Irrigation Requirements Research Articles

Sensitivity analysis of estimated evapotranspiration using soil moisture content, energy balance model of SEBAL algorithm

Abstract Evapotranspiration is the main factor of plant growth, which can be estimated using different methods. Accurate estimation of evaporation and transpiration helps in planning to meet the actual needs of the plant. In general, daily evapotranspiration depends on temperature, climate, soil moisture, plant, growth period, root length, and the texture of the root zone porous media. In this study, the moisture balance of the soil surface is simulated daily and the components of deep percolation, evaporation, irrigation, and transpiration are calculated. The obtained results have been compared with the output of the surface energy balance algorithm for land (SEBAL) algorithm to determine the energy balance on the earth's surface and to estimate evapotranspiration. Normalized objective function (NOF), Nash–Sutcliffe efficiency index, and mean absolute error were incorporated into the problem for evaluating the predicted values. The energy balance of the soil surface and the moisture balance of the root zone each have a different process in estimating the actual evaporation and transpiration, but the correlation between them was acceptable. The interval between the calculated values can be used as input data to determine the irrigation requirement.

IoT-digital twin-inspired smart irrigation approach for optimal water utilization

Agriculture industry faces the challenge of increasing productivity by 50% from 2012 to 2050 while reducing water usage, given that it currently consumes 69% of the world’s freshwater. To achieve this goal, smart technologies such as Artificial Intelligence (AI), Digital Twins (DT), and Internet of Things (IoT) are being increasingly utilized. However, the use of DT in agriculture is still in its early stages. This study proposes a smart irrigation framework inspired by digital twins in an application domain. The irrigation framework’s sensors and actuators are linked to their virtual counterparts to create a digital twin. The IoT platform collects, aggregates, and processes data to determine daily irrigation requirements, and the behavior of the irrigation system is simulated. The proposed framework has two main advantages: evaluating the behavior of the digital twin and IoT platform in the context of agriculture before integrating them into the field and comparing various irrigation methods with current farming methods. By providing farmers with information about soil, weather, and crops, the system has the potential to improve farm operations and reduce water consumption.

Preparation of Land Cover and Land Use Maps for the Period 1976-2022 in Northern Kirkuk

A region representative of dry lands in northern Iraq in Kirkuk Governorate was chosen to prepare and map agricultural land uses based on remote sensing data in satellite images for a period of time from 1976 to 2022 and to calculate the volume of the area variation in the distribution of land cover Remote sensing software and applications were used by digital classification to distinguish. There are six spectral classes that represent, in fact, six classes represented by agricultural lands exploited for the cultivation of field crops (wheat and yellow corn), the class of barren lands and the class of pastures, as well as the class of mountainous regions and rocky outcrops, and the class of water and urban areas. Barren lands and poor use of agricultural lands Which occupies an area of 26.2 km2 of the region’s area is 350.81 square kilometers’, For 47 years, the region is still being exploited by cultivating very small areas which negatively affecting the increase in the volume of the desertification of the region and the deterioration of agricultural lands, and the region needs environmental re-planning by providing irrigation sources and requirements for successful land management to increase the area of agricultural lands that lead to achieving sustainable development that ensures the preservation of environmental resources natural.

Agronomic Analysis of the Replacement of Conventional Agricultural Water Supply by Desalinated Seawater as an Adaptive Strategy to Water Scarcity in South-Eastern Spain

Climate change is affecting water resources in south-eastern Spain, and this mainly affects irrigated agriculture. In this context, seawater desalination is an adaptive strategy that has provided increasing water allotments to agriculture for the last decade, to replace decreasing conventional resources. Farmers are concerned about the agronomic effects of this substitution and its economic consequences. This study focuses on the potential agronomic impacts of the progressive replacement of the irrigation water from the Tagus–Segura transfer (TST) with desalinated seawater (DSW) on the main crops of south-eastern Spain. To that end, five main agronomic concerns were selected and analyzed under three water supply scenarios using increasing rates of DSW (0, 50, and 100%). The results indicated that, in addition to other economic or environmental considerations, sufficiently relevant agronomic aspects exist that need to be considered when replacing the TST supply with DSW. This study evidences the risks of phytotoxicity and soil alkalinization, due to the increase in boron concentration and the imbalance between monovalent and divalent cations in the DSW, respectively, and also a slight increase in the cost of fertilizers. In addition, the irrigation water salinity effect on production and total irrigation requirements was negligible, as both water sources present sufficiently low salinity. The detrimental effects were mitigated under a partial replacement scenario, so the blended use of DSW with conventional resources seems the most recommendable option for its agricultural management, rather than irrigating with DSW alone.

Sustainable Water Resources Management under Climate Change: A Case Study with Potato Irrigation in an Insular Mediterranean Environment

Potato cultivation is a significant agricultural activity worldwide. As a staple food in many countries, potatoes provide essential nutrients and are a significant source of income for farmers. This paper investigates current and future net irrigation requirements for potatoes in combination with LCA (life cycle assessment) to assess the GHG emissions due to irrigation. Potato cultivation in Cyprus is used as a model for insular environments, which are often neglected from such studies. The models suggest that an increase in net irrigation requirements is expected but there is a large variability among locations and between years. The increase in rainfall that some of the models predict does not mean that this water will be effectively stored in the soil (and reduce irrigation requirements). The GHG emissions due to potato irrigation in Cyprus are estimated to be 1369.41 tons CO2eq and expected to decrease after 2030 by 35%, mainly due to changes in the electricity mix (from heavy fuel to renewable energy). Further research including other important (irrigated) crops in the island will support the development of strategies towards sustainable resources management under climate change.

Rhizospheric Addition of Hydrogel Polymer and Zeolite Plus Glutathione Mitigate the Hazard Effects of Water Deficiency On Common Bean Plants Through Enhancing the Defensive Antioxidants

AbstractCurrently, the world is facing many troubles in crop production and the irrigation water deficit is the most harmful among them. Saving irrigation water is the main target for all countries of the world, especially in arid areas. Field trial was executed aiming to assess the influence of irrigation regimes (100, 80 and 60% of irrigation requirements, IR (IR100, IR80 and IR60, respectively), water-absorbent substances (control, hydrogel polymer and zeolite as soil addition) and foliar application of glutathione (GSH) [with GSH or without] on the performance of common bean plants. Findings clarified that plant fresh weight obtained with the combination of IR80 × zeolite × GSH had no significant differences with the superior combination of IR100 × hydrogel polymer or zeolite × GSH. Hydrogel polymer plus GSH supply showed distinctive enhancements for N, P and K accumulation in common bean leaves. Application of zeolite plus GSH reduced the accumulation of POX and CAT by 21.8 and 15.5% under IR80 and 16.1 and 7.6% under IR60, respectively. Spraying of GSH × hydrogel polymer under IR100, IR80 and IR60 significantly increased the value of pods yield by 26.5%, 25.23% and 32.80, respectively, as compared to corresponding control treatment. the interaction of IR100 and hydrogel polymer whether with or without GSH showed the highest significant values of N, P, protein, fiber, carbohydrates percentages and TDS. Briefly, it can be concluded that water holding amendments i.e., polymer and zeolite as well as low-molecular-weight antioxidants i.e., glutathione can mitigate the hazard impacts of elevated reactive oxygen species production under drought. Practically, common bean growers are advised to treat the soil with available soil amendment (hydrogel polymer, 0.24 t ha−1 or zeolite, 1.20 t ha−1) and spraying common bean plants by glutathione, 1.0 mM to sustain the crop productivity and quality under shortage water conditions

Assessment of Net Irrigation Requirements for Spring Wheat and Spring Maize in Shiyanghe Watershed, Northwestern China

Assessment of Net Irrigation Requirements for Spring Wheat and Spring Maize in Shiyanghe Watershed, Northwestern China

UAV SWARM SYSTEMS

The rapid development of technology has introduced new and groundbreaking approaches to unmanned aerial vehicles (UAVs). One such innovation is the swarm UAV systems. These systems represent a technology where multiple small and often autonomous aircraft collaborate to perform specific tasks. An important component of this system is artificial intelligence. Swarm UAV systems can operate more effectively and coordinatedly by utilising artificial intelligence algorithms. Each UAV is equipped with autonomous control systems and artificial intelligence software. Through data analysis, object recognition, route planning, environmental adaptation and other complex tasks, artificial intelligence aids these UAVs in successfully completing tasks. As a result, swarm UAVs are capable of performing more complex and dynamic missions. The integration of artificial intelligence into swarm UAV systems holds great potential for several application fields. For instance, in search and rescue operations, AI can perform image analysis to detect individuals trapped under debris. In agriculture applications, AI can detect plant diseases or pests and optimize irrigation requirements. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. Additionally, we will focus on the potential future application areas of swarm UAV systems and the potential impacts of this integration. Swarm UAV systems and artificial intelligence represent a significant transformation in the world of technology, and this article will provide a resource to better understand these exciting developments. Keywords: Research of UAVs, integration of artificial intelligence, flight programs, autonomous control.

Exploring the Applicability of Regression Models and Artificial Neural Networks for Calculating Reference Evapotranspiration in Arid Regions

Reference evapotranspiration (ET0) is critical in agriculture and irrigation water management, particularly in arid and semi-arid regions. Our study aimed to develop an accurate and efficient model for estimating ET0 using various climatic variables as predictors. This research evaluated two model techniques, i.e., stepwise regression and artificial neural networks (ANNs), to identify the most effective model for calculating ET0. The two models were developed and tested based on climate data obtained from the whole climatic station of Egypt. The CLIMWAT 2.0 program was used to acquire the climate data for Egypt from a total of 32 stations. This software is a dedicated meteorological database created specifically to work with the CROPWAT computer program. The models were developed using average climate data spanning 29 years, from 1991 to 2020. The obtained data were utilized to compute reference evapotranspiration using CROPWAT 8, based on the Penman–Monteith equation. The results showed that the ANN model demonstrated superior performance in ET0 calculations compared to other methods, achieving a coefficient of determination (R2) of 0.99 and a mean absolute percentage error (MAPE) of 2.7%. In contrast, the stepwise model regression yielded an R2 of 0.95 and an MAPE of 8.06. On the other hand, the most influential climatic variables were maximum temperature, humidity, solar radiation, and wind speed. The findings of this study could be applied in various fields, such as agriculture, irrigation, and crop water requirements, to optimize crop growth under limited water resources and global environmental changes. Furthermore, our study identifies the limitations and challenges of applying these models in arid regions, such as data availability constraints and model complexity. We discuss the need for more extensive and reliable datasets and suggest future research directions, including ensemble modeling, remote sensing data integration, and evaluating climate change’s impact on ET0 estimation. Overall, this study contributes to the understanding of ET0 estimation in arid regions and provides valuable insights into the applicability of regression models and ANNs. The superior performance of ANNs offers potential advancements in water resource management and agricultural planning, enabling more accurate and informed decision-making processes.

Optimizing evapotranspiration and crop irrigation requirements of tropical forages cropping systems in Southern Brazil.

Crop irrigation requirements are usually estimated based on crop evapotranspiration (ETc) as determined by the reference evapotranspiration (ETo) and crop coefficient (Kc). There is a lack of knowledge on the irrigation requirements of tropical forage crops in Brazil, contrasting with the increasing use of irrigation in pastures. The effort of this study was to investigate what would be the water needs of tropical forages in Southern Brazil, based on a robust experimental database. The study was carried out in São Paulo State-Brazil using different forages species and their combinations [Guinea grass (GG); Guinea grass + black oat + ryegrass (GOR); Bermuda grass (BG), and Bermuda + black oat + ryegrass (BOR)]. The experimental fields were fully irrigated, and the Kc values were derived from ETc measurements on lysimeters; ETo was estimated using daily data from a nearby weather station and the standard FAO56 parameterization. Mean daily ETc values for GG, GOR, BG and BOR were 4.1, 2.9, 3.6, and 3.4mm, respectively, and respective mean Kc values were 0.99, 0.90, 1.0, and 0.94. Average Kc values for all plots decreased as ETo increased, producing a negative Kc-ETo relationship, mainly when ETo reached values greater than 5mm d-1. This was most likely due to internal plant stomatal resistance to vapor release from the leaves diffusing to the atmosphere at high ETo. So, the time-based Kc curves described by FAO 56 manual should be adjusted for the analyzed crops considering different ranges of ETo to improve the required irrigation depth.

Effects of conservation practices on rainfed maize yield, furrow water infiltration, and soil moisture for surface sealing loam soils in the Yazoo‐Mississippi Delta

AbstractDecades of intense tillage have created the problem of surface sealing in loam soils across the Mid‐South United States and beyond. These soils exhibit low organic matter, poor aggregate stability, and ultimately low infiltration rates that decrease rainfed crop productivity and increase irrigation water demand. This research quantified the effects of soil management practices on rainfed maize (Zea mays L.) yield, furrow water infiltration, and soil moisture in Commerce very fine sandy loam and silt loam near Stoneville, MS. The six treatments included conventional tillage (CT), cereal rye (CR; Secale cereale L.), furrow diking (FD), no‐till (NT), polyacrylamide (PAM), and subsoiling (SS). No other treatment achieved a significantly higher maize grain yield than the CT control treatment in both years consistently. Single‐ring infiltrometer results indicated no significant differences among CT, CR, PAM, and SS in 2022 and between CT and NT in 2021. Neutron attenuation found that total soil water within the top 1 m was not significantly different among CT, FD, and SS on any of the eight measurement dates throughout the 2022 maize reproductive period. However, soil moisture was numerically lower for FD in the late reproductive stages. These findings suggest that environmental factors and other agronomic operations may counteract conservation practices, limiting their effectiveness at reducing irrigation requirements to mitigate groundwater depletion in the Mississippi River Valley Alluvial Aquifer.

Induction of Tolerance in Groundnut Plants Against Drought Stress and Cercospora Leaf Spot Disease with Exogenous Application of Arginine and Sodium Nitroprusside Under Field Conditions

Naturally, under field conditions, plants are regularly experienced by a mixture of two or more stress factors. Drought is a major abiotic stress, and fungal pathogens characterize a main biotic stress challenge faced by plants and impact negatively on plant development and productivity. We propose that foliar application of nitric oxide (NO) donors can have positive effects on the induction of tolerance to biotic and abiotic stress on groundnut plants. This investigation was carried out to study the changes in growth, some biochemical aspects, and yield and quality of groundnut plants as well as induction of resistance to Cercospora leaf spot disease in response to nitric oxide (NO) donors, sodium nitroprusside (SNP), and arginine (Arg) (2.5, 5.0, and 7.5 mM) under two water irrigation levels 100% and 75% of water irrigation requirements (WIR), in two field experiments through two successive growing seasons of 2021 and 2022. Decreasing irrigation water significantly reduced shoot length, branches numberplant-1, shoot fresh and dry weight, photosynthetic pigments components, endogenous indole acetic acid (IAA) contents, and yield components. Furthermore, root fresh and dry weight, phenols, total soluble sugars (TSS), proline contents, and the accumulation of hydrogen peroxide (H2O2) and lipid peroxidation of groundnut leaves increased significantly. Contrarily, foliar application with Arg and SNP alleviated the negative influences of drought on growth and productivity of groundnut plants via enhancing photosynthetic pigments, IAA, phenolic compounds, TSS, and proline contents. Additionally, SNP and Arg significantly decreased oxidative damage through decreasing H2O2 and lipid peroxidation by the induction of antioxidant enzymes. Remarkably, the increase of drought level led to a reduction in Cercospora leaf spot (CLS) disease with the use of high concentrations of both Arg and SNP. Interestingly, in both stressed and unstressed plants, SNP treatment at 7.5 mM was the most effective in reducing the incidence and severity of disease, while Arg at 2.5 mM recorded the lowest reduction compared to other treatments. In conclusion, foliar treatment of either SNP or Arg is a profound effect on modulating the drought stress and induction of resistance to Cercospora leaf spot disease of groundnut plants throughout regulating physiological and biochemical processes associated with photosynthesis and oxidative responses.

Prediction of Evapotranspiration in the Pampean Plain from CERES Satellite Products and Machine Learning Techniques

A key aspect in agricultural zones, such as the Pampean Plain of Argentina, is to accurately estimate evapotranspiration rates to optimize crops and irrigation requirements and the floods and droughts prediction. In this sense, we evaluate six machine learning approaches to estimate the reference and actual evapotranspiration (ET0 and ETa) through CERES satellite products data. The results obtained applying machine learning techniques were compared with values obtained from ground-based information. After training and validating the algorithms, we observed that Support Vector machine-based Regressor (SVR) showed the best accuracy. Then, with an independent dataset, the calibrated SVR were tested. For predicting the reference evapotranspiration, we observed statistical errors of MAE = 0.437 mm d−1, and RMSE = 0.616 mm d−1, with a determination coefficient, R2, of 0.893. Regarding actual evapotranspiration modelling, we observed statistical errors of MAE = 0.422 mm d−1, and RMSE =0.599 mm d−1, with a R2 of 0.614. Comparing the results obtained with the machine learning models developed another studies in the same field, we understand that the results are promising and represent a baseline for future studies. Combining CERES data with information from other sources may generate more specific evapotranspiration products, considering the different land covers.

Prediction of Evapotranspiration in the Pampean Plain from CERES Satellite Products and Machine Learning Techniques

A key aspect in agricultural zones, such as the Pampean Plain of Argentina, is to accurately estimate evapotranspiration rates to optimize crops and irrigation requirements and the floods and droughts prediction. In this sense, we evaluate six machine learning approaches to estimate the reference and actual evapotranspiration (ET0 and ETa) through CERES satellite products data. The results obtained applying machine learning techniques were compared with values obtained from ground-based information. After training and validating the algorithms, we observed that Support Vector machine-based Regressor (SVR) showed the best accuracy. Then, with an independent dataset, the calibrated SVR were tested. For predicting the reference evapotranspiration, we observed statistical errors of MAE = 0.437 mm d−1, and RMSE = 0.616 mm d−1, with a determination coefficient, R2, of 0.893. Regarding actual evapotranspiration modelling, we observed statistical errors of MAE = 0.422 mm d−1, and RMSE =0.599 mm d−1, with a R2 of 0.614. Comparing the results obtained with the machine learning models developed another studies in the same field, we understand that the results are promising and represent a baseline for future studies. Combining CERES data with information from other sources may generate more specific evapotranspiration products, considering the different land covers.

EFFECTIVENESS EVALUATION OF THE WATER USE PLAN IN THE BA RIVER BASIN

The Ba River basin has abundant water resources for agriculture and power generation. To respond to the socio-economic development of the basin, the water work system has been planning to improve the efficiency of water resource allocation, serving sustainable development. This study was designed to evaluate the effectiveness of water work plans built on the Ba River basin by assessing their ability to supply water for agriculture, domestic, industries and services. Water balance calculations show that water demand in irrigation areas in the Ba River basin increases significantly corresponding to basin development, especially in the Krong Pa and Nam Bac An Khe regions. With the application of irrigation planning, agricultural development plans and changes in land use structure, water resource allocation in the Ba River basin has been improved in the future. The effectiveness of the planning is obviously shown in the Upper Ayun, Ayun Pa, Upper and Lower Dong Cam where water demand increases sharply and water shortages are controlled, ensuring water supply requirements for irrigation and other sectors of over 85% and over 90%, respectively.

Robustness of climate indices relevant for agriculture in Africa deduced from GCMs and RCMs against reanalysis and gridded observations

This study assesses the ability of climate models to represent rainy season (RS) dependent climate indices relevant for agriculture and crop-specific agricultural indices in eleven African subregions. For this, we analyze model ensembles build from Regional Climate Models (RCMs) from CORDEX-CORE (RCM_hist) and their respective driving General Circulation Models (GCMs) from CMIP5 (GCM_hist). Those are compared with gridded reference data including reanalyses at high spatio-temporal resolution (≤ 0.25°, daily) over the climatological period 1981–2010. Furthermore, the ensemble of RCM-evaluation runs forced by ERA-Interim (RCM_eval) is considered. Beside precipitation indices like the precipitation sum or number of rainy days annually and during the RS, we examine three agricultural indices (crop water need (CWN), irrigation requirement, water availability), depending on the RS’ onset. The agricultural-relevant indices as simulated by climate models, including CORDEX-CORE, are assessed for the first time over several African subregions. All model ensembles simulate the general precipitation characteristics well. However, their performance strongly depends on the subregion. We show that the models can represent the RS in subregions with one RS adequately yet struggle in reproducing characteristics of two RSs. Precipitation indices based on the RS also show variable errors among the models and subregions. The representation of CWN is affected by the model family (GCM, RCM) and the forcing data (GCM, ERA-Interim). Nevertheless, the too coarse resolution of the GCMs hinders the representation of such specific indices as they are not able to consider land surface features and related processes of smaller scale. Additionally, the daily scale and the usage of complex variables (e.g., surface latent heat flux for CWN) and related preconditions (e.g., RS-onset and its spatial representation) add uncertainty to the index calculation. Mostly, the RCMs show a higher skill in representing the indices and add value to their forcing models.

Trend assessment of changing climate patterns over the major agro-climatic zones of Sindh and Punjab

The agriculture sector, due to its significant dependence on climate patterns and water availability, is highly vulnerable to changing climate patterns. Pakistan is an agrarian economy with 30% of its land area under cultivation and 93% of its water resources being utilized for agricultural production. Therefore, the changing climate patterns may adversely affect the agriculture and water resources of the country. This study was conducted to assess the climate variations over the major agro-climatic zones of Sindh and Punjab, which serve as an important hub for the production of major food and cash crops in Pakistan. For this purpose, the climate data of 21 stations were analyzed using the Mann–Kendall test and Sen's slope estimator method for the period 1990–2022. The results obtained from the analysis revealed that, in Sindh, the mean annual temperature rose by ~0.1 to 1.4°C, with ~0.1 to 1.2°C in cotton-wheat Sindh and 0.8 to 1.4°C in rice-other Sindh during the study period. Similarly, in Punjab, the mean annual temperature increased by ~0.1 to 1.0°C, with 0.6 to 0.9°C in cotton-wheat Punjab and 0.2 to 0.6°C in rainfed Punjab. Seasonally, warming was found to be highest during the spring season. The precipitation analysis showed a rising annual precipitation trend in Sindh (+30 to +60 mm) and Punjab (+100 to 300 mm), while the monsoon precipitation increased by ~50 to 200 mm. For winter precipitation, an upward trend was found in mixed Punjab, while the remaining stations showed a declining pattern. Conclusively, the warming temperatures as found in the analysis may result in increased irrigation requirements, soil moisture desiccation, and wilting of crops, ultimately leading to low crop yield and threatening the livelihoods of local farmers. On the other hand, the increasing precipitation may favor national agriculture in terms of less freshwater withdrawals. However, it may also result in increased rainfall-induced floods inundating the crop fields and causing water logging and soil salinization. The study outcomes comprehensively highlighted the prevailing climate trends over the important agro-climatic zones of Pakistan, which may aid in devising an effective climate change adaptation and mitigation strategy to ensure the state of water and food security in the country.

Using Film-Mulched Drip Irrigation to Improve the Irrigation Water Productivity of Cotton in the Tarim River Basin, Central Asia

Climate change has significantly influenced water resource patterns in arid regions. Applying effective water-saving measures to improve irrigation efficiency and evaluate their future water-saving capabilities is crucial for ensuring the sustainable development of irrigation agriculture. Based on the daily meteorological data from 15 global climate models (GCMs) in the sixth phase of the Coupled Model Intercomparison Project (CMIP6), this study used the AquaCrop model to perform high-resolution (0.1° × 0.1°) grid simulations of cotton yields and irrigation requirements. The study also investigated the ability of film-mulched drip irrigation (FMDI) to improve future irrigation efficiency under two shared socio-economic pathways (SSP245 and SSP585) in the Tarim River Basin (TRB), Central Asia, from 2025 to 2100. The results showed that the cotton yield and irrigation water productivity (WPI) in the TRB exhibited an upward trend of 13.82 kg/ha/decade (80.68 kg/ha/decade) and 0.015 kg/m3/decade (0.068 kg/m3/decade), respectively, during the study period. The cotton yield and WPI were higher in the northern, northwestern plains, and northeastern intermountain basin areas, where they reach over 4000 kg/ha and 0.8 kg/m3/decade. However, the cotton yield and WPI were lower in the southwestern part of the study area. Therefore, large-scale cotton production was not recommended there. Furthermore, compared to flood irrigation, the use of FMDI can, on average, improve the WPI by approx. 25% and reduce irrigation water requirements by more than 550 m3/ha. Therefore, using FMDI can save a substantial amount of irrigation water in cotton production, which is beneficial for improving irrigation efficiency and ensuring the future stable production of cotton in the TRB. The research results provide a scientific reference for the efficient utilization and management of water resources for cotton production in the TRB and in similar arid regions elsewhere in the world.

Water and Irrigation Requirements of Glycine max (L.) Merr. in 1981–2020 in Central Poland, Central Europe

Soya, one of the most economically important crops, is sensitive to periodic water deficits, which, because of climate change, are becoming more and more common in central Europe. The goal of this study was to estimate the water requirements of soybean from 1981 to 2020 in four provinces of central Poland and the rainfall deficits affecting soybean cultivation; the study also evaluates rainfall water use efficiency for soybean cultivated in production fields to investigate the future necessity of supplemental irrigation. Calculations were based on the values of monthly air temperature and the sum of precipitation. Soybean water requirements were calculated using the method of crop coefficients and reference evapotranspiration was estimated using the Blaney–Criddle approach. Crop water requirements were defined as potential evapotranspiration. Precipitation deficits for soybean were estimated using the Ostromęcki method. Water use efficiency was calculated based on rainfall totals and soybean yield in the rainfed fields. It was found that, on average in the central Poland provinces, from 1981 to 2020, the water requirements of soybean in the growing season amounted to 384 mm, and the highest water requirements occurred in June and July. In the studied forty-year period, a significant upward trend in soybean water requirements in central Poland was observed, both from 1 May to 31 August and from 1 June to 31 August. Rainfall deficits in soybean cultivation in central Poland were found from May to August and amounted to 123 mm in normal years (N50%). The rainfall water use efficiency from April to August for soybean cultivated in central Poland on average amounted to 6.6 kg ha−1 mm−1 and varied in individual years and regions. The results of the study indicate the need to develop supplemental irrigation systems for soybean crops cultivated in central Poland and other areas of the world with similar climate conditions to optimize yield and the sustainable use of water resources.

Construction of an intelligent agricultural irrigation control platform based on PLC technology

Abstract This paper firstly constructs a PLC-based intelligent irrigation control system, analyzes the requirements of its system functions, and determines three major modules of the system: data acquisition module, intelligent irrigation module, and Web service module. Then the system fuzzy control process is determined. According to the moisture value collected by the soil moisture sensor, the average is taken, and the deviation and deviation change rate is obtained by calculating the difference with the set value, and the control value is obtained by querying the previously deposited fuzzy control table through a series of transformations, and the command is executed according to the program setting. The automatic irrigation requirement is achieved by controlling the solenoid valve on and off. Finally, the analysis of system functions in terms of sensor accuracy, system function testing, and temperature and air humidity prediction accuracy was carried out. Regarding sensor accuracy, the error of relative soil moisture content is within 3%, and the relative error of soil temperature is within 0.5. In the system control function test, the solenoid valve will open for irrigation when the soil relative water content is lower than 40% or higher than 70% of the warning range, and the lighting will be turned on when the light intensity is lower than 200 Lux, and the system control is in line with the expectation. The intelligent irrigation system is of great significance to agricultural development.