covariance indicated that only the runoff had a significant impact on soil erosion (p = 0.02) with a medium effect (ε2p = 0.26). However, the impacts of rainfall events and slope categories on soil erosion were limited (ε2p < 0.01) and not significant (p > 0.05). ML techniques were usually efficient in the prediction, the RF and MARS models were the most accurate: RF had the strongest correlation with the measured values (r = 0.85) with a low estimation error (0.06 kg m−2), but MARS's standard deviation (SD) was closer to the recorded values' SD. GLM and EN were the weakest predictor models. Modeled values of the slightest slope (8%) had the worst accuracies, and the predictions of the 12% slope were the best in all models. This study provides important insights into the usefulness of machine learning techniques and algorithms in predicting the rate of soil erosion and runoff in agricultural dominated landscapes. We highlighted that the RF and MARS algorithms were better predictors of soil erosion and runoff in the coastal region of Syria.

Future projections of crop water and irrigation water requirements using a bias-corrected regional climate model coupled with CROPWAT

Abstract The study is conducted to examine the climate change impact on rice Crop Water Requirement (CWR) and Net Irrigation Requirement (NIR) using the NASA Earth Exchange Global Daily Downscaled Projection (NEX-GDDP) coupled with the CROPWAT 8.0 model. The maximum temperature (Tmax), minimum temperature (Tmin), and rainfall projections for the baseline (years 1981–2015) and future (years 2030 and 2040) under Representative Concentration Pathway (RCP) 4.5 were derived from NEX-GDDP. To reduce the bias, linear scaling (LS) and the modified difference approach (MDA) were employed. Results show that LS performed better than the MDA along with improved statistical measures such as mean (μ), standard deviation (σ), and percent bias (Pbias), in the case of Tmax and Tmin (μ = 31.14 and 19.63 °C, σ = 5.75 and 6.78 °C, Pbias = 1.43 and 0.33%), followed by rainfall (μ = 2.67 mm, σ = 4.94 mm, and Pbias = 2.4%). The future climatic projections showed an increasing trend in both Tmax and Tmin, which are expected to increase by 1.7 °C by 2040. This would cause an increased range of 1.2 and 2% in 2030 and 2040, respectively. Due to a wide variation in effective rainfall (Peff), NIR could increase by 4 and 9% in 2030 and 2040, respectively. The above results may help formulate adaptation measures to alleviate the impacts of climate change on rice production.

A VBA-Based Field Water Balance Model for Efficient Irrigation Water Management of Corn (Zea mays L.)

A field water balance model for efficient irrigation water management of corn was developed using Excel VBA. The model consists of five sub-components or modules, namely, (1) a plant subcomponent, (2) an effective rainfall subcomponent, (3) an evapotranspiration subcomponent, (4) a soil water dynamics subcomponent for the modeling of water flow into and within the soil layers, and (5) an irrigation subcomponent for the estimation of the required amount and timing of irrigation. The model was calibrated and validated using observed data from field experiments and the results showed a reasonably good agreement between the observed and simulated soil moisture values (MAE = 5.76 mm to 12.00 mm, RMSE = 6.83 mm to 13.12 mm, NRMSE = 0.102 to 0.196, and NSE = 0.37 to 0.90). The simulations emphasized that a significant amount of water savings can be achieved when rainfall is properly accounted for in managing water in the field, and that the frequency of rainfall occurrences is as important as the magnitude of rainfall received by the crops. The wide-ranging user-friendliness and simplicity of the model developed in this study can pave the way to eliminating the barriers which cause farmers to resist advancements in their farming practices as the model can easily be used not only by researchers and scientists but also by farmers, especially those with basic knowledge of spreadsheets.

The NRCS curve number equation derived from an instantaneous unit hydrograph: Some consequences

The NCRS-curve number equation allows calculating the storm runoff from a rainfall event for specific types of land use. It was based on an analysis of direct runoff data using baseflow corrected hydrographs and rainfall. Given this basis, the curve number equation can be derived assuming a constant effective rainfall intensity and a cubic reciprocal function as the instantaneous unit hydrograph. The instantaneous unit hydrograph and the resulting curve number equation are further generalized by adding a lag time. The equation for a curve number related hydrograph is presented, allowing to fit this curve number-based hydrograph to event data. The curve number itself is shown be a function of a catchment response time and the average event rainfall intensity. As the catchment response time is linked to the time of concentration the curve number equation and the storage index can be linked to catchment- and flow type characteristics. First results suggest that including the rainfall intensity duration frequency function in the curve number equation may explain systematic deviations observed when fitting the NCRS curve number equation to measured data.

Rainfall prediction using time-delay wavelet neural network (TDWNN) model for assessing agrometeorological risk

In an agriculturally dependent nation like India, accurate and effective rainfall forecasting methods are crucial for assessing agrometeorological risk. Forecasting rainfall is perhaps one amongst the most arduous tasks in this context due to the prevalence of a non-linear pattern. One of the most promising and frequently employed approaches for forecasting rainfall data is the Time-Delay Neural Network (TDNN) model. TDNN's non-parametric, data-driven, and self-adaptive characteristics make it increasingly attractive for modelling nonlinear dynamics and generating nonlinear forecasts. Nevertheless, since the conventional TDNN uses the sigmoid activation function, there is always a chance that the training process may converge to local minima. This study addresses the usage of a Time-delay wavelet neural network (TDWNN), which employs a TDNN architecture with a hidden layer activation function derived from the orthonormal wavelet family, in order to circumvent this issue. TDWNN has been empirically demonstrated using annual rainfall data from two districts in the Indian state of West Bengal. According to the findings of this study, the TDWNN model is superior than the conventional TDNN method to assess agrometeorological risk.

Hydrological Model Adaptability to Rainfall Inputs of Varied Quality

AbstractNumerous studies have evaluated the reliability and hydrologic utility of various rainfall data sets through hydrological modeling. However, the calibration of hydrological models compensates for errors in rainfall inputs. The drivers, conditions, and factors affecting the calibration of hydrological models given the accuracy of rainfall inputs are not well understood. Here, we explore hydrological model adaptability to rainfall inputs of varied quality and its potential mechanisms. Twenty‐eight rainfall products from multiple sources are collected for a headwater catchment in the Southern United States. These rainfall data sets include measurements from rain gauges, weather radars, satellites, reanalysis products, and Weather Research and Forecasting model simulations. Such rainfall data sets with varied errors are used to independently calibrate a widely used conceptual Xin'anjiang (XAJ) hydrological model. Results suggest that the hydrological model can often adapt well to two scenarios of inaccurate rainfall inputs producing high‐performance streamflow simulations. This adaptive ability is controlled by an adaptable threshold of the overall bias of the rainfall inputs. Moreover, hydrological model adaptability to rainfall inputs is further influenced by how event‐based rainfall bias shapes the overall rainfall bias, especially from those of heavy rainstorms. The hydrological model can adapt to those rainfall inputs that contain important information content for model calibration. Notably, the adaptability to rainfall inputs of the XAJ model is mainly controlled by a bias reduction through adjustment of evapotranspiration and soil moisture storage, yielding satisfactory effective rainfall. The study quantitatively sheds new light on hydrological model adaptability to rainfall input quality.

<b>Proposal of Snowmelt Disaster Warning Criteria Using Effective Rainfall Index Which Reflects Snowmelt</b>

In snow-covered areas, slope failures induced by snowmelt water may occur. In this study, we attempted to develop a warning method for snowmelt disasters using the effective rainfall index, which reflects the analyzed snowmelt amount (hereinafter referred to as the effective snowmelt index) and we verified the appropriate half-life of the effective snowmelt index by comparing it with the observation results of groundwater response. As a result, it was confirmed that a strong correlation between them under the conditions that the half-life of the index was set from 24 hour to 96 hour. In addition, through case studies, we found that the warning method using the effective snowmelt index and snow depth as evaluation indices could effectively provide warning of snowmelt disasters.

English

Indonesia lies in a tropical climate area and depends on rainfall as the main source of irrigation. Changes in rainfall patterns and air temperature directly impact the water availability for irrigation purposes, including Danda Besar Swamp Irrigation Area (DIR). DIR Danda Besar is a tidal swamp irrigation field, but the tidal power cannot reach several areas, so it needs to use rainwater to supply water demand and becomes rainfed agriculture. The observation of changes in rainfall patterns and air temperature from 2001 to 2020 was conducted using daily TRMM (Tropical Rainfall Measurement Mission) data split into 2 observation periods: Period I from 2001 to 2010 and period II from 2011 to 2020. The data analysis process consists of validation and correction to the TRMM data before it is used for water balance analysis, calculation of effective rainfall for rice and evapotranspiration, and analysis of water availability and demand. From the comparison of water availability discharge between Period I (2001-2010) and Period II (2011-2020), the highest increase in mid-month water availability occured on December 1 and December 2, and the highest decrease in water availability occured on November 1 and November 2. The results of the water balance analysis are based on water demand according to the cropping scenario with two cropping scenarios. Water balance 1 has a deficit in water availability for Periods I and II; water balance 2 is recommended for cropping patterns due to climate change, so the water balance is calculated based on water availability for Period II. No water deficit in Water balance 2.

EVALUASI KEBUTUHAN DAN KETERSEDIAAN AIR IRIGASI DALAM RANGKA PENINGKATAN HASIL PERTANIAN

The availability of water in irrigation areas can affect agricultural productivity. Water balance must be maintained so that there is no shortage of water. Cikunten II has an area of 4.362 ha, mostly in the Tasikmalaya district. This study aims to analyze the water requirement and balance. Data analysis used the KP01 guidelines and the Cropwat program. The data analysis results obtained differences in reference plants' evapotranspiration results using the Modified Penman method in KP and Pennman–Monteith in the Cropwat program. The difference between the two methods is 133,83% due to the difference in albedo coefficients. The effective rainfall for paddy and secondary crops, the KP method analysis is lower than the Cropwat simulation of 110.10%. Availability of water using data from the Ciwulan weir with a reliability of 80% obtained a maximum discharge of 2,73 m3/sec. The water requirement was calculated during planting periods I, II, and II. The KP method is greater than the Cropwat method with a percentage of 277.42% in analyzing water requirements in the Paddy-Paddy-plants cropping pattern. The availability of water in the Cikunten area using Cropwat has fulfilled the needs marked by a more stable k factor than the KP01 method.

Shallow landslide susceptibility assessment under future climate and land cover changes: A case study from southwest China

There is no doubt that land cover and climate changes have consequences on landslide activity, but it is still an open issue to assess and quantify their impacts. Wanzhou County in southwest China was selected as the test area to study rainfall-induced shallow landslide susceptibility under the future changes of land use and land cover (LULC) and climate. We used a high-resolution meteorological precipitation dataset and frequency distribution model to analyse the present extreme and antecedent rainfall conditions related to landslide activity. The future climate change factors were obtained from a 4-member multi-model ensemble that was derived from statistically downscaled regional climate simulations. The future LULC maps were simulated by the land change modeller (LCM) integrated into IDRISI Selva software. A total of six scenarios were defined by considering the rainfall (antecedent conditions and extreme events) and LULC changes towards two time periods (mid and late XXI century). A physically-based model was used to assess landslide susceptibility under these different scenarios. The results showed that the magnitude of both antecedent effective recharge and event rainfall in the region will evidently increase in the future. Under the scenario with a return period of 100 years, the antecedent rainfall in summer will increase by up to 63% whereas the event rainfall will increase by up to 54% for the late 21st century. The most considerable changes of LULC will be the increase of forest cover and the decrease of farming land. The magnitude of this change can reach + 22.1% (forest) and –9.2% (farmland) from 2010 until 2100, respectively. We found that the negative impact of climate change on landslide susceptibility is greater than the stabilizing effect of LULC change, leading to an over decrease in stability over the study area. This is one of the first studies across Asia to assess and quantify changes of regional landslide susceptibility under scenarios driven by LULC and climate change. Our results aim to guide land use planning and climate change mitigation considerations to reduce landslide risk.

Investigation of Irrigation Water Requirement and Evapotranspiration for Water Resource Management in Southern Punjab, Pakistan

Water scarcity and water quality degradation are exacerbated by climate change in all countries, including Pakistan. The use of water in agriculture is one of the most predominant resources, so reducing consumption and improving resource management is of utmost importance. In the past few decades, excessive irrigation has led to severe water scarcity and reduced water quality. This study determined the irrigation requirements for cotton, rice, and wheat, using the CROPWAT model in Southern Punjab (Multan District). In the study area, evapotranspiration ranged from 1.8 to 10.24 mm/day, while effective rainfall ranged from 2 to 31.3 mm. Rice, cotton, and wheat each required 996.4, 623.3, and 209.5 mm of irrigation, respectively. Among rice, cotton, and wheat, the total net irrigation was 72.4, 67.8, and 44.1 mm, respectively, while the total gross irrigation was 103.5, 99.8, and 63 mm. The CROPWAT model showed a moderately useful result for identifying irrigation needs in Southern Punjab. The study emphasizes the need for groundwater harvesting and water management technologies to implement a water management system that reduces water shortages.

Optimization of Irrigation Water to Maximize Transplanted Aman Rice Production in Selected Areas of Bangladesh

In rainfed and irrigated agriculture, optimization of irrigation scheduling is very essential to maximize the crop production using the limited of agricultural water resources. A study was conducted to estimate requirement of irrigation and its scheduling of T. Aman (wet season) rice in the Northwest and Southeast region of Bangladesh using CROPWAT model. This paper utilized the daily rainfall and reference evapotranspiration data sets, obtained from seven weather stations for the period of 1981 to 2015. Effective rainfall and reference crop evapotranspiration (ETo) were calculated using the USDA Soil Conservation method and Penman-Monteith method, respectively. The simulation model of this explored the appropriate transplanting date to utilize maximum rainwater and to avoid supplemental irrigation. Four transplanting dates 15 July, 30 July, 15 August and 30 August were considered for transplanting T. Aman rice in the study areas. The optimization results showed that the water requirement was lower of the rice crop transplanting on 15 July in all locations due to higher effective rainfall. No supplemental irrigation was required for T. Aman cultivation transplanted on 30 July in Rangpur, Dinajpur, Bogura and Mymensingh regions since rainfall was sufficient to meet the demand of the crop. Therefore, this period was found for suitable and recommended for transplanting of T. Aman rice in these four locations. Transplanting after 30 July in these four locations needed supplemental irrigations. This study also revealed that 30 July transplanting required one supplemental irrigation in Jashore and Rajshahi regions due to insufficient rainfall. This period was also considered as the appropriate transplanting time in these two locations. However, in Pabna, one supplemental irrigation must be applied for T. Aman even transplanting on 15 July owing to in adequate rainfall. Irrigation requirement and number of irrigations were gradually increased with delaying the transplanting time. Early transplanting demanded less irrigation than late transplanting. Bangladesh Rice J. 25 (2) : 11-20, 2021

Models Comparative Study for Estimating Crop Water Requirement and Irrigation Scheduling of Maize in Metekel Zone, Benishangul Gumuz Regional State, Ethiopia

This study was aimed to compare estimation methods of crop water requirement and irrigation scheduling for major crops using different models and compare the significance of models for adoption at different situations in Metekel zone. Crop water requirement and irrigation scheduling of maize in selected districts of Metekel zone were estimated using CropWat model based on soil, crop and meteorological data and AquaCrop based on soil, crop and meteorological data including Co 2 , groundwater, field management, and fertility status. Model performance was evaluated using Normalized Root mean square errors (NRMSE), model by Nash-Sutcliffe efficiency (NSE), Prediction error (Pe), and Model efficiency (MF). It is observed that the maximum reference evapotranspiration in the study area was found to be 7.1 mm/day in Guba and minimum reference evapotranspiration was 2.9 mm/day in Bullen district. In all cases, the maximum ETo in all districts was fund to in March and the lowest in August. The maximum ETc of maize was found to be 702.4mm in Guba district and minimum ETc was found to be 572.6mm in Bullen district using CropWat but the effective rainfall (Pe) for maize were determined as 185mm respectively in Wembera district. However, using AquaCrop model the maximum ETc of 565 mm was recorded in Guba but 425 mm was recorded as minimum in Wembera district for irrigated maize in the study area. The study revealed that the irrigation scheduling with a fixed interval criterion for maize 10 days with 12 irrigation events has been determined. Moreover, furrow irrigation with 60 % irrigation application efficiency was adjusted during irrigation water applications for all districts. The performance of the irrigation schedule and crop response was evaluated by the analysis results in the simulation using different models. It has been observed that there was a strong relationship and a significant relation between the simulated and observed values for validation. Hence, Normalized Root mean square errors (NRMSE), model by Nash-Sutcliffe efficiency (NSE), Prediction error (Pe), and Model efficiency (MF) showed that AquaCrop model well simulated in all parameters considered. AquaCrop model is the most suitable soil-water-crop-environment management model, so future studies should suggest a focus on addressing deficit irrigation strategy with different field management conditions to improve agricultural water productivity under irrigated agriculture for the study area for major crops. Keywords: Depilation, Irrigation events, AquaCrop, Fixed interval and Deficit Irrigation. DOI: 10.7176/JEES/13-1-01 Publication date: January 31 st 2023

Evaluating Model Effectiveness for Soil Infiltration Attribute: Comparison of Green-Ampt, Horton and Modified Green-Ampt Infiltration Models

Soil infiltration is a very important concept in hydrology as well as irrigation, which plays a vital role in estimating surface runoff and groundwater recharge. It is a complicated process that varies with numerous factors. Accurate estimation of soil infiltration is required for future irrigation, and many other purposes. To estimate the infiltration process, there are numerous models. The majority of them have some presumptions, a unique calculation method, and some limitations. The purpose of the paper was to assess the model’s performance for a similar hypothetical scenario involving soil infiltration. It compared the infiltration rate, runoff rate, and incremental infiltration versus time for three different infiltration models: the Green-Ampt model (GA), the Horton model and the Modified Green-Ampt (MGA) model. A spreadsheet was used to calculate the Horton model, and HYDROL-INF (V 5.03) was used to simulate the other two models. Among those three models, the MGA model outperformed those three models, while the GA model produced greater infiltration rate than rainfall, which was insensible. The study showed that the MGA model, which provides useful infiltration predictions, outperformed the other two infiltration models. Since the Horton model does not consider ponding conditions, it is only applicable when the effective rainfall intensity exceeds the final infiltration capacity. Moreover, the GA model’s initial infiltration rate is irrational because it disregards the intensity of the rainfall. The results of this study will assist in selecting the most accurate method for estimating soil infiltration for agricultural purposes.

Potential impact of climate change on shifting the calendar of rainfed crops produced in Jordan

Climate change alters the usual pattern of rainfall distribution and temperature in the East Mediterranean region. We investigated the impact of climate change on true season onset, and crop calendar for field crops grown in areas with Semi-Arid-Sub-Humid Mediterranean climate. Analysis showed a clear reduction of annual rainfall for the zone with rainfall more than 200 mm, and above 1°C rise in air temperature for all zones. Generally, the rainfall reduction occurred, in descending order, along the South-North direction. A new approach is proposed to identify the true season onset and the end of the effective rainy season. Shifting the true season onset consequently reduces the crop-growing season and hence the yield. Reducing annual rainfall and the length of effective rainfall season, rising air temperature, and the sharp increase in the number of heat waves will have profound negative impacts on both plant growth and yield.

The Impact of Dam Reservoir on Flood Reduction (Case Study Logung Reservoir, Kudus, Central Java)

The Indonesian government targeted building 65 reservoirs and dams in 2015-2022. The purpose is to maintain food security facing the long-term climate crisis, besides reducing the impact of a flood. Logung Reservoir is located on the Logung River, Kudus, Central Java. This earth-fill dam was built to solve the flood and drought problems in Kudus. However, the existence of the Logung Reservoir gives a risk to the downsteam area. The purpose of this research is to study flood reduction and the risk of overtopping, which can cause a dam break, and to calculate the Logung Reservoir spillway's performance in handling floods. This study is based on four main steps: the rainfal-runoff modelling, the extreme rainfall estimation, the hydrograph inflow of the dam estimation, and the evaluation of food reduction. The results of the 1000-year rainfall analysis and PMP for the Logung dam basin were 409 mm and 704 mm, respectively. Rainfall distribution is made using ABM, and effective rainfall is calculated by the SCS method. Land use analysis in the Logung dam watershed obtained a CN-II of 71.4 and a CN-III of 85.2. The rainfall-runoff transformation method used a unit hydrograph. The Nakayasu and Gama-I unit hydrographs show significant differences from the peak discharge, although not for the time base. The flood hydrograph design affects the reservoir performance. The reservoir flood routing results a dumping efficiency of 35% for the 1000-year and 21% for PMF. The 1000-year flood routing shows that there was no risk of overtopping. The highest water level on the PMF flood routing at +94.1. Several solutions can prevent overtopping by reducing inflow discharge through small dams upstream, increasing the green area, or modifying spillway structure.

An intelligent approach to improve date palm crop yield and water productivity under different irrigation and climate scenarios

Drought, rising demand for water, declining water resources, and mismanagement have put society at serious risk. Therefore, it is essential to provide appropriate solutions to increase water productivity (WP). As an element of research, this study presents a hybrid machine learning approach and investigates its potential for estimating date palm crop yield and WP under different levels of subsurface drip irrigation (SDI). The amount of applied water in the SDI system was compared at three levels of 125% (T1), 100% (T2), and 75% (T3) of water requirement. The proposed ACVO-ANFIS approach is composed of an anti-coronavirus optimization algorithm (ACVO) and an adaptive neuro-fuzzy inference system (ANFIS). Since the effect of irrigation factors, climate, and crop characteristics are not equal in estimating the WP and yield, the importance of these factors should be measured in the estimation phase. To fulfill this aim, ACVO-ANFIS employed eight different feature combination models based on irrigation factors, climate, and crop characteristics. The proposed approach was evaluated on a benchmark dataset that contains information about the groves of Behbahan agricultural research station located in southeast Khuzestan, Iran. The results explained that the treatment T3 advanced data palm crop yield by 3.91 and 1.31%, and WP by 35.50 and 20.40 kg/m3, corresponding to T1 and T2 treatments, respectively. The amount of applied water in treatment T3 was 7528.80 m3/ha, which suggests a decrease of 5019.20 and 2509.6 m3/ha of applied water compared to the T1 and T2 treatments. The modeling results of the ACVO-ANFIS approach using a model with factors of crop variety, irrigation (75% water requirement of SDI system), and effective rainfall achieved RMSE = 0.005, δ = 0.603, and AICC = 183.25. The results confirmed that the ACVO-ANFIS outperformed its counterparts in terms of performance criteria.

Estimation of Crop Water Requirements and Irrigation Scheduling for Major Crops Grown in India's North-Eastern Region

The water demand of some agricultural crops grown in Assam was assessed in this research. To estimate the crop water requirements of potato, chili, and brinjal, the FAO-CROPWAT model was used. The climatic parameters like rainfall, solar radiation, wind speed, sun hours, relative humidity, minimum and maximum temperature were collected for 30 years from the Indian Meteorological Department (IMD) to estimate the crop water requirement (CWR). The reference evapotranspiration (ETo) was computed by using the Food and Agriculture Organization (FAO)-recommended Penman-Monteith method and was found to vary from 1.84 mm/day to 4.49 mm/day. The calculated ETo was further used to compute the CWR, the net irrigation requirement (NIR), and the gross irrigation requirement (GIR) to plan for irrigation application during the growing stages of the selected crops. The total CWR of potato, chili, and brinjal was estimated to be 260.6 mm, 262.1 mm, and 274.2 mm, respectively, and the total NIR of potato, chili, and brinjal sowed on the 1st of October was found to be 162.8 mm, 149.0 mm, and 167.2 mm, respectively, with an irrigation efficiency of 70%. In December, the CWR and NIR of potato and brinjal were found to be the highest. The month with the least effective rainfall (8.5 mm) was December. The findings of the study showed that efficient irrigation water management and irrigation scheduling can be achieved by using the CROPWAT model to maximize crop yield and minimize crop water stress.