Potential Crop Evapotranspiration Research Articles

A New Agricultural Drought Index to Characterize Agricultural Drought Using Data Mining Techniques

Drought monitoring is a critical task as its occurrence and extent vary according to many factors like drought type, risk, agricultural losses, and impact. Monitoring drought is important because the footprint of this hazard is larger than that of other natural hazards. Many drought indices are developed to monitor complex drought conditions. The intensity and severity of drought in a particular region and at a particular time can be tracked by the drought indicator. In this research, a new agricultural drought index, Yield-Evapotranspiration Drought Index (YEDI) is developed using crop yield, potential, and reference crop evapotranspiration. Data mining and Neural Network techniques have been used to model the drought index. The agricultural and climatic data used is selected from the year 1983 to 2015 (33 years) from the period of June to October (Kharif period) for Maharashtra state in India. The drought index generates the positive values which are further divided into a range of high, medium, and low intensities of drought. SPI and SPEI indices are used for validation against YEDI. Results show that there is a correlation between YEDI and SPEI whereas a low correlation is between YEDI and SPI. YEDI proves to be useful for agricultural drought monitoring.

Hydrological digital twin model of a large anthropized italian alpine catchment: The Adige river basin

Understanding and simulating the hydrological cycle, especially in a context of climate change, is crucial for quantitative water risk assessment and basin management. The hydrological cycle is complex as it is a combination of non-linear natural processes and anthropogenic influences that alter landforms and water flows. Human-induced changes of relevance, including changes in land uses, construction of dams and artificial reservoirs, and diversion of the river course, lead to changes in water flows throughout the basin. These should be explicitly accounted for a realistic representation of the anthropogenically altered hydrological cycle. Such a realistic representation of the hydrological cycle is a necessary input for the water risk assessment in a particular region. In this paper, we present a hydrological digital twin (HDT) model of a large anthropized alpine basin: the Adige basin located in the northeast of Italy.Most catchments model often overlook land-uses changes over time and forget to model reservoir operation and their influence over time on water flow. Yet, for example, the Adige basin has>30 reservoirs affecting the water flow. We therefore use the GEOframe modeling framework to demonstrate the ability to create a hydrological twin model accounting for these anthropogenic changes.Specifically, we model each component of the water cycle over 39 years (1980–2018) at daily timescale through calibration of the Adige HDT with a multi-site approach using discharge data of 33 stations, based on a high-resolution (1 km) temperature and precipitation dataset and a calculated crop potential evapotranspiration (PETc) dataset, which accounts for human-induced change of the land cover over time. The modeling system also includes the simulation of artificial reservoirs and dams by the dynamically zoned target release (DZTR) reservoir model.The Adige HDT is assessed/validated/compared through a variety of hydrological processes (i.e., river and reservoir discharges, PETc and actual evapotranspiration, snow, and soil moisture) and data sources (i.e., observations and remote sensing data).Overall, the HDT reproduces well the measured discharge in space and time with a Kling Gupta Efficiency (KGE) above 0.7 (0.8) for 30 (23) of the 33 gauge-stations. For 7 artificial reservoirs with available data, the reservoir turbinated discharges are successfully reproduced with an average KGE of 0.92. A comparison between modeled and MODIS remote sensing snow data showed an average error of < 10% across the entire basin; the model also presented a good spatio-temporal agreement both with GLEAMS potential (and actual evapotranspiration) with an average KGE of 0.63 (0.60) and a high-level of correlation (0.5 on average) with the ASCAT satellite retrieved soil moisture.The findings of this paper demonstrate the potential of the open-source, component-based, GEOframe system to build a HDT, to provide a reliable and long term (39 years) estimation of all the water cycle components in a complex anthropized river basin at high spatial resolution. Spatially detailed HDT models results of this type can be used to inform basin-wise adaptation policy decisions and better water management practices in a time of changing climate.

On the Pattern and Attribution of Pan Evaporation over China (1951–2021)

Abstract Pan evaporation (Epan) serves as a monitorable method for estimating potential evaporation, evapotranspiration, and reference crop evapotranspiration, providing crucial data and information for fields such as water resource management and agricultural irrigation. Based on the PenPan model, the monthly Epan was calculated over China during 1951–2021, resulting in an average R2 of 0.93 ± 0.045 and an RMSE of 21.48 ± 6.06 mm month−1. The trend of Epan over time was characterized by an initial increase before 1961, followed by a decrease from 1961 to 1993, and a subsequent increase from 1994 to 2021. However, the sustained duration and magnitude of the decreasing trend led to an overall decreasing trend in the long-term dataset. To better understand the drivers of Epan trends, the Epan process was decomposed into radiative and aerodynamic components. While radiation was found to be the dominant component, its trend remained relatively stable over time. In contrast, the aerodynamic component, although smaller in proportion, exhibited larger fluctuations and played a crucial role in the trend of Epan. The primary influencing factors of the aerodynamic component were found to be wind speed and vapor pressure deficit (VPD). Wind speed and VPD jointly promoted Epan before 1961, and the significant decrease in wind speed from 1961 to 1993 led to a decrease in Epan. From 1994 to 2021, the increase in VPD was found to be the main driver of the observed increase in Epan. These results show the complex and dynamic nature of Epan and underscore the need for continued monitoring and in-depth analysis of its drivers. Significance Statement The primary objective of this study is to explore the spatiotemporal patterns and potential driving factors of pan evaporation in China based on constructing a comprehensive dataset of pan evaporation. This is important because pan evaporation is an important indicator of the water cycle, which is currently undergoing modifications and is expected to become more pronounced as the climate continues to warm. Our findings showed that the patterns of pan evaporation were characterized by its drivers. As the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to the pattern and attribution of pan evaporation.

Assessment of Crop-Water Requirement of Alfalfa Using FAO-CROPWAT Model-8.0

The current study was conducted at the Farming System Research Station, situated within the Department of Agronomy at the College of Agriculture, Junagadh Agricultural University in Junagadh, Gujarat, India. This research took place during the winter cycle (Rabi season) 2021 to 2022 and 2022 to 2023. In this study, the CROPWAT crop irrigation schedule provides an overall Evapotranspiration (ETc) value of 549.6 millimeters as the optimal water requirement for achieving maximum crop production with sufficient water supply. The actual water use by the crop during its growth period amounted to 544.2 millimeters and total net irrigation applied was 507.9 millimeters, while the gross irrigation amounted to 725.6 millimeters. The irrigation efficiency was 100%. Comparing this approach to irrigation scheduling based on the Irrigation Water to Crop Potential Evapotranspiration (IW/CPE) ratio, using CROPWAT enabled the precise management of irrigation water. It ensured that the alfalfa crop received the appropriate amount of water according to its specific growth stage requirements. The CROPWAT model data indicates that there was no yield reduction, even with a 100% critical depletion of available soil moisture. Additionally, the seasonal yield response factor was calculated to be 0.80%. These findings emphasize the effectiveness of the irrigation strategy employed, resulting in optimal crop yields without yield losses due to critical soil moisture depletion.

Climate risk zoning for wheat crops in the southeastern region of Brazil.

Wheat (Triticum aestivum L.) is the second most consumed food in the world. One way to meet this demand is the expansion of wheat cultivation to the Brazilian Cerrado in the southeastern region. However, one of the major limitations is that there are few studies related to wheat climate risk zoning. Thus, this study aimed to determine the agroclimatic zoning of wheat by estimating the water needs satisfaction index (ISNA) in the southeastern region of Brazil. For this purpose, a 60-year historical series of meteorological data was used to calculate the potential evapotranspiration, crop evapotranspiration, and climatological water balance values. To define the agroclimatic zones of wheat and sowing date, the ISNA method was used. The data were analyzed using descriptive statistics to determine the variations. To obtain the agroclimatic zoning of wheat, the geostatistical method of kriging interpolation was used. The regions with the highest rainfall are the south of Minas Gerais and the coast of São Paulo. The sowing period directly impacts the development of the crop, the available water capacity and the ISNA values indicated the spring and summer had better cultivation conditions, and the best window for wheat cultivation is concentrated in the fall due to the limitation of biotic factors. In terms of altitude (>700 m), Minas Gerais has 39.4% of the area suitable for wheat cultivation. Thus, climatic variations within and between the states of the southeastern region should be considered for the positioning of wheat cultivars in these regions to obtain the maximum yield. © 2023 Society of Chemical Industry.

CNN deep learning performance in estimating nitrate uptake by maize and root zone losses under surface drip irrigation

Optimal fertigation regimes will minimize the leaching of agrochemicals while providing crops with sufficient nutrition for growth. A comprehensive objective function of optimization involves minimizing (i) difference between weekly plant nitrogen uptake and requirement and (ii) nitrate losses (nitrate leaching and accumulated nitrate in the root zone). Coupling a simulation model to an optimization algorithm for obtaining the objective function variables (N plant uptake and nitrate losses) typically requires long runtimes as the simulation model is repeatedly applied over iterations of the optimization algorithm. One attractive way to reduce the programming and computational demands associated with optimization is to construct a simpler, faster, but sufficiently accurate emulator of the simulation model based on machine-learning model. This study assessed the performance of convolutional neural networks (CNN) in modelling nitrate uptake by maize and estimation of nitrate losses in surface drip irrigation in three texturally different soils. To do this, the CNN was trained on output of the simulation model HYDRUS-2D. Various combinations of parameters influencing daily nitrate uptake (daily potential crop evapotranspiration, irrigation water and injected fertilizer amount) were evaluated as model inputs. Results indicated that daily nitrate uptake at any time t was predicted best with inputs of daily potential evapotranspiration at time t, irrigation and fertilizer amounts of the previous 1–7 days and the previous day’s nitrate uptake amount. Estimated nitrate losses for constant weekly fertigation (CWF) were about 32% in sandy loam and 38.5% in sandy clay loam soil. Variable weekly fertigation (VWF) reduced the nitrate losses to 24.5% in sandy loam and 31.5% in sandy clay loam soil, while losses in loam soil were intermediate to these. The study demonstrates that CNN estimation of nitrate uptake and root zone losses can be widely used in optimization of fertigation management.

Trocas gasosas e crescimento da cultura da abobrinha submetida aos estresses salino e hídrico

ABSTRACT Semi-arid regions present the inherent problem of accumulation of salts in the soil due to the use of brackish water for irrigation, and water deficit compromises the growth and physiological indices of crops. This study evaluated the effect of salt and water stress on growth and gas exchange in the zucchini cv. Caserta crop. The study was conducted at the University of International Integration of Afro-Brazilian Lusophony, Redenção, Ceará State, Brazil. The experimental design was completely randomized, in a 5 × 2 factorial scheme with five levels of electrical conductivity of the irrigation water - ECw (0.5, 1.0, 1.5, 2.0, and 2.5 dS m-1) and two water regimes (50 and 100% of the potential crop evapotranspiration - ETc), with five replicates. At 36 days after sowing, the following traits were evaluated: CO2 assimilation rate, transpiration, stomatal conductance, internal carbon concentration, leaf temperature, relative chlorophyll index, and water use efficiency. At 45 days after sowing, the plant height, stem diameter, number of leaves, and leaf area were also evaluated. The use of brackish water (1 to 2.5 dS m-1) reduced the growth parameters of zucchini cv. Caserta. The increase in ECw caused a decline in the physiological traits. Under 100% ETc, higher values of CO2 assimilation rate, transpiration, and instantaneous water use efficiency were recorded, and there was 50% ETc for internal carbon concentration, even with the increase in ECw.

Crop coefficient, reference crop evapotranspiration and water demand of dry‐season Boro rice as affected by climate variability: A case study from northeast Bangladesh

AbstractClimate variability and change significantly influence crop water demand by affecting crop coefficient (Kc), reference crop evapotranspiration (ETo, a grass reference) and potential crop evapotranspiration (ETc). The climatic effects on these hydroclimatic parameters were investigated in the past mostly for the entire crop growth period, but little or no information is available on their variation over different growth stages of the crops, specifically in Bangladesh. Therefore, this study intended to evaluate the climatic effects on these hydroclimatic parameters at the initial, development, mid‐season and late‐season growth stages of two rice varieties (Bangladesh Rice Research Institute [BRRI] dhan28 and BRRI dhan29) dominantly grown in the dry season in two northeast districts/subdistricts (Sylhet and Srimongal) as well as many other districts of Bangladesh. Kc, ET0 and ETc were analysed for the period from 1994 to 2015 using the analytical approach of the Food and Agriculture Organization (FAO) Penman–Monteith method. The results revealed that the Kc of the rice varieties increased in all growth stages over time, with the maximum Kc (1.247) in the mid‐season stage in Srimongal. ETo and ETc were found to be maximum in the mid‐season and late‐season stages, respectively. The potential crop water demand (CWRpot) greatly varied among the initial and development stages and between the two rice varieties. The initial and mid‐season growth stages of rice required more water than the other growth stages. The results on the growth stage‐wise variation of the hydroclimatic parameters pertinent to rice cultivation may greatly contribute to irrigation scheduling for dry‐season rice cultivation and planning water resources in the study region.

Machine learning approach to estimate soil matric potential in the plant root zone based on remote sensing data.

There is an increasing interest in using the Internet of Things (IoT) in the agriculture sector to acquire soil- and crop-related parameters that provide helpful information to manage farms more efficiently. One example of this technology is using IoT soil moisture sensors for scheduling irrigation. Soil moisture sensors are usually deployed in nodes. A more significant number of sensors/nodes is recommended in larger fields, such as those found in broadacre agriculture, to better account for soil heterogeneity. However, this comes at a higher and often limiting cost for farmers (purchase, labour costs from installation and removal, and maintenance). Methodologies that enable maintaining the monitoring capability/intensity with a reduced number of in-field sensors would be valuable for the sector and of great interest. In this study, sensor data analysis conducted across two irrigation seasons in three cotton fields from two cotton-growing areas of Australia, identified a relationship between soil matric potential and cumulative satellite-derived crop evapotranspiration (ETcn) between irrigation events. A second-degree function represents this relationship, which is affected by the crop development stage, rainfall, irrigation events and the transition between saturated and non-saturated soil. Two machine learning models [a Dense Multilayer Perceptron (DMP) and Support Vector Regression (SVR) algorithms] were studied to explore these second-degree function properties and assess whether the models were capable of learning the pattern of the soil matric potential-ETcn relation to estimate soil moisture from satellite-derived ETc measurements. The algorithms performance evaluation in predicting soil matric potential applied the k-fold method in each farm individually and combining data from all fields and seasons. The latter approach made it possible to avoid the influence of farm consultants’ decisions regarding when to irrigate the crop in the training process. Both algorithms accurately estimated soil matric potential for individual (up to 90% of predicted values within ±10 kPa) and combined datasets (73% of predicted values within ±10 kPa). The technique presented here can accurately monitor soil matric potential in the root zone of cotton plants with reduced in-field sensor equipment and offers promising applications for its use in irrigation-decision systems.

Effect of the Forecast Air Temperature Change on the Water Needs of Vines in the Region of Bydgoszcz, Northern Poland

The climatic changes observed in Poland are manifested by an increase in air temperature, but not an increase in rainfall, which causes an increase in the water needs of plants, and hence the necessity to develop irrigation systems for crops. The aim of this study was to assess the water needs of grapevines in 2021–2050 in the Bydgoszcz region (northern Poland), an area with high requirements for supplementary irrigation. The calculations were based on the expected changes in air temperature according to the climate change scenario for Poland: the 4th Intergovernmental Panel on Climate Change—Special Report on Emissions Scenarios: balanced scenario (A1B). The average monthly temperature in 1981–2010 was used in the calculations. The water needs of grapevines were estimated using the reference evapotranspiration calculated using the Blaney–Criddle equation, and then using the crop coefficient and potential (crop) evapotranspiration. It was found that in 2021–2050, the water needs of grapevines, both during the growing season (May–September) and during the irrigation period (June–August), will increase by 6%. The highest and significant increase in water needs, by 10%, should be expected in August. The estimated increase in water needs of grapevines indicates the need to development sustainable irrigation systems for vineyards in northern Poland.

Evapotranspiration and Vegetation Cover Classifications Maps Based on Cloud Computing at the Arab Countries Scale

According to the most recent Köppen–Geiger classification, Arab countries are divided into seven climate classes. Ground data availability is limited in developing countries, and ground meteorological data are scarce and concentrated in a few locations, rather than station maintenance capability being adequate for the responsibilities. The current study uses remote sensing and meteorological data to create regional classification maps of reference evapotranspiration (ETo), potential crop evapotranspiration, and vegetation cover in Arab countries from 2005 to 2020. The Stand-alone Remote Sensing Approach to Estimate Reference Evapotranspiration (SARE) was used to estimate ETo using satellite data from 2005 to 2020. The Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were extracted from MODIS satellite data and used in the SARE model, in addition to elevation (E), Julian day (J), and Latitude (Lat). To validate the SARE model results, the FAO-Penman–Monteith model was applied to 35 ground meteorological stations distributed across Arab countries to cover all climate classes based on the most recent Köppen–Geiger climate classification. Google Earth Engine was used to create the classification. The statistical indices produced acceptable results, with average RMSE values ranging from 6.9 to 17.3 (mm/month), while correlation coefficient (r) and index of agreement (d) values are more significant than 0.9. To be included in the ETc calculation, the crop coefficient (Kc) was calculated using NDVI 250 m spatial resolution. The density of the vegetation cover is used to classify it (low to high). The average vegetation cover was calculated to be greater than 31.5 Mha. The minimum vegetation cover was 14.9 Mha, and the maximum vegetation cover was 49.2 Mha. 15.8 Mha can be cultivated without supplementary irrigation for at least one agricultural season, according to the rainfall classification map.

Variety specific irrigation of almonds during hull split, effects on yield and quality

Regulated deficit irrigation (RDI) during hull-split can reduce water applied in almond orchards. Strategies for imposing RDI in almond orchards with multiple varieties with different hull-split schedules have not been extensively developed. Commonly, irrigation system design requires the same irrigation schedule for all varieties in the orchard. A 2-year study evaluated the impacts of two different regulated deficit irrigation strategies in an almond orchard with Butte, Aldrich, and Nonpareil varieties in the Sacramento Valley of California, USA. The two strategies were (1) RDI in all varieties during Nonpareil hull-split timing and (2) RDI according to variety-specific hull-split timing. The two levels of irrigation were 50 % and 75 % of potential crop evapotranspiration (ETc) replacement during the hull-split period. Results show that the kernel thickness of the Aldrich almonds increased under 75 % ETc irrigation replacement during the Aldrich hull-split period compared to 75 % ETc and 50 % ETc irrigation replacement during the Nonpareil hull-split period. In the Butte almonds, 75 % ETc and 50 % ETc irrigation replacement during variety-specific hull-split reduced the fraction of sealed shells of the Butte variety compared to 75 % ETc and 50 % ETc irrigation replacement during the Nonpareil hull-split period. This study demonstrated that an irrigation system design that allows for the independent irrigation scheduling of each variety can change the almond physical quality in the Butte and Aldrich varieties. RDI according to variety-specific hull-split did not result in significant marketable kernel yield improvements after two years of imposing the irrigation strategies. More research should be conducted for different orchard designs, varieties, and climates.

Salt Accumulation during Cropping Season in an Arid Irrigation Area with Shallow Water Table Depth: A 10-Year Regional Monitoring

Nowadays, irrigation takes up about 35% of agricultural water consumption worldwide, and irrigation induced secondary soil salinizationsalinization affects the crop production and sustainable development of arid irrigation areas globally. However, the regular pattern of salt accumulation in the root zone during the cropping season and the contributions of its attribute factors are still unclear. Therefore, a 10-year monitoring was conducted in the Hetao Irrigation District to reveal the soil salt accumulation during the cropping season and to relate it to influential factors, including potential crop evapotranspiration, water input (field irrigation + precipitation) and water table depth. It was found that under the climate conditions and water-saving irrigation measures of the investigated 10-year period, (1) the salt accumulated during the cropping season could be effectively leached by autumn irrigation and the root zone soil could remain suitable for crop germination, (2) the cropping season water deficit (potential crop evapotranspiration − field irrigation − precipitation) showed strong correlation with the cropping season salt accumulation, and (3) maintaining the cropping season average water table depth larger than a critical depth (roughly 3 m) might be the most economical way to alleviate salt accumulation. Therefore, it is recommended to balance the salt leaching and the water table depth controlling in the future water-saving irrigation management practices.

Differences in Spatiotemporal Variability of Potential and Reference Crop Evapotranspirations

Potential evapotranspiration (ETp) and reference crop evapotranspiration (ETo) are two key parameters in hydrology, meteorology, and agronomy. ETp and ETo are related to each other but have different meanings and applications. In this study, the ETp and ETo were distinguished and calculated with the Penman and FAO56 PM equations using the weather data of 551 stations in China from 1961 to 2018. The differences in their spatiotemporal variations were examined with an MMK test, an R/S test, and wavelet analysis. The monthly ETp and ETo were close but the ETp was always larger than the ETo, with values ranging from 1 to 356 mm and 2 to 323 mm, respectively. Their differences varied in different months and sub-regions. The maximum monthly difference transferred from south to north and then back to the south in a yearly cycle, showing spatiotemporal heterogeneity. The annual values of the ETp and ETo were also close, but the ETp was significantly higher than the ETo. The increasing future trends of ETp but decreasing trends of ETo were tested at most sites in China. Although the primary periods were almost the same, their spatial distribution was slightly different. In conclusion, ETp is different from ETo and they should be applied carefully. This study performs a thorough comparison and reveals the underlying basis of and discrepancy between ETp and ETo.

Internet of Things (IoT) and Machine Learning (ML) Assisted Reference Evapotranspiration (ETO) Estimations

Reference Evapotranspiration (ETo) is the amount of irrigation water required by a model crop to grow at its optimal level. ETo determination is a complex process that requires complicated calculations with many variables involved. There is a need to determine the ETo from available environmental conditions. Internet of Things (IoT) and Machine Learning (ML) based ETo estimation is proposed. IoT-assisted directly captured temperature data from the crop field is used to estimate the ETo. The estimated ETo can be used in many Precisions Agriculture (PA) applications especially in Precision Irrigation (PI) for measurement of Crop Potential Evapotranspiration (ETc), (irrigation water requirements of specific crops) using crop coefficient (Kc). Naive Bays ML algorithm is applied and evaluated for accurate estimations of ETo. The performance of the ML model is evaluated based on accuracy, f-measures, and recall for ETo estimation. Blaney-Criddle method of ETo measurements is used as a standard approach to benchmark the performance of the ML-based ETo estimations.

English

The overall objective of this present research is assessment of rice-water requirement in Cyabayaga wetland of Nyagatare District in Rwanda. Specific objectives of this research project were to collect and analyze weather parameters, of soil physical properties, crop-water requirements and to recommend efficient water management of the study area. Research design, data collection and field observations were made during July, 2019 to January, 2020. The experiment was carried out in Cyabayaga marshland and was especially based on the assessment of rice water requirement, weather parameters and hydraulic parameters like measurement of soil infiltration rate, soil hydraulic conductivity, flow discharge in irrigation channels, water loss, conveyance efficiency and application efficiency. Evapotranspiration and irrigation needs were determined by Cropwat 8.0 software. Conveyance efficiency (Ec) was found to be 70.05% and 30% of water was lost through seepage and evapotranspiration through weeds in unlined channels. The overall deep percolation, seepage and evapotranspiration losses were found to be 25 mm/day. Considering the highest potential crop evapotranspiration (ETc) of 4.8 mm, it was indicated that huge water loss of 20.2 mm/day was caused by seepage and percolation within the soil. The application efficiency (Ea) was found to be 23.8%. Hence, the overall efficiency was 16.7%. With the current rice yield of 4.25 t/ha, a low water-use-efficiency of 0.089 kg/m3 was found. &nbsp; Key words: Cropwater requirements, rice, irrigation requirements, water use efficiency and Cropwat.

Transpiration and Water Use of an Irrigated Traditional Olive Grove with Sap-Flow Observations and the FAO56 Dual Crop Coefficient Approach

The SIMDualKc model was applied to evaluate the crop water use and the crop coefficient (Kc) of an irrigated olive grove (Olea europaea L.) located in Sicily, Italy, using experimental data collected from two crop seasons. The model applies the FAO56 dual Kc approach to compute the actual crop evapotranspiration (ETc act) and its components, i.e., the actual tree transpiration (Tc act), obtained through the basal crop coefficient (Kcb), and soil evaporation according to an evaporation coefficient (Ke). Model calibration was performed by minimizing the difference between the predicted Tc act and the observed daily tree transpiration measured with sap flow instrumentation (TSF field) acquired in 2009. The validation was performed using the independent data set of sap flow measurements from 2011. The calibrated Kcb was equal to 0.30 for the initial and non-growing season stages, 0.42 for the mid-season, and 0.37 for the end season. For both seasons, the goodness-of-fit indicators relative to comparing TSF field with the simulated Tc act resulted in root mean square errors (RMSE) lower than 0.27 mm d−1 and a slope of the linear regression close to 1.0 (0.94 ≤ b0 ≤ 1.00). The olive grove water balance simulated with SIMDualKc produced a ratio between soil evaporation (Es) and ETc act that averaged 39%. The ratio between actual (ETc act) and potential crop evapotranspiration (ETc) varied from 84% to about 99% in the mid-season, indicating that the values of ETc act are close to ETc, i.e., the adopted deficit irrigation led to limited water stress. The results confirm the suitability of the SIMDualKc model to apply the FAO56 dual Kc approach to tree crops, thus assessing the water use of olives and supporting the development of appropriate irrigation management tools that are usable by farmers. A different way to estimate Kcb is based on the approach suggested in 2009 by Allen and Pereira (A&amp;P), which involves the measured fraction of ground covered (shaded) by the crop and the height of the trees. Its application to the studied grove produced the mid-season Kcb values ranging from 0.40–0.45 and end-season Kcb values ranging from 0.35–0.40. The comparison between the A&amp;P-computed Tc act A&amp;P and TSF field shows RMSE values ranging from 0.27 to 0.43 mm d−1, which demonstrates the adequacy of the latter approach for parameterizing water balance models and for irrigation scheduling decision making.

Modeling impacts of climate change on the water needs and growing cycle of crops in three Mediterranean basins

In this study, the suitability of major crops currently growing in three case study basins in Catalonia (NE Spain) was assessed for the first half of the 21st century. For this purpose, an estimation was made of net hydric needs (NHN) and a set of agroclimatic parameters. Climate change impacts were estimated at sub-basin level using temperature and precipitation temporal series based on the Third Report on Climate Change in Catalonia under the RCP4.5 scenario. Potential crop evapotranspiration (ETc, FAO procedure) and monthly water balance considering soil water holding capacity were used to estimate actual evapotranspiration (ETa) and NHN. Over the period studied, NHN would generally rise, with small (+ 0.1%) to high (+ 6.6%) increases in the 2020 s and moderate (+ 3.9%) to high (+ 6.7%) increases in the 2040 s. Dynamics would be different for the three basins and general trends vary from crop to crop. At all events, a generalized increase in NHN together with lower water availability could severely limit crop productivity in the case of both rainfed and irrigated crops (irrigation restrictions). Phenological changes could represent a greater constraint for crop productivity. Overall, the number of frost days will decrease (from −0.1 days in March to −8.7 days in April) in the three basins, while extremely hot days will increase (from + 0.3 days in July to + 3.8 days in August). Growth cycles will begin earlier (from −1 days to −12 days for crops with a base temperature of 10 °C), and for some crops they will be shorter (from −8 days to −27 days in the case of maize and up to −10 days in the case of vines). The impacts of climate change in the three basins could result in significant limitations for crops if adaptive strategies beyond irrigation and growing cycle issues are not applied. The results of this study could serve as a basis for the development of adaptation strategies to improve and maintain agriculture in the case study basins and in similar regions.

Modeling Cotton Growth and Yield Response to Irrigation Practices for Thermally Limited Growing Seasons in Kansas

HighlightsLater planting and greater site elevation or latitude decreased seasonal growing degree days and cotton yield in Kansas.Higher irrigation capacity (rate) usually increased lint yield, which was probably due to increased early boll load.Strategies for splitting land allocations between high irrigation rates and dryland did not increase production.Cotton may reduce irrigation withdrawals from the Ogallala aquifer, but the Kansas growing season limits production.Abstract. Precipitation in the western Great Plains averages about 450 mm, varying little with latitude and providing 40% to 80% of potential crop evapotranspiration (ETc). Supplemental irrigation is required to fully meet crop water demand, but the Ogallala or High Plains aquifer is essentially non-recharging south of Nebraska. Pumping water from this aquifer draws down water tables, leading to reduced water availability and deficit irrigation to produce an alternate crop such as cotton (Gossypium hirsutum L.) with a lower peak water demand than corn (Zea mays L.). Our objective was to compare simulated cotton yield response to emergence date, irrigation capacity, and application period at three western Kansas locations (Colby, Tribune, and Garden City) with varying seasonal energy or cumulative growing degree days (CGDD) and compare split center pivot deficit irrigation strategies with a fixed water supply (i.e., where portions of the center pivot land area are managed with different irrigation strategies). We used actual 1961-2000 location weather records with the GOSSYM simulation model to estimate yields of cotton planted into soil at 50% plant-available water for three emergence dates (DOY 145, 152, and 159) and all combinations of irrigation period (0, 4, 6, 8, and 10 weeks beginning at first square) and capacity (2.5, 3.75, and 5.0 mm d-1). Simulated lint yield and its ratio to ETc, or water use efficiency (WUE), consistently decreased with delayed planting (emergence) as location elevation or latitude increased due to effects on growing season CGDD. Depending on location, simulated cotton lint consistently increased (p = 0.05) for scenarios with increasing irrigation capacity, which promoted greater early season boll load, but not for durations exceeding 4 to 6 weeks, probably because later irrigation and fruiting did not complete maturation during the short growing season. Cotton WUE generally increased, with greater yields resulting from earlier emergence and early high-capacity irrigation. We calculated lower WUE where irrigation promoted vigorous growth with added fruiting forms that delayed maturation and reduced the fraction of open bolls. The irrigation strategy of focusing water at higher capacities on a portion of the center pivot in combination with the dryland balance did not increase net yields significantly at any location because the available seasonal energy limited potential crop growth and yield response to irrigation. However, the overall net lint yield was numerically larger for focused irrigation strategies at the southwest Kansas location (Garden City). Based on lint yields simulated under uniform or split center pivot deficit irrigation, we conclude that cotton is poorly suited as an alternative crop for central western and northwestern Kansas because of limited growing season CGDD. Keywords: Cotton, Crop simulation, Deficit irrigation, Evapotranspiration, Irrigation capacity, Split center pivot irrigation, Water use efficiency, Yield limiting factors.

OPTIMIZE IRRIGATION WATER USE OF GREEN PEAS UNDER DEFICIT IRRIGATION IN SEMI-ARID REGIONS

A study was carried out in the 2017 and 2018 cropping seasons, to determine the effect of deficit irrigation and stressed growing stages on the green pea yield and water use efficiency under semi-arid climatic conditions of Elbayda, Libya. Irrigation treatments included (IR100: 1 time potential crop evapotranspiration (ETc), IR90: 0.9 ETc, IR80: 0.8 IR70 and 0.7 ETc, ET4), and stressed-growing stages included vegetative (V), flowering (F), pods (P) and all stages (A). It is clear from the results that the water regime affected the growth and yield of the pea plants. Both the level of deficit and its timing during the plant life had an effect on the plant growth indicators and the final plant yield. In general the yield decreased as the deficit level increased but the water use efficiency increased with mild water deficit (IR90) then decreased as the deficit level increased. The drought stress during the flowering stage resulted in an increase in the final yield of the pea plants. The water regime to achieve the highest water use efficiency (WUE) while minimizing the water use is 80 % ETc during the flowering stage (IR80). The highest value of yield production would be at 90% ETc during the flowering stage (IR90F).