Reference Evapotranspiration Values Research Articles

Estimating Crop and Reference Evapotranspiration and Crop Factor and Some Growth Indicators for Wheat and Barley in Dhi Qar Governorate Using the CropWat Program

Information about the average water consumption or evapotranspiration of crops is the basis for scheduling irrigation and the water requirements of plants, and choosing the appropriate crops for the region in light. This experiment was carried out to evaluate and calculate the ability of the CropWat program to estimate evapotranspiration rates, and plant consumptive water use for strategic crops in Iraq, including wheat and barley in Dhi Qar Governorate. The results of the study showed that the maximum root depth ranged between 1.20 m, and 1.10 m in the distance between the vegetative growth and flowering stages for wheat and barley crops, respectively. It was also noted that the crop evapotranspiration (ETc) values decreased at the beginning of the growing season, ranging from 0.79 mm day–1 to 1.57 mm day–1 for wheat and barley crops, respectively. Then, the values increased with the progress of the growing season and the increase of the vegetative mass of the plants, reaching 3.72 mm day–1 and 3.46 mm day–1 for wheat and barley crops, respectively. The reference evapotranspiration (ETo) values were also low in the first months of the year and were around 2.1 mm day–1 at the beginning, then gradually increased with the progress of the months of the year, reaching the maximum reference evapotranspiration values of 10.5 mm day–1 in the seventh month, and then the values decreased at the end of the season for wheat and barley crops.

Regional calibration of the Hargreaves model for estimation of reference evapotranspiration

Abstract Estimation of reference evapotranspiration values is crucial in climatological and hydrological research, agricultural engineering, and irrigation design. The Penman-Monteith method, endorsed by the Food and Agriculture Organization (FAO) of the United Nations and numerous research studies, is widely regarded as the gold standard. However, its extensive data requirements limit its applicability in regions with sparse meteorological networks or limited measurement capabilities. The Hargreaves method, which requires only basic air temperature inputs, offers an alternative solution. The aims of this study were to calibrate the Hargreaves model for Central European climate conditions, considering altitudinal dependence, and to evaluate the temporal stability of the model parameters. In the first part of the research, we regionalized the Hargreaves coefficients using a curve-fitting method to ensure the best accuracy across 60 climatological stations in Slovakia. The regionalization of the Hargreaves coefficient improved accuracy by 10.1%, reducing the weighted absolute percentage error (WAPE) to 17.9%. However, our results showed that the accuracy of the modified Hargreaves model decreased with the increasing altitude of a climatological station. Incorporating altitude into the Hargreaves equation significantly improved model accuracy in stations at higher altitudes, providing a consistent level of accuracy across all climatological stations, regardless of their location and altitude. The results also indicated that the optimal model coefficient values change over time, showing a decreasing trend of –0.5 for the B coefficient and –0.1 for the C coefficient between the periods 1981–2000 and 2001–2020. Although regionalizing the Hargreaves model coefficients for local conditions can achieve good model performance, the model’s accuracy is not stable over time. Thus, periodic validation of the model is necessary for short-term applications.

Single vs dual source surface energy balance model based actual evapotranspiration estimation

The current study aims to inter-compare the performance efficiency of the single and the dual source surface energy balance modeling approaches, namely EEFlux and SETMI, respectively for real time catchment scale - crop water demand estimations. For this, the afore-stated two surface energy balance modelling approaches were applied on the Narmada Canal Project, Sanchore, Rajasthan, India for estimating catchment scale actual evapotranspiration (ETa) values for the Rabi cropping seasons of the years 2013-14 and 2018-19, after incorporating the basic satellite data derived inputs viz. Land use, Land surface temperature and Gridded weather data. Due to the non-availability of the catchment scale ground based daily reference evapotranspiration (ETo) values for the study area, the Global Land Data Assimilation System based gridded meteorological data product was utilized, as a substitute for obtaining observed actual evapotranspiration (ETa) values for the investigated Rabi seasons of the study area. These actual evapotranspiration values were compared with those estimated through the single source, EEFlux and the dual source, SETMI modelling approaches to ascertain their comparative performance efficiency through the use of the five statistical indices viz. Mean Absolute Error, Root Mean Square Error, Mean Bias Error, Nash-Sutcliffe Efficiency and the Index of Agreement. The investigations revealed almost at par performance of the two modelling approaches. However, it was concluded that in contrast to the more detailed dual source approach i.e., SETMI, the simple single source approach i.e., EEFlux seemed to be more promising due to its user-friendly implementation and input data automation.

Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting

Reference evapotranspiration is a critical hydrological measurement closely associated with agriculture. Accurate forecasting is vital in effective water management and crop planning in sustainable agriculture. In this study, the future values of reference evapotranspiration are forecasted by applying a recurrent long short-term memory neural network optimized using the Coronavirus Optimization Algorithm, a novel bioinspired metaheuristic based on the spread of COVID-19. The input data is sourced from the Sistema Agrometeorológico para a Gestão da Rega no Alentejo, in Portugal, with meteorological data such as air temperature or wind speed. Several baseline models are applied to the same problem to facilitate comparisons, including support vector machines, multi-layer perceptron, Lasso and decision tree. The results demonstrate the successful forecasting performance of the proposed model and its potential in this field. In turn, to gain deeper insights into the model’s inner workings, the SHapley Additive exPlanation tool is applied for explainability. Consequently, the study identifies the most relevant variables for reference evapotranspiration forecasting, including previously measured evapotranspiration values. Additionally, a univariable model is tested using historic evapotranspiration values as input, offering a comparable performance with a considerable reduction of computational time.

Investigation of the effect of climate change on reference evapotranspiration in Tekirdag, Turkiye

It is important to determine the impact of global climate change on water resources. In the effective use of water resources, the amount of water required for agricultural irrigation should be planned under these conditions. In this study, the trend analysis of the climate data required for the calculation of reference evapotranspiration (ET0) between 2011 and 2020 for the meteorology station of Tekirdag, Turkey was carried out. Analysis of change in the study was carried out according to the methods of Linear Regression Test, Mann-Kendall Test and Sen T Test. As a result of the change analysis, it was determined that the reference evapotranspiration (ET0) values had a decreasing trend in June and July and this decrease was statistically significant. As a result of the analysis of the changes in the climate parameters used in the calculation of reference evapotranspiration (ET0), it was determined that the values of maximum temperature, average relative humidity and average sunshine duration were in a decreasing trend, while minimum temperature and wind speed values were in an increasing trend. In the study, the change in monthly precipitation amounts during the measurement periods was also examined. As a result of the analysis of the changes, it was determined that the monthly precipitation amounts have a decreasing trend and the changes especially in April, September, October and December are statistically significant. It is thought that the results obtained will contribute to the future agricultural production planning in the regional conditions.

Assessing the feasibility of applying remote sensing technique in calculating water consumption

Abstract Remote sensing techniques are currently used in different fields including irrigation and water management fields. One of the important fields is the calculation of water consumption (WC). Remote sensing techniques can be used to estimate actual evapotranspiration and it can also be used to estimate crop coefficients based on spectral reflectance of vegetation indices (VIs), and from a relation with normalized difference vegetation index (NDVI), which is an indicator for the absorption and the reflection ratios by the green plant. The current study used remote sensing data to calculate WC for El-Bostan irrigation district. Crop coefficient values were calculated from NDVI and reference evapotranspiration values were calculated using CROPWAT program. The obtained results were verified by comparing them with field measurements. There was a strong linear correlation between the measured and calculated values with r2 of 0.90, while the root-mean-square error (RMSE) was 0.68. The results were also verified by comparing irrigation efficiencies obtained from remote sensing and from field measurements. The results were very close to each other. The study illustrated the importance and the reliability of using remote sensing techniques in calculating WC values, and which could improve water management and water use efficiency.

Predictive Modelling of Reference Evapotranspiration Using Machine Learning Models Coupled with Grey Wolf Optimizer

Mismanagement of fresh water is a primary concern that negatively impacts agricultural productivity. Judicious use of water in agriculture is possible by estimating the optimal requirement. The present practice of estimating crop water requirements is using reference evapotranspiration (ET0) values, which is considered a standard method. Hence, predicting ET0 is vital in allocating and managing available resources. In this study, different machine learning (ML) algorithms, namely random forests (RF), extreme gradient boosting (XGB), and light gradient boosting (LGB), were optimized using the naturally inspired grey wolf optimizer (GWO) viz. GWORF, GWOXGB, and GWOLGB. The daily meteorological data of 10 locations falling under humid and sub-humid regions of India for different cross-validation stages were employed, using eighteen input scenarios. Besides, different empirical models were also compared with the ML models. The hybrid ML models were found superior in accurately predicting at all the stations than the conventional and empirical models. The reduction in the root mean square error (RMSE) from 0.919 to 0.812 mm/day in the humid region and 1.253 mm/day to 1.154 mm/day in the sub-humid region was seen in the least accurate model using the hyperparameter tuning. The RF models have improved their accuracies substantially using the GWO optimizer than LGB and XGB models.

Documenting evapotranspiration and surface energy fluxes over rainfed annual crops within a Mediterranean hilly agrosystem

The current study aims to document evapotranspiration and associated surface energy fluxes for rainfed annual crops within a Mediterranean hilly agrosystem, in order to provide information on crop water use under such little-studied conditions. For this, an experimental study is conducted within the Tunisian study site of the OMERE observatory (French acronym for the Mediterranean Observatory of Water and the Rural Environment), located in the north-eastern Cap Bon peninsula. It relies on eddy covariance (EC) measurements at the plot scale. We report that (1) observations are consistent with previous studies under Mediterranean or semi-arid contexts, with time series of energy fluxes that depict classical seasonal dynamics, (2) common flux ratios (i.e., Bowen Ratio, ratio of actual to reference evapotranspiration) may change according to upwinds and downwinds, which requires further investigations about possible changes in aerodynamic conditions, and (3) a reference evapotranspiration value of 4 mm day−1 seems to be a threshold beyond which actual evapotranspiration decreases systematically and rapidly. In terms of agricultural water management, the current study suggests to look for early sowing species/varieties, in order to reduce the evaporation-based water loss in autumn. Overall, EC measurements seem promising over rainfed annual crops within semiarid hilly agrosystems, for long term observations, environmental modelling and operational purposes. Since the current study is conducted over few small fields within a specific hilly topography, the original results we report here need to be strengthened with complementary studies.

Quantification of water requirement of some major crops under semi-arid climate in Turkey.

The Southeastern Anatolian Region of Turkey is located in semi arid climate zone and therefore requires an efficient water use. Well-planned irrigation with optimum water required by the crops is essential for the limited water resources of the region. The numerical tool CROPWAT of the Food and Agriculture Organization (FAO) was used for modelling efficient irrigation of local crops pistachio, olive, almond and grape without reducing the yield. Local climatic, soil, plant and rainfall information were used as inputs to CROPWAT model to predict the reference evapotranspiration (ETo) values. The crop water requirement (CWR) for pistachio, olive, almond, and grape was calculated as 1,294.0 mm, 659.4 mm, 790.2 mm, and 752.0 mm, respectively, The number of irrigation needed during growth stages was determined as eight for pistachio, three for olive, six for almond and five for grape.

Yarıkurak sahalarda referans evaporanstpirasyonun (Eto) alansal dağılımı ve zamansal değişimi: Şanlıurfa örneği

İklimdeki değişimlerin, kuraklığın yıl içerisinde uzun süre devam ettiği sahalarda hidrolojik döngüve su temini üzerine etkisinin araştırıldığı bu çalışmada Şanlıurfa ili sınırları içerisinde yeralan 8 istasyona ait (Şanlıurfa, Akçakale, Birecik, Bozova, Ceylanpınar, Hilvan, Siverek ve Viranşehir)1980-2020 yılları arası aylık minimum sıcaklık, maksimum sıcaklık, toplam yağış, ortalamarüzgar hızı, güneşlenme süresi, ortalama nispi nem ve radyasyon şiddeti verileri kullanılarakETo değerlerinin alansal ve zamansal değişimleri ortaya konulmuştur. FAO-Penmann-Monteithreferans evapotranspirasyon (ETo) değerleri, FAO tarafından geliştirilen CROPWAT yazılımıkullanılarak hesaplanmıştır. Elde edilen ETo değerlerinin alansal ve zamansal olarak değişiminigösteren haritaların oluşturulmasında ise ArcGIS yazılımı kullanılmıştır. Özellikle tarımsal etkinliğinyoğun olduğu güney istasyonlarda ETo değerlerinin eğilimlerinde istatistiksel olarak oldukçakuvvetli pozitif artışlar gözlenmiştir. Ayrıca, Aridity Index kuraklık hesaplama yöntemi ile sahadayarıkurak şartların hakim olduğu ortaya konulmuştur. GAP’ın önemli ayaklarından biri olan Şanlıurfa’datarımsal faaliyelerde kullanılacak su ihtiyacının belirlenmesinde ETo değerlerinin bilinmesielzemdir. Bu sebeple FAO tarafından önerilen Penmann-Monteith yönteminin planlamaçalışmalarında göz önünde bulundurulması faydalı olacaktır. Su isteğinin bitkiden bitkiye ve herbitkinin de vejetasyon devresinin dönemine bağlı olarak değişmesi ETo değerlerinin ürüne görespesifik olarak hesaplanmasını gerekli kılmaktadır.

Calibration and evaluation of six popular evapotranspiration formula based on the Penman-Monteith model for continental climate in Turkey

Due to the difficulties in measuring and collecting meteorological data, it may be necessary to use methods that allow for the estimation of reference evapotranspiration values by using the few available data. For this reason, it is useful to calibrate empirical formulas locally, which enables obtaining accurate and reliable estimates with a small number of data. The effects of other parameters excluded from the employed data are included in the formula as regional coefficients. In this study, six empirical evapotranspiration formulas among temperature-based and solar radiation-based methods, including Thornthwaite (1948), Makkink (1957), Turc (1961), Jensen and haise (1963), Priestley and Taylor (1972), and Hargreaves and samani (1985) are calibrated, for continental climates of Central Anatolia's. To this end, meteorological data of three synoptic stations located in the west of Turkey in Kutahya province are employed from 2007 through 2019. Moreover, the data of the fourth station are applied for evaluating the accuracy of the estimated results. The main reason for employing these six formulas are behind in the fact that they are globally used, easy-to-use, and need few types of data. Meanwhile, Penman-Monteith 56 equation is applied to calibrate the empirical equations. In total, three methods are followed to estimate the local calibration coefficients. Finally, the evaluation of the results is performed by using the coefficient of determination, percentage error of estimate, mean absolute error, and root-mean-square error. According to the evaluation criteria, among calibrated formulas, Jensen and haise's (1963) formula was obtained to be the best for estimating ETo.

AgroML: An Open-Source Repository to Forecast Reference Evapotranspiration in Different Geo-Climatic Conditions Using Machine Learning and Transformer-Based Models

Accurately forecasting reference evapotranspiration (ET0) values is crucial to improve crop irrigation scheduling, allowing anticipated planning decisions and optimized water resource management and agricultural production. In this work, a recent state-of-the-art architecture has been adapted and deployed for multivariate input time series forecasting (transformers) using past values of ET0 and temperature-based parameters (28 input configurations) to forecast daily ET0 up to a week (1 to 7 days). Additionally, it has been compared to standard machine learning models such as multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), extreme learning machine (ELM), convolutional neural network (CNN), long short-term memory (LSTM), and two baselines (historical monthly mean value and a moving average of the previous seven days) in five locations with different geo-climatic characteristics in the Andalusian region, Southern Spain. In general, machine learning models significantly outperformed the baselines. Furthermore, the accuracy dramatically dropped when forecasting ET0 for any horizon longer than three days. SVM, ELM, and RF using configurations I, III, IV, and IX outperformed, on average, the rest of the configurations in most cases. The best NSE values ranged from 0.934 in Córdoba to 0.869 in Tabernas, using SVM. The best RMSE, on average, ranged from 0.704 mm/day for Málaga to 0.883 mm/day for Conil using RF. In terms of MBE, most models and cases performed very accurately, with a total average performance of 0.011 mm/day. We found a relationship in performance regarding the aridity index and the distance to the sea. The higher the aridity index at inland locations, the better results were obtained in forecasts. On the other hand, for coastal sites, the higher the aridity index, the higher the error. Due to the good performance and the availability as an open-source repository of these models, they can be used to accurately forecast ET0 in different geo-climatic conditions, helping to increase efficiency in tasks of great agronomic importance, especially in areas with low rainfall or where water resources are limiting for the development of crops.

Transfer learning augmented enhanced memory network models for reference evapotranspiration estimation

Accurate estimation of reference evapotranspiration is a prerequisite for achieving sustainable development, designing irrigation plans, and efficient utilization of water resources. Traditionally, several conventional and statistical methods have been developed for this time-series prediction task. However, these methods are not well capable of handling the non-linear complexities, historical dependencies of the data and are majorly external features driven. In recent years, machine learning and deep learning models have gained significant attention for achieving state-of-the-art results in nearly all engineering domains. In this context, the present research study proposes three deep learning-based models to estimate future evapotranspiration values. Initially, a moving window-based baseline model is proposed for prediction by utilizing the minimum available historic evapotranspiration data only. The proposed model provides support for handling historical data dependencies by employing the Long Short Term Memory Network (LSTMN) model. Then, the concept of transfer learning is introduced/augmented for improving the prediction performance of the baseline model. Two transfer learning augmented deep learning prediction models (TL−LSTM1 and TL−LSTM2) are developed to forecast the future reference evapotranspiration values. The application of the proposed three models (Baseline, TL−LSTM1 and TL−LSTM2) is demonstrated on the dataset of nine different locations of State Punjab, India. The performance of the proposed models is compared with the prediction results of three supervised benchmark models in terms of well-known performance measures. The overall prediction results indicate that the transfer learning-based models achieve promising results.

Evaluation of crop water demand for sustainable crop production using geospatial tools in a canal command of West Bengal


 The agricultural sector is the primary consumer of water resources around the world, and the need for additional food production for growing population further exerts more pressure on water resources. In this study, crop water demand was assessed spatially and temporally for a case study area, Damodar Canal Command (DCC) using geospatial techniques. Crop evapotranspiration was estimated for all the crop seasons using reference evapotranspiration and Fraction of Vegetation cover (FV) that was used as a surrogate for crop coefficient. The reference evapotranspiration (ET ) was calculated using the FAO o Penman-Monteith method. FV was computed based on Normalized Difference Vegetation Index (NDVI) derived from MODIS satellite imagery and its value ranges from 0 to 1. The maximum and minimum reference evapotranspiration values were estimated as 8.44 and 1.88 mmday-1 in May and September, respectively during the normal year 2004. The average monthly crop water demand was maximum in May i.e. 8.08 mmday-1. Among all crop seasons, Boro season has the maximum crop water demand followed by Aus and Aman seasons with maximum ET as 496, 438 and 328 mm, respectively. Total annual crop c water demand for normal year, 2004 was estimated at 1237 mmyr-1 in the study area. Spatially and temporally distributed crop water demand estimates help the irrigation planners to devise the strategies for effective irrigation management.

Improving silage maize productivity using recycled wastewater under different irrigation methods

The contribution of wastewater irrigation with the improvement of irrigation practices to the productivity of silage maize is a priority issue to investigate considering the saving of scarce freshwater resources and the necessity to dispose wastewater. The aim of this study was to evaluate the effect of different irrigation levels (L100, L67 and L33) of recycled municipal wastewater (RW) compared to freshwater (FW) using subsurface drip (SSDI), surface drip (SDI) and furrow irrigation (FI) methods on yield and some physiological traits of silage maize in semi-arid conditions at a high altitude. When the total daily reference evapotranspiration value reached 50 ± 5 mm, irrigation quantities corresponding to 100%, 67% and 33% of soil moisture deficit based on field capacity in fully irrigated plots with freshwater were applied in different irrigation levels. Crop actual evapotranspiration (ETa) values were found close in both water types. SSDI reduced ETa by 18.5% and 45.6% in L100 level, 15.2% and 38.9% in L67, and 11.6% and 32.6% in L33, respectively compared to SDI and FI. The highest fresh biomass yield was determined in the SSDI-RW-L100 combination as 77.55 t ha-1, and resulted in 5% and 12.9% higher values than in SDI and FI. Leaf relative chlorophyll and water contents, leaf area index and electrolyte leakage showed strong linear correlations with yield and evapotranspiration values. The highest water productivity was determined in the SSDI-RW-L100 combination as 21.48 kg m-3 and it was higher by 28.2% and 99.4% than those in SDI and FI, respectively. Improvement of productivity with increased irrigation quantities in SSDI delivered the high yield response factor of 1.70–1.77. Therefore, it is concluded that the SSDI method can be a successful practice to improve productivity by alleviating the need for water for silage maize especially under full irrigation with RW.

Estimation of irrigation water requirement of strawberry crop under polyhouse and shadenet house conditions

Strawberry is a commercial crop with high added value, which was extended to new cultivation zones of India.Therefore, it is important to know the suitability of climate condition for growing strawberry in Indian condition. Protected cultivation structures are used to cultivate crops under partial controlled climatic condition to get higher yield and better quality harvest. There are different kinds of protected cultivation structure normally adopted in India such as greenhouse, shadenet house and low tunnels. Exact of amount water and nutrients required to applied to get higher yield through minimizing loss of quality. The objective of the study is to assess the effect of protected cultivation structure on ambient temperature, relative humidity and crop water requirement of strawberry with drip irrigation system grown during winter season (November-February). Reference evapotranspiration was calculated using the FAO-56 Penman-Monteith equation considering the locally recorded weather parameters. Monthly average of daily reference evapotranspiration values are ranging between 1.3 to 3.3 mm day-1, 1.4 to 3.7 mm day-1 and 2.0 to 4.9 mm day-1 for polyhouse, shadenet house and open field, respectively. The total water requirement of drip irrigated straw berry in protected cultivation structure is reduced by about 35.2 % under polyhouse and 25.5 % under shade net house in comparison to open field cultivation.

Application of the Hargreaves method for calculating the potential evapotranspiration in the hydrological modelling

To improve the results of the Slovenian Environment Agency’s hydrological forecasting system, especially in the river basins with lower specific runoff (Pomurje) and during high water events following a long dry period, we decided to find a good method for calculating daily values of the potential evapotranspiration (PET). We were deciding between several temperature-based methods for the daily reference evapotranspiration (ET0) values calculation. For selected meteorological stations we calculated ET0 using three different methods and then compared them to the ET0 values calculated using the much more complex Penman-Monteith method. Among the tested temperature methods the results given by the Hargreaves method fitted best to the results of the Penman-Monteith method. The reason for this may lie in the fact that the Hargreaves method, besides the mean daily air temperature as other temperature-based methods, considers the daily temperature range as well. Afterwards, considering the ground cover factor, we calculated the PET values from the ET0 values and then applied them in the hydrological modelling. The model setups for the Sava, Soča, and Mura Rivers were reanalysed twice, considering firstly the climatologic monthly PET values that were already used in the hydrological forecasting system of the Slovenian Environment Agency for many years, and, secondly the daily PET values calculated according to the Hargreaves method and using hourly air temperature 2 m above the ground, originating from the short-term weather forecasting model ALADIN or the INCA/AT meteorological system. At all selected calculation points, the model setups using daily PET values showed better performance over the model setups using climatological monthly values.

Analisis Evapotranspirasi Menggunakan Metode Penman-Monteith pada Vertical Garden

The development of environmentally friendly infrastructure is very important to reduce the environmental damage that has occurred. Environmentally friendly infrastructure can be supported by vertical garden. Plants in vertical garden undergo evaporation process called evapotranspiration process. The evapotranspiration process can be used to find out the needs of water in plants. The method used to obtain the evapotranspiration value is the Penman-Monteith method. The plants used are Plectranthus scutellarioides, Begonia, Coleus, Euodia ridleyi dwarf, Euodia ridleyi, and Chlorophytum comosum. The reference evapotranspiration value in The Hydraulic Laboratory of Sriwijaya University is 4.9826 mm/day and the smallest is 2.1262 mm/day. The reference evapotranspiration value is influenced by temperature, wind speed, and humidity conditions. Based on these three influences, temperature has a greater influence on the reference evapotranspiration. The largest evapotranspiration value of the six types of plants used is the Plectranthus scutellarioides, which is 3.3347 mm/day, the evapotranspiration value of the smallest plant is Euodia ridleyi dwarf, which is 2.6616 mm/day. The location and arrangement of plants and environmental conditions such as temperature, humidity, wind speed affect the amount of evapotranspiration value.

Potreba za navodnjavanjem kukuruza na zemunskom černozemu u poslednjih pola veka

The aim of this paper is to examine the water regime of chernozem under maize crops in the last half century (1966-2019) and to determine whether during that period and to what extent, there was an increase or decrease in maize irrigation requirements. The mathematical plant model FAO CROPWAT 8.0 was used for the calculation. The calculation was performed on the basis of monthly values of reference evapotranspiration (ETo) for the period 1966-2019, calculated by the modified Hargreaves method, daily values of precipitation from the meteorological station Surcin, data on the selected plant, which are in accordance with FAO56. The soil is chernozem on the Zemun les terrace. The analysis was performed by dividing the research period into three subperiods: the first twenty (1966-1985), the second twenty (1986-2005) and the last fourteen (2006-2019) years. It was found that the average values of potential evapotranspiration of maize were increasing, starting from the first (500mm) to the third (562mm) subperiod, while the average values of actual evapotranspiration, as well as the average amount of effective precipitation in the vegetation period of maize, decreased. Consequently, the average water deficit, i.e. maize irrigation requirements was increased by 56%, starting from the first (205mm) to the third (319mm) subperiod of the research. The increase in the water deficit also caused an increase in the projected reduction in maize yield related to its genetic capasity, which averaged 31% in the first subperiod and 47% in the third. Analysis of the results on a monthly and decadal level showed that maize irrigation requirements lasts from June to August, with a maximum in the second decade of July. In all three summer months, an increase in maize irrigation requirements was registered from the first to the third subperiod of the research, with the maximum increase during July. The conducted research, which generally gives an insight into the state of the water regime of Zemun chernozem in the last half century, shows that the conditions of maize production in the natural rain regime are deteriorating and that the irrigation requirement is increasing.

Water requirements for irrigated crops in semi-arid region in Jordan using sentinel satellite images

The agricultural sector consumes most of the groundwater in Jordan, and Mujib basin is one of the most important available ground basins. Unsupervised classification has been applied on multitemporal satellite image for an arid region such as Mujib basin in order to identify the types of irrigated agricultural crops present in it, where it consisted of vegetables, field crops and fruit trees. Then find the reference evapotranspiration values and estimate crop coefficient values for each crop to calculate the water requirements of irrigated crops in order to find the quantities of water consumption in the basin which amounted to 29.7 million cubic meters. Where area of irrigated crops reached about 59 km2, which represents 1% of the total basin area and its concentrated in the northern and eastern regions, while rainfed agricultural activity was found in the western part of the basin. Vegetables are consumed high quantities of water, especially summer ones. Where this water is invaluable, and its importance will increase greatly soon as a result of high evaporation rates and scarcity of water resources in the region besides presence the political conflicts on these water resources. The classification of lands using multitemporal images from Sentinel satellite led to obtain more accurate results about the irrigated agricultural crops in terms of cultivation dates, duration, harvest time, monitoring the crop, determining its type and calculating its area.