Crop Water Requirements Research Articles

Monitoring soil elements for irrigation management using Internet of Things (IoT) sensors

AbstractSmart farming focuses on three crucial areas: soil quality, weather conditions, and crop health. Because of the significant impact of weather and irrigation factors, many smart farming solutions are tightly integrated with smart environment and smart water (pollution, turbidity, nutrients) systems for a holistic approach. The Internet of Things (IoT) is revolutionizing soil monitoring, giving farmers and growers the means to optimize yields, minimize disease risks, and rationalize resource use. Thanks to IoT sensors, it is possible to measure various parameters such as soil temperature, NPK (nitrogen, phosphorus, potassium) levels, and soil moisture. The information collected by these IoT sensors is then transmitted to a central point or stored in the cloud server for in‐depth analysis, visual representation, and trend identification. The application of the IoT in agriculture is widely referred to as smart farming. The IoT is an essential pillar of precision agriculture, enabling more targeted and efficient management of resources. Smart sensor systems provide more information about water and crop water requirements. This information can be used to mechanically manage the water supply system and help farmers optimize their irrigation system. The information acquired by the first sensors is transferred to the cloud server. This article proposes and evaluates the concept of remote sensing systems. Three sites were selected to test the IoT system. At each site, five sensors were employed, specifically designed for soil and irrigation water analysis. The sensors dedicated to soil pH, moisture, and NPK demonstrated commendable accuracy; equally, the irrigation water pH and turbidity sensors exhibited notably precise measurements. The results were noteworthy, with an R2 value surpassing 90% across all sensor measurements. These IoT sensors could facilitate effective crop monitoring without excessive expenditures and serve to safeguard groundwater from nutrient contamination.

Predicting yield of crop type and water requirement for a given plot of land using machine learning techniques

Internet of things (IoT) smart technology enables new digital agriculture. Technology has become necessary to address today's challenges, and many sectors are automating their processes with the newest technologies. By maximizing fertiliser use to boost plant efficiency, smart agriculture, which is based on IoT technology, intends to assist producers and farmers in reducing waste while improving output. With IoT-based smart farming, farmers may better manage their animals, develop crops, save costs, and conserve resources. Climate monitoring, drought detection, agriculture and production, pollution distribution, and many more applications rely on the weather forecast. The accuracy of the forecast is determined by prior weather conditions across broad areas and over long periods. Machine learning algorithms can help us to build a model with proper accuracy. As a result, increasing the output on the limited acreage is important. IoT smart farming is a high-tech method that allows people to cultivate crops cleanly and sustainably. In agriculture, it is the use of current information and communication technologies.

Crop Water Requirement Prediction Method Based on EEMD-Attention-LSTM Model

Crop water demand prediction is an important part of Precision agriculture. Due to the nonlinear relationship between input variables (weather data, soil moisture, and crop type) and output variables (crop water demand), it is difficult to accurately predict crop water demand. This article proposes a method for predicting crop water demand based on the EEMD Attention LSTM model. The model combines the ensemble empirical mode decomposition (EEMD), attention mechanism (Attention), and Long short-term memory (LSTM) neural networks to capture the changes in different scales of input variables. It selectively focuses on the relevant parts of the input sequence when making predictions. The performance of the EEMD attention LSTM model was evaluated on the historical crop water demand data set and compared with other prediction models such as RNN, LSTM, and EMD-LSTM. It shows that the model is superior to other methods in terms of Mean squared error and mean absolute error. When the relationship between input variables and output variables is nonlinear, the EEMD-attention-LSTM model is a powerful tool for crop water demand prediction.

Estimation of water consumption and productivity for wheat using remote sensing and SEBAL model: A case study from central clay plain Ecosystem in Sudan

Abstract Remote sensing (RS) can efficiently support the quantification of crop water requirements and water productivity (WP) for evaluating the performance of agricultural production systems and provides relevant feedback for management. This research aimed to estimate winter wheat water consumption and WP in the central clay plain of Sudan by integrating remotely sensed images, climate data, and biophysical modelling. The wheat crop was cultivated under a centre-pivot irrigation system during the winter season of 2014/2015. The Landsat-8 satellite data were used to retrieve the required spectral data. The Surface Energy Balance Algorithm for Land (SEBAL) was supported with RS and climate data for estimating the Actual Evapotranspiration (ETa) and the WP for the wheat crop. The SEBAL outputs were validated using the FAO Penman–Monteith method coupled with field measurements and observation. The results showed that the seasonal ETa ranged from 400 to 600 mm. However, the WP was between 1.2 and 1.5 kg/m3 during the wheat cycle. The spatial ETa and WP maps produced by the SEBAL model and Landsat-8 images can improve water use efficiency at field scale environment and estimate the water balance over large agricultural areas.

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.

Evaluation of transpiration in different almond production systems with two-source energy balance models from UAV thermal and multispectral imagery

AbstractA growing number of intensive irrigated production systems of the almond crop have been established in recent years. However, there is little information regarding the crop water requirements. Remote sensing-based models such as the two-source energy balance (TSEB) have proven to be reliable ways to accurately estimate actual crop evapotranspiration. However, few efforts have been made to validate the transpiration with sap flow measurements in woody row crops with different production systems and water status. In this study, the TSEB Priestley-Taylor (TSEB-PT) and contextual approach (TSEB-2T) models were assessed to estimate canopy transpiration. In addition, the effect of applying a basic clumping index for heterogeneous randomly placed clumped canopies and a rectangular hedgerow clumping index on the TSEB transpiration estimation was assessed. The TSEB inputs were obtained from high resolution multispectral and thermal imagery using an unmanned aerial vehicle. The leaf area index (LAI), stem water potential (Ψstem) and fractional intercepted photosynthetically active radiation (fIPAR) were also measured. Significant differences were observed in transpiration between production systems and irrigation treatments. The combined use of the TSEB-2T with the C&N-R transmittance model gave the best transpiration estimations for all production systems and irrigation treatments. The use of in situ PAR transmittance in the TSEB-2T model significantly improved the root mean squared error. Thus, the better agreement observed with the TSEB when using the C&N-R model and in situ PAR transmittance highlights the importance of improving radiative transfer models for shortwave canopy transmittance, especially in woody row crops.

The effects of deficit irrigation on water use efficiency, yield and quality of drip-irrigated tomatoes grown under field conditions in Zimbabwe

Water availability in the root zone directly affects the yield and quality of tomatoes yet in most cases in sub-Saharan Africa water is either expensive or scarce. It is therefore important to establish and utilise suitable irrigation strategies in order to produce the crop in a sustainable way. In this study the effect of conventional and deficit irrigation treatments on yield, fruit quality and water use efficiency (WUE) were determined. Four trials were conducted at the University of Zimbabwe Farm from 2014-2017 with four treatments per trial: T1 = 100%, T2 = 80%, T3 = 60%, and T4 = 50% of crop water requirements (ETc). Treatments had equal number of plants per trial with an in-row plant spacing of 0.3 m and 0.5 m between adjacent rows. ETc was determined daily for each treatment and the corresponding volume applied through one drip emitter per plant. Fruits from each treatment were gathered while ripening and the total yield obtained. WUE was calculated by dividing the total fresh yield by total irrigation water applied. Maximum yield was obtained where 100% ETc was applied, with no significant difference between yield of plants at 80% and 60% ETc, except in 2016. Yield decreased with 50% ETc in 2014, 2015, and 2017 with no significant difference in yield between 60% and 50% ETc treatments in 2016. The 2015 season recorded the highest yield when compared to other trials showing that we can save 40% of water resulting in high WUE with minimum loss in yield. Deficit irrigation reduced fruit water but increased fruit soluble solids (°brix), vitamin C and fruit acid concentrations. Firmness was best when 60% ETc was applied. These results show that deficit irrigation is feasible for crop water management options for the production of high-quality field-grown tomatoes without major yield reductions.

Effects of different mulch materials on the photosynthetic characteristics, yield, and soil water use efficiency of wheat in Loess tableland

Due to the lack of precipitation and poor temporal and spatial stability in the Loess Plateau for a long time, it is necessary to adopt mulching technology to support the stable and high yield of wheat cultivation system. This study aims at exploring different mulching materials on the soil water content, photosynthetic characteristics, wheat yield, and yield components of winter wheat in the gully region of the Loess Plateau. The results showed that the traditional flat soil water content is the lowest in the seedling stage. In the jointing stage and heading stage of many crop water requirements, ridge film mulching treatment can effectively promote the growth of wheat and increase the water use efficiency. The leaf area index (LAI) of different treatments wheat showed a trend of increasing first and then decreasing. In the jointing stage, ordinary mulching film (T1) and liquid mulching film (T3) had the highest LAI content, which were 3.78 and 3.71 respectively. The Pn and Gs in wheat flag leaves of T3 treatment is higher than that of CK throughout the entire growth period, and T3 significantly increased Ci and WUEi in different growth stage. And the grain number per panicle and grain weight of T3 treatment were 24.44 and 41.00 g, which were 19.3% and 5.4% higher than CK, respectively. Through the actual production calculation of the final harvest, the ridge film mulching has a significant increase in production compared with the CK. The yield of T3 treatment was 4980.25 kg hm−2, which was 29.37% higher than CK. It was significantly different from CK (P < 0.05). Based on the comprehensive analysis, the ridge film mulching treatment significantly affected the soil water content and wheat yield. And the liquid mulching film had the best effect. Exploring the impact of different covering techniques on the wheat cultivation system in the Loess Plateau region, to promote the scientific promotion of this technology.

Mapping crop evapotranspiration with high-resolution imagery and meteorological data: insights into sustainable agriculture in Prince Edward Island

Soil moisture variability caused by soil erosion, weather extremes, and spatial variations in soil health is a limiting factor for crop growth and productivity. Crop evapotranspiration (ET) is significant for irrigation water management systems. The variability in crop water requirements at various growth stages is a common concern at a global level. In Canada’s Prince Edward Island (PEI), where agriculture is particularly prominent, this concern is predominantly evident. The island’s most prominent business, agriculture, finds it challenging to predict agricultural water needs due to shifting climate extremes, weather patterns, and precipitation patterns. Thus, accurate estimations for irrigation water requirements are essential for water conservation and precision farming. This work used a satellite-based normalized difference vegetation index (NDVI) technique to simulate the crop coefficient (Kc) and crop evapotranspiration (ETc) for field-scale potato cultivation at various crop growth stages for the growing seasons of 2021 and 2022. The standard FAO Penman–Monteith equation was used to estimate the reference evapotranspiration (ETr) using weather data from the nearest weather stations. The findings showed a statistically significant (p &amp;lt; 0.05) positive association between NDVI and tabulated Kc values extracted from all three satellites (Landsat 8, Sentinel-2A, and Planet) for the 2021 season. However, the correlation weakened in the subsequent year, particularly for Sentinel-2A and Planet data, while the association with Landsat 8 data became statistically insignificant (p &amp;gt; 0.05). Sentinel-2A outperformed Landsat 8 and Planet overall. The Kc values peaked at the halfway stage, fell before the maturity period, and were at their lowest at the start of the season. A similar pattern was observed for ETc (mm/day), which peaked at midseason and decreased with each developmental stage of the potato crop. Similar trends were observed for ETc (mm/day), which peaked at the mid-stage with mean values of 4.0 (2021) and 3.7 (2022), was the lowest in the initial phase with mean values of 1.8 (2021) and 1.5 (2022), and grew with each developmental stage of the potato crop. The study’s ET maps show how agricultural water use varies throughout a growing season. Farmers in Prince Edward Island may find the applied technique helpful in creating sustainable growth plans at different phases of crop development. Integrating high-resolution imagery with soil health, yield mapping, and crop growth parameters can help develop a decision support system to tailor sustainable management practices to improve profit margins, crop yield, and quality.

Simulation model to analyze the spatial distribution of solar radiation in agrivoltaic Mediterranean greenhouses and its effect on crop water needs

Agrivoltaics is a new paradigm that combines agricultural and renewable energy production, making agriculture more sustainable, profitable, and resilient. Applying the agrivoltaics approach to greenhouse production presents both a challenge and an opportunity. The opportunity lies in the use of greenhouse rooftops to install PV panels to provide required shading in hot summer months in a more efficient and profitable way. The challenge relates to the fact that the intensity and uniformity of the shading may affect crop development and yield.To address this dilemma, the present study proposes a new model to simulate the distribution and uniformity of the radiation inside agrivoltaic greenhouses with PV panels installed on their rooftops. The proposed model can analyze any plane geometry for greenhouses as well as any PV panel layout and surface covering percentage. It can also generate reference evapotranspiration (ETo) maps to analyze the effect of radiation on crop water requirements and support farmers to manage irrigation.The results of the model not only provide the average reduction of radiation as a function of the fraction of greenhouse covered by PV panels, but also the uniformity of the radiation distribution, which is a key factor when designing an agrivoltaic greenhouse. The proposed model was validated by applying it to a case study in a real experimental greenhouse that had been utilized in a previous research work.

Assessing the impact of future climate scenarios on crop water requirements and agricultural water supply across different climatic zones of Pakistan

This study investigates the potential impacts of future climate change on crop water requirements (CWR) in different climatic zones of Pakistan and the subsequent implications for agricultural water demand and supply. Using the latest CMIP6 climate projections, we focused on Rabi and Kharif crop seasons, which are crucial for the growth of major crops in Pakistan. An empirical climate-crop and a hydrological model based on the Budyko theory were modified and forced to project future CWR changes and the potential widening of the water demand-supply gap until 2,100. Our results indicate a significant rise in mean annual CWR across all zones and emissions scenarios, with increasing rates at 2.30–2.57 mm/yr under SSP585 and 1.0–1.26 mm/yr under SSP245. Both Rabi and Kharif seasons show rising CWR, notably more under SSP585 (Kharif: 8%–14%, Rabi: 12%–15%) than SSP245 (Kharif: 4%–7%, Rabi: 6%–8%). The demand-supply gap is expected to grow notably, with arid and semi-arid zones being the most affected. Compared to 2015–2025, by 2091–2,100, the gap increased by 7%–15% (SSP245) and 15%–28% (SSP585) for Kharif and 7%–13% (SSP245) and 13%–32% (SSP585) for Rabi. To address these challenges, we recommend strategies like enhancing irrigation efficiency, adjusting crop patterns, and developing heat-resilient crops. Our insights aim to inform policy decisions on agriculture and water management in Pakistan under future climate change.

Crop Water Requirement Estimation by Using Cropwat Model: A Case Study of Abrajit Earthen Dam Command Area, East Gojjam, Ethiopia

Crop Water Requirement Estimation by Using Cropwat Model: A Case Study of Abrajit Earthen Dam Command Area, East Gojjam, Ethiopia

An Adaptive Framework for Regenerative Agriculture based on Environmental Management

Introduction The scope of implementing innovative research findings aimed at sustainable development goals seems to be inherently limited by economic and social acceptance, despite their technical efficacy. There exists a huge demand for coordinated efforts to mitigate the negative impacts of the so-called ‘development’ activities that are tampering with the very existence of humanity, natural resources, and the environment. Agriculture, though historically the first and foremost occupation of humans, has always been intrigued by nature at the cost of natural calamities and uncertainties in ensuring the availability and timely distribution of essential resources. In the modern context of water-energy-food paradigms, the agriculture sector can play a pivotal role in bolstering the state economy in multifaceted ways by assuming the adaptability of recent scientific advancements to the core functional attributes of sustainability. In this scenario, the concept of regenerative agriculture can be better understood as a holistic, self-adaptive, and integrated approach towards not only bringing about a revolutionary breakthrough in agricultural productivity but also providing a retroactive and reconstructive strategy for minimizing the inevitable consequences of destructive developments. An organized, synthesized approach in this direction would certainly culminate in fabricating close-connected, supportive interlinking pathways for related fields of science and technology to make them more beneficial towards the responsible productivity expected from the agriculture sector [1].The prospects of environmental research have established a close interlink with the agriculture sector, mainly through the attributes of material, energy, and living species. It is envisaged that the environment is a bigger space with multiple spheres of activity, while agriculture comprises a smaller sect with limited but well-defined interactions with the environment [2]. Even though it is generally accepted that most of these interactions (such as soil quality and crop growth, water requirement and crop selection, raining pattern and cropping seasons, etc.) are mutually directed, one could perceive them as if initially driven by agricultural activities. However, there are some other interactions where environmental attributes are driving agricultural activities and outputs [3-5]. Most of these interactions are, nonetheless, subtle and complex, and little is known about their individual lead roles. Hence, it is important to identify the specific attributes of some of the well-known interactions and look for the key drivers towards addressing the original problem of agriculture and sustainability.A dynamic interaction between agriculture and environment is observed to be well-reinstated through the simultaneous existence and exchanges of the energy, materials and activities (Figure 1). It is, however, expected that a compensating step is essential to redirect (or push forward) such exchanges without creating adverse environmental impacts as well as agricultural losses.List of About the Conference, Conference Model, Publication of the Proceedings, Proceedings Editors, Technical Advisory Committee are available in this Pdf.

Determination of Water Requirements for Major Crops in Sindh Using Cropwat Model

Due to population growth and increasing urbanization trends, there is a shortage of water around the world. Irrigation systems are critical for increasing crop output and ensuring food security. Desertification caused by humans and water shortages have further exacerbated the world's regular water scarcity, putting a significant impact on food production. Water scarcity and rising food demands necessitate greater efficiency in water use, both in rain-fed and irrigated agriculture. Hence, it is the most demanding requirement for the farming community to use some type of irrigation planning to conserve the limited water resources. The purpose of this study is to calculate crop water requirements and irrigation scheduling for some major crops in Sindh using the CROPWAT model developed by FAO of United Nations. In this regard, selection from among the major crops cultivated in the Sindh province of Pakistan was made in such a way at least one crop from each category be included in this research. Accordingly, Wheat as Rabi, Rice as Kharif, Sugarcane as Perineal and Chili and Rape-seed Mustard as Cash Crops were selected. CROPWAT model calculates crop water requirements and irrigation scheduling from existing or new climatic- and crop-information. The Input crop data for CROPWAT software was obtained from various online resources and published information, whereas, climatic data incorporated in the CLIMWAT tool attached with CROPWAT was utilized. In addition, the software used average rainfall data of the particular years for each crop observed at DRIP Tandojam metrological station. The results of water requirements for the crops were obtained through CROPWAT model are: Wheat (352.1 mm); Rice (1662.5 mm); Sugarcane (2184.5 mm); Chili (714.4 mm); and Rape-seed Mustard (475.6 mm), while those by lysimeter were: Wheat (415 mm); Rice (1633 mm); Sugarcane 2150 mm); Chili (808.9 mm); and Rape-seed mustard (424.87 mm). These results delineate that there is decrease in crop water requirement of Wheat, Rice, Sugarcane and Chili to the tune of 15%, 1.5%, 11.6% and 1.7%, respectively, whereas an increase of 12% for Rape-seed mustard.

Impact of Irrigation and Fertigation Levels on Growth, Yield Components and Yield of Aerobic Rice (Oryza sativa L.) under Drip System

Back ground: More than half of the world’s population depends on rice, which is grown in transplanting conditions. Due to the global water crisis, irrigated rice systems are no longer sustainable. Therefore, cultivating aerobic rice with drip irrigation and fertigation meets the crop water and nutrient requirements, results in the optimal yield. Method: A field experiment was conducted during summer 2022 and 2023 at Research Farm of Wetland in Tamil Nadu Agricultural University, Coimbatore, to evaluate the performance of aerobic rice under drip irrigation and fertigation levels on growth and yield of Co-51 variety. The experiment was laid out in randomized complete block design with combination of thirteen treatments, replicated thrice. Results: Among different treatments, drip fertigation @ 100% PE up to 30 DAS+150% PE up to 60 DAS+ 200% PE up to 90 DAS with 125% RDF (25% through NF+75% through WSF) resulted significantly superior growth characters viz., higher plant height (92.54 and 95.95 cm), no. of tiller m-2(469.78 and 478.42), total drymatter production m-2 (988.21 and 1025.42 g m-2), no. of filled grains panicle-1 (107.5 and 114.5), total no. of grains panicle-1 (122.6 and 127.1), fertility percentage (87.7% and 90.1%) and lower no. of ill filled grains panicle-1 (15.1 and 12.6), grain yield (4316 and 4446 kg ha-1) and straw yield (5528 and 5775 kg ha-1) as compared to others, during summer 2022 and 2023, respectively.

Analysis the characterization of climate change and its impacts on smallholder farmers in Eastern Ethiopia

The impact of climate change on agriculture and food production is manifested through alterations in agro-ecological factors. The region of sub-Saharan Africa is particularly susceptible to the effects of climate change and variability, given its reliance on rainfall-dependent agriculture and inadequate adaptive capabilities. The objective of this study was to analysis of the characterization of climate change and its impacts on smallholder farmers in eastern Ethiopia. Historical daily rainfall, maximum and minimum temperature in 1991–2021 years of 12 weather station were collected from National Meteorological Institute of Ethiopia and NASA/POWER. Data quality control, trend test and outlier detection test were used. Climate characterization (onset, cessation, LGS and Dry/wet spell length) and precipitation concentration index were examined. The earliest, and latest start of rainy season in Kombolcha and Haramaya were 81DOY (March 21) and 225DOY (August 13) within mean 130DOY and 125DOY, respectively. In study area the minimum and maximum length of growing period of was recorded in Chinakesen and Haramaya by the values of 32DOY and 253DOY (Days of the year) respectively. In this arid and semi-arid areas, growing period was recorded under the short season crop maturity while long cropping season maturity does no satisfy crop water requirement due to moisture stress. The coefficient of variation of length of growing season from Mieso and Chinakesen were 36.2% and 47.9% which implies that the areas were highly vulnerable to climate extreme events of drought. Length of growing season (LGS) of Gemechis district was decreasing by a factor −2.533 shows highly significant at alpha value of 0.05 confidence level. Dry spell length probability occurrence of 5 days during the main cropping rainy season is significantly very high. The 30 years of data record suggests that a 29–48% precipitation concentration index of kiremt (JJAS) and belg (FMAM) seasonal rainfall which are demonstrating irregular precipitation. kiremt (JJAS) rainfall trend tests of Babile, Kurfa chale and Mieso districts were increasing by a factor of 2.016, 2.043 and 2.117, respectively and statistically significant at 95% confidence level, in the time period of 1991–2021 years. If furtherstudy will be examined climate extreme indices and adaptation strategies designed to simulate the impacts and variability of climate change in the study area.

A Low Cost Smart Irrigation Planning Based on Machine Learning and Internet of Things

In an agriculture system major issues of irrigation systems for plants water supply is a critical factor. A significant amount of freshwater is required for this task but after the utilization of water in the irrigation process it is being polluted. In addition, the excessive use of water during the irrigation process can negatively affect crop production. Therefore, we need to provide a balanced amount of water for effective crop production and conservation of water. In this paper, we proposed low-cost irrigation planning with two key aims: first is to reduce the installation and maintenance costs of data collection in innovative irrigation systems and second is to control the valve for water supply automatically. In this context, we first provide a review of recent irrigation systems based on the Internet of Things (IoT) and Machine Learning (ML). Next, we introduce a working plan to collect crop water requirements using a soil moisture sensor. Then, an algorithm is proposed to decide the water supply for water treatment. Finally, the experiments are conducted on the samples collected from the farmland of wheat crops. Additionally, two different scenarios are considered to collect the water requirement samples. Based on the experimental and theoretical analysis of water requirements the proposed irrigation system can reduce the water demand by up to 25% as compared to traditional ways of irrigation. Moreover, in comparison of popular valve automation system the proposed multiple valve based system reduces the amount of water wastage up-to 22%. Therefore by utilizing the advance computational techniques (IoT and ML), we can reduce the cost of irrigation system and planning.

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.

Determination of Crop Coefficients and Evapotranspiration of Potato in a Semi-Arid Climate Using Canopy State Variables and Satellite-Based NDVI

Estimating crop coefficients and evapotranspiration (ET) accurately is crucial for optimizing irrigation. Remote sensing techniques using green canopy cover, leaf area index (LAI), and normalized difference vegetation index (NDVI) have been applied to estimate basal crop coefficients (Kcb) and ET for different crops. However, analysis of the potential of these techniques to improve water management in irrigated potato (Solanum tuberosum L.) is still lacking. This study aimed to assess the modified nonlinear relationship between LAI, Kcb and NDVI in estimating crop coefficients (Kc) and ET of potato. Moreover, Kc and ET were derived from the measured fraction of green canopy cover (FGCC) and the FAO-56 approach. ET estimated from the FAO-56, FGCC and NDVI approaches were compared with the ET simulated using the LINTUL-Potato model. The results showed that the Kc values based on FGCC and NDVI were on average 0.16 lower than values based on FAO-56 Kc during the mid-season growing stage. ET estimated from FAO-56, FGCC and NDVI compared well with ET calculated by the LINTUL-Potato model, with RMSE values of 0.83, 0.79, and 0.78 mm day−1, respectively. These results indicate that dynamic crop coefficients and potato ET can be estimated from canopy cover and NDVI. The outcomes of this study will assist potato growers in determining crop water requirements using real-time ETo, canopy state variables and NDVI data from satellite images.

Modulating the antioxidant defense systems and nutrients content by proline for higher yielding of wheat under water deficit

Numerous plant metabolites, especially amino acids, are accumulated as a result of stress. These amino acids are crucial for plant metabolism and development and have historically been viewed as the building blocks of proteins. Several studies suggested that there is a link between proline buildup and exposure plants to stress. Proline performs important functions under stress in addition to be a great osmolyte, antioxidant enzyme, acts as an antioxidant defense and signaling molecules. Two field trials were during two successive winter seasons (2020/2021 and 2021/2022). The effects of proline foliar application (0, 100, 200, 300 and 400 mg L−1) and irrigation water levels (irrigation by 100, 80 and 60% of crop water requirements, CWR, CWR100, CWR80 and CWR60, respectively) on plant pigments, antioxidants activity, yield traits and nutrient contents of wheat were assessed. The experiment was designed in a split-plot involving 4 replicates. Drought stress (CWR80 and CWR60) led to reductions in photosynthetic pigments, and yield components. Under severe water stress (CWR60), proline 200 and 300 mg L−1 recorded the highest values of chlorophyll a, chlorophyll b, and total pigments. Application of proline 300 mg L−1 was the potent practice for enhancing the antioxidant activity% (DPPH radical scavenging) and phenols content under CWR100 and CWR80. Indole acetic acid (IAA) possessed the maximum values with proline 200 mg L−1 under all irrigation patterns. Under severe deficit water (CWR60), without proline spraying (for super oxide dismutase), 200, 300 or 400 mg L−1 proline (for peroxidase) and 200 or 400 mg L−1 proline (for polyphenol oxidase) recorded the highest values. Spraying proline 300 mg L−1 achieved the highest values of grain number spike−1, weight of 1000 grains and grain yield ha−1 under different irrigation regimes. In conclusion, proline is considered a good mitigator for drought stress, due to it increased wheat plant tolerance to water deficiency throughout improving plant physiology and consequently yields quantity and quality. The most efficient concentrations of proline for coping the adverse impact of drought were 200 and 300 mg L−1.