Irrigation Scheduling Strategies Research Articles

Irrigation Scheduling Impacts Vegetative Growth, Seed Yield, and Fungal Diseases of Spinach Seed Crops in a Maritime Mediterranean Climate

Although irrigation scheduling has been studied for diverse vegetable crops, much less attention has been given to irrigation scheduling for the seed crops on which these production systems rely. In spinach, for which irrigation scheduling needs are likely to vary greatly between seed and leaf production, this leaves seed producers without adequate resources to make irrigation scheduling decisions. Our research sought to fill this gap by evaluating two alternative irrigation scheduling strategies (a publicly available decision-support tool and soil moisture sensors) and four soil moisture thresholds for irrigation for their impacts on vegetative growth, marketable seed yield, seed quality, and the severity of Stemphylium leaf spot (caused by Stemphylium vesicarium and Stemphylium beticola), a common foliar disease of spinach, under sprinkler irrigation. We found that in all 3 years of the study, earlier and more frequent irrigation increased vegetative growth. However, marketable seed yield only increased relative to the control treatment based on farmers’ standard irrigation practices in 1 of the 3 years—a year with an abnormally late planting date. This indicates that vegetative growth is more responsive than seed yield to earlier and more frequent irrigation, and that increases in vegetative growth do not translate directly to increased marketable seed yield. Contrary to the expected increase in Stemphylium leaf spot severity with increasing irrigation, the severity decreased in both years it was measured, likely as a result of the small stature of the spinach seed parent lines used in our study and opportunistic pathogenicity on moisture-stressed plants. These results provide a useful foundation from which spinach seed producers can make irrigation management decisions for their crops that underpin a valuable global industry.

Comparative analysis in selecting best irrigation method to maximize tomato yield from various irrigation approaches in water scarce regions

It is now essential for farmers to evaluate and compare different irrigation scheduling strategies in order to operate their irrigation systems in accordance with their resources and achievements. For tomato crops, a rigorous comparison was made between the yield of soil moisture and irrigation scheduling systems based on evapotranspiration. The experimental setup used a randomized complete block design (RCBD) with three replications, with water requirements of 100%, 75%, and 50%. Using a ReplogleBos-Clemmens (RBC) flume with an input rate of 1.62 l/s, water was applied to the furrows. The total applied amount in the Soil Moisture (SM) and Evapotranspiration (ET) based methods was 229.1 mm, and 280 mm, respectively. The collected data were assessed using ANOVA at a 5% significance level. For every technique, yield values, yield attributes, and agronomic traits were computed. The result indicates that there is no appreciable variation among the factors. The net yield advantage was highest at 75% SM and lowest at 25% SM. Furthermore, compared to the ET-based approach, it was demonstrated that the SM-based method conserved approximately 18.2% of irrigation water. As a result, when producing crops that require a lot of water, like tomatoes, in areas with limited water resources, SM-based irrigation scheduling is preferable to ET-based irrigation scheduling.

Assessing the impact of irrigation and nitrogen management on potato performance under varying climate in the state of Florida, USA

Optimizing irrigation and nitrogen (N) fertilizer management in irrigated potato crops grown on sandy soils in subtropical regions such as in northeastern Florida, USA is essential to sustain a high yield and to minimize leaching. N applications in this region typically occur at approximately 25–30 days prior to planting (Npre), at emergence (Neme), and at tuber initiation (Nti). However, recent studies suggest that applying N near planting (Npl) enhances fertilizer N use efficiency (FNUE). We combined experimentation with modeling to assess irrigation and N management options for potato in northeastern Florida. We first aimed to evaluate the DSSAT/CSM-SUBSTOR-Potato model using two-year irrigated field experiments conducted on sandy soils with variable N rates and application timings. CSM-SUBSTOR-Potato accurately simulated aboveground plus tuber dry weight [Relative root mean squared error (RRMSE) = 26.4%, Willmott’s index (d) = 0.98] and N accumulation (RRMSE = 28.6%, d = 0.97). Soil moisture and mineral N were captured well overall, but they were often underestimated due to a water table influence that is currently not considered in DSSAT. Subsequently, CSM-SUBSTOR-Potato was applied to simulate tuber yield, N leaching, and FNUE under scenarios of irrigation scheduling and N-fertilizer application (rate/timing) strategies, focusing on Npre versus Npl aiming to improve resource use efficiency. The simulations indicated that a target of 60% and 70% of the available soil water can be safely used as an irrigation strategy to achieve a high yield, while reducing irrigation water applied and N leached to the environment. Overall Npl increased crop N uptake by 10%, tuber yield by 7%, reduced N leached by 13%, and consequently increasing FNUE by 9%, compared to Npre across the irrigation treatments. Thus, Npl should be preferred in sandy soils and climate-risky subtropical environments, along with Neme and Nti as key timings to synchronize N supply with potato growth.

Irrigation Scheduling Strategies for High-Yield, WaterEfficient on Tomato Production under Drip Irrigation in Western Uttar Pradesh, India

Irrigation Scheduling Strategies for High-Yield, WaterEfficient on Tomato Production under Drip Irrigation in Western Uttar Pradesh, India

Determination of the Most Efficient Forage Sorghum Irrigation Scheduling Strategies in the U.S. Central High Plains Using the AquaCrop Model and Field Experiments

The current status of water resources in the U.S. Central High Plains necessitates adopting water conservation practices to move toward a sustainable agricultural economy. Identifying proper irrigation scheduling techniques is a conservative practice to maintain the sustainability of the agricultural systems. However, conducting field experiments is time and money consuming. Thus, the utilization of crop models, such as AquaCrop, could be a convenient alternative to field experiments. The FAO AquaCrop model was calibrated and validated for simulating forage sorghum yield response to various deficit irrigation conditions in a semi-arid region. Afterwards, the model was used to investigate the efficiency of the pre-season and in-season irrigation scheduling scenarios. In this study, the soil water status at the planting time was considered as the indicator of the pre-season irrigation level. Therefore, the pre-season irrigation scenarios were arranged as the replenishment of soil water deficiency at the time of planting at up to 30, 50, and 100% of the soil’s total available water for the first 60 cm of soil depth and the same replenishment levels for the entire crop root zone (150 cm soil depth). Then, AquaCrop long-term (37 years) simulations of forage sorghum biomass and irrigation water use efficiency reactions to three levels of maximum allowable depletion (MAD) (40, 55, and 70%) were compared to three fixed irrigation interval (4, 6, and 10 days) scenarios by considering six pre-season irrigation conditions (36 scenarios). The scenarios analysis found the 10-day irrigation interval and the MAD levels of 55% and 70% to be the most efficient irrigation scheduling strategies if combined with pre-season irrigation that brought the crop root zone (0–150 cm soil depth) to field capacity. Moreover, the 40% MAD application was the least efficient strategy. This study’s outputs can be a baseline for establishing forage sorghum irrigation scheduling in the U.S. Central High Plains. However, exploring the interactions of irrigation scheduling strategies with other irrigation and agronomic practices, such as salinity management and fertilizer application, is highly recommended.

An Automated Data-Driven Irrigation Scheduling Approach Using Model Simulated Soil Moisture and Evapotranspiration

Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making.

Data-driven irrigation scheduling increases the crop water use efficiency of Cabernet Sauvignon grapevines

In the context of water management in agriculture, irrigation scheduling is critically important as it optimises water application to crops and can also target specific production goals. However, there is no consensus on the ideal irrigation scheduling strategy regarding crop water use efficiency (WUEc). In a premium Cabernet Sauvignon vineyard in Coonawarra, South Australia, over three growing seasons, irrigation scheduling strategies based on experience or historical knowledge (‘GROW’ treatment) were compared to data-driven strategies including crop evapotranspiration, and plant and soil water status thresholds to evaluate their effects on leaf- and vine-level WUEs. A final treatment, GROW + , that doubled the GROW level of irrigation was also evaluated in the third season. The WUE metrics were determined at the leaf, vine, and fruit scales as intrinsic WUE (WUEi), crop WUE (WUEc), and carbon isotope ratio (δ13C), respectively. Furthermore, the irrigation strategies were evaluated in the background of two contrasting soil types: Terra Rossa (light clay, well-drained) and Rendzina (heavier clay, poorly drained). Seasonal soil and vine water status, leaf gas exchange, and light interception were measured, and yield components and pruning weights were obtained following harvest. The amount of seasonal irrigation water based on the data-driven strategies was up to 65% lower across both soil types compared with the GROW or GROW + approaches. WUEi and δ13C were largely similar between treatments. However, for vines grown on Terra Rossa soil, little to no yield penalty was observed when data-driven irrigation scheduling was applied, in addition to increased WUEc values of up to 41%. It can be concluded that irrigation scheduling decisions based on data were superior to the conventional irrigation scheduling method on account of reducing irrigation water volume and increasing WUE, particularly in Terra Rossa soils.

Evaluation of IoT based smart drip irrigation and ETc based system for sweet corn

Smart irrigation is one form of precision irrigation that can help farmers maximize crop production while preserving water and energy. Many different types of smart irrigation system can be used, but there is no scientific consensus about which type is the finest. The purpose of this study was to compare various irrigation methods: an IoT-based soil moisture monitoring (IoT-SM) method using moisture sensors and an ET–based strategy. The best irrigation scheduling strategy should be determined by commercial production: keeping the soil water content near field capacity will result in a better fresh yield but not a more dry matter. Sweet corn was chosen as the crop under study. Growth characteristics (plant height, yield, and water productivity) were compared for each of the two approaches. For IoT based soil moisture monitoring method, two irrigation regimes were used: 43.5 per cent and 34.8 per cent (as field capacity (FC) of soil and 80% of FC, respectively) and crop evapotranspiration (ETc 100 per cent) for the ET-based method. The results showed that the IoT-SM 43.5 per cent could produce higher yields of about 12.05% and water savings by 11% compared to the ETc 100 per cent-based irrigation method. The developed IoT system was durable and water-resistant, allowing it to be deployed in outdoor agriculture. At the same time, a solar power supply eliminates the need for cabling and reduces sensor node maintenance.

Cool- and Warm-Season Turfgrass Irrigation with Subsurface Drip and Sprinkler Methods Using Different Water Management Strategies and Tools

This study compared the performances of subsurface drip irrigation (SDI) to sprinkler irrigation (SI) of cool-season turf mix (CS) and warm-season (WS) turfgrass species while investigating their response to different irrigation scheduling strategies. Moreover, the suitability of crop water stress index (CWSI) for monitoring water stress and scheduling irrigation was tested. Irrigation was applied up to the field capacity when 30%, 50% and 70% of total available water was consumed. All parameters, including color, quality, fresh yield, dry matter yield, irrigation water productivity, water productivity, vegetation height and mowing, differed significantly for different irrigation methods and water supply strategies for both species. The best visual turf was maintained under non-limiting soil moisture conditions (30%) in all main and sub-treatments. At this irrigation threshold, maximum and minimum amounts of irrigation water were applied in SI CS treatment (523.5 mm) and SDI WS treatment (298.6 mm), respectively. Warm-season turf required up to 40% less water than cool-season turf mix. In the water-scarce regions, 50% treatment for cool-season and 70% treatment for warm-season can be suggested for acceptable visual quality, lower water consumption and less frequent clipping. The mean CWSI before irrigation, representing irrigation threshold, ranged between 0.22–0.70.

Effect of Irrigation Scheduling Methods on Maize Grain Yield and Nitrate Leaching in Central Minnesota

Highlights Four irrigation scheduling methods were compared for maize yield vs nitrate-N losses. Yield was similar with SM, CB, and EPIC, and lower for IMA irrigation scheduling. Nitrate-N leaching losses were highly dependent on precipitation as well as on the rate and frequency of irrigation. IMA and EPIC methods should be adopted with further calibration. ABSTRACT. Coarse-textured soils in the Great Lakes states have limited water holding capacity, which makes irrigation essential to obtain optimum yields for maize; however, excess irrigation has the potential to contaminate ground and surface water resources through deep seepage of applied fertilizers, making irrigation management critical. The objective of this study was to evaluate the impact of different strategies for agricultural irrigation water management on maize grain yield and nitrate leaching losses. In this three-year study (2019-2021), the following four irrigation scheduling strategies were compared under continuous maize cropping systems at two sites in Central Minnesota, Becker and Westport: (1) soil moisture monitoring using soil moisture sensors (SM), (2) checkbook method of irrigation scheduling (CB), (3) irrigation management assistant tool (IMA), and (4) EPIC model auto-irrigation (EPIC). Overall, in comparison to the CB method (highest irrigation method), the SM, IMA, and EPIC methods recommended 2.58%, 51.05%, and 9.27% less water in irrigation, respectively. In terms of grain yield, no significant differences were observed between irrigation treatments at the Becker site. The average maize grain yield at the Becker site was 12.02, 11.97, 11.76, and 10.62 Mg/ha, for SM, CB, EPIC, and IMA treatments, respectively. However, in the drier 2021 season, a grain yield loss of 30%, 27%, and 21% was observed under IMA when compared to SM, CB, and EPIC methods, respectively, at the Becker site. At the Westport site, on average, no significant difference in yield was obtained between SM, CB, and EPIC, however, yield under IMA treatment was significantly lower (12%) than CB. The results suggest that nitrate leaching losses can be significantly reduced by altering the rate and frequency of irrigation. IMA treatment resulted in significantly lower nitrate leaching (62%) than the CB method, reducing nitrate leaching from 35.86 kg/ha to 13.71 kg/ha at the Westport site. No significant differences in nitrate leaching were observed at the Becker site because the impact of precipitation superseded the impact of irrigation. Results from this study can aid in the adoption and further development of less labor-intensive and more accurate methods of irrigation scheduling in coarse-textured soils of Great Lakes states. Keywords: Coarse textured soils, Crop evapotranspiration, Irrigation scheduling methods, Maize grain yield, Nitrate-N leaching, Water quality, Water quantity.

Measuring turfgrass canopy interception and throughfall using co-located pluviometers.

Turfgrass management relies on frequent watering events from natural precipitation or irrigation. However, most irrigation scheduling strategies in turfgrass ignore the magnitude of canopy interception. Interception is the process by which precipitation or irrigation water is intercepted by and evaporated from plant canopies or plant residue. The objective of this study was to quantify the magnitude of precipitation interception and throughfall in ‘Meyer’ zoysiagrass (Zoysia japonica L.) and ‘007’ creeping bentgrass (Agrostis stolonifera L.). We used a new method consisting of co-located pluviometers with and without circular turfgrass patches to measure interception and throughfall. The resulting dataset includes 15 storms and 25 individual rainfall events ranging in precipitation totals from 0.3 mm to 42.4 mm throughout the research study. Throughfall amount resulted in a strong (r = 0.98) positive linear relationship with precipitation totals. On average, zoysiagrass and creeping bentgrass canopies intercepted a minimum of 4.4 mm before throughfall occurred. This indicates that, on average, no precipitation reaches the soil surface for precipitation events <4.4 mm. After the point of throughfall, 16% of each additional millimeter of precipitation or irrigation is lost due to interception. Nearly, 45% of the area of the contiguous U.S. could result in >50% of the annual precipitation being intercepted by canopies of zoysiagrass and bentgrass. This study provides detailed insights to understanding the interception dynamics in turfgrass and highlights the inefficient nature of small precipitation and irrigation events in turfgrass systems.

Combination of Sentinel-2 Satellite Images and Meteorological Data for Crop Water Requirements Estimation in Intensive Agriculture

In arid and semi-arid regions, agriculture is an important element of the national economy, but this sector is a large consumer of water. In a context of high pressure on water resources, appropriate management is required. In semi-arid, intensive agricultural systems, such as the Tadla irrigated perimeter in central Morocco, a large amount of water is lost by evapotranspiration (ET), and farmers need an effective decision support system for good irrigation management. The main objective of this study was to combine a high spatial resolution Sentinel-2 satellite and meteorological data for estimating crop water requirements in the irrigated perimeter of Tadla and qualifying its irrigation strategy. The dual approach of the FAO-56 (Food and Agriculture Organization) model, based on the modulation of evaporative demand, was used for the estimation of crop water requirements. Sentinel-2A temporal images were used for crop type mapping and deriving the basal crop coefficient (Kcb) based on NDVI data. Meteorological data were also used in crop water requirement simulation, using SAMIR (satellite monitoring of irrigation) software. The results allowed for the spatialization of crop water requirements on a large area of irrigated crops during the 2016–2017 agricultural season. In general, the crops’ requirement for water is at its maximum during the months of March and April, and the critical period starts from February for most crops. Maps of water requirements were developed. They showed the variability over time of crop development and their estimated water requirements. The results obtained constitute an important indicator of how water should be distributed over the area in order to improve the efficiency of the irrigation scheduling strategy.

Irrigation Scheduling and Weed Management: A Sustainable Approach for Managing Broomrape and Other Weeds in Tomato Crop

Broomrape (Orobanche cernua L.) is an obligate root parasitic weed that significantly reduces the qualitative and yield attributes of tomatoes globally. The efficient management of broomrape is challenging because of its complicated parasitic nature. Field trials were conducted to assess the influence of various irrigation scheduling and weed control strategies on broomrape, weed presence, and tomato productivity. The experiment was conducted with a randomized complete block design (RCBD) with a split-plot arrangement and was replicated three times. Three irrigation intervals (3, 6, and 9 days) were assigned to the main block, while sub-blocks including treatments and year were taken as the source of variance (year × irrigation timing × treatments). The experiment comprised sixteen treatments, including transparent polythene, black polythene, weedy check (Control), sole weeding of broomrape only, weeding of all weeds, weeding except broomrape, humic acid 25 kg ha−1+ copper oxychloride in single and split doses, copper oxychloride (1.5 kg a.i ha−1 in single and split doses), ammonium sulphate 200 kg ha−1 in single and split doses, copper sulfate (2 kg ha−1 in single/split doses), and glyphosate 48 SL (1.5 kg a.i ha−1) and pendimethalin 33 EC (1.44 kg a.i ha−1). The results revealed that among the various irrigation intervals, the highest broomrape intensity (4.34 plant−1) was observed with a9-day irrigation interval. Similarly, the highest weed density (35 m−2) resulted in a3-day irrigation interval. Furthermore, irrigation at a 6-day interval increased the plant height by 11%, fruit yield tons ha−1 by 24.9 %, and produced the highest cost/benefit ratio (CBR) of (1:4). Black polythene, transparent polythene, and pendimethalin reduced the weed density by 92%, 89%, and 84%;weed dry biomass by 97%, 95%, and 91%; and broomrape intensity by 67%, 77%, and 28%. Conversely, the plant height increased by 24%, 23%, and 23.6%; and fruit yield by 286%, 270%, and 191%; and had the highest CBR of 1:5, 1:4, and 1:4, respectively, as compared to the weedy check. Consequently, an increase in irrigation frequency increases other weed densities and decreases the broomrape intensity plant−1 of tomato. Therefore, black polythene could be recommended in a severely broomrape-infested field. Moreover, irrigation at 6-day intervals combined with pendimethalin and ammonium sulfate fertilizers revealed the lowest incidence of broomrape and other weeds and produced an economic yield.

Comparing the water use metrics of just-in-case, just-in-time and justified irrigation strategies using a scenario-based tool

Efficient water use is an important outcome of effective irrigation scheduling strategies. We developed a physically-based hydrology tool IrriSET (Irrigation Strategy Evaluation Tool) to simulate the performance of different scheduling strategies against three water use metrics - amount of irrigation used, drainage resulted from irrigation, and duration root zone soil water (crop soil water reservoir) was held within optimum pasture growth conditions during the irrigation season. IrriSET was tested on an irrigated dairy farm in Canterbury, New Zealand, simulating the daily soil water balance using observed and estimated data on rainfall, evaporation, root zone soil water holding capacity, irrigation supply reliability, irrigation infrastructure limitations (how quickly and how much irrigation could be applied per event), and irrigation application efficiency (designed versus actual application) from eighteen irrigation seasons for three scheduling strategies – ‘just-in-case', ‘just-in-time', and ‘justified’. Just-in-case, a rostered, supply-based irrigation strategy, resulted in the most irrigation applied per season and drainage generated, and the least duration of time soil water held in the optimum pasture growth condition. Soil water demand-based just-in-time, and demand- and weather-forecast based justified irrigation strategies resulted in similar water use metrics, though the latter consistently and marginally outperformed the former. Even during irrigation seasons with below normal rainfall and average irrigation supply conditions, though the demand and supply-based strategies used similar irrigation amounts, the latter held soil water in the optimal growth range longer than the former. A strategy that proactively manages irrigation based on supply (irrigation water availability and forecast rainfall) and demand (soil water condition) has been shown to be environmentally sustainable and positively influential of soil water availability within root zone, and thus productivity.

Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle

Crop water status and irrigation requirements are of great importance to the horticultural industry due to changing climatic conditions leading to high evaporative demands, drought and water scarcity in semi-arid and arid regions worldwide. Irrigation scheduling strategies based on evapotranspiration (ET), such as regulated deficit irrigation, requires the estimation of seasonal crop coefficients (kc). The ET-driven irrigation decisions for grapevines rely on the sampling of several kc values from each irrigation zone. Here, we present an unmanned aerial vehicle (UAV)-based technique to estimate kc at the single vine level in order to capture the spatial variability of water requirements in a commercial vineyard located in South Australia. A UAV carrying a multispectral sensor is used to extract the spectral, as well as the structural, information of Cabernet Sauvignon grapevines. The spectral and structural information, acquired at the various phenological stages of the vine through two seasons, is used to model kc using univariate (simple linear), multivariate (generalised linear and additive) and machine learning (convolution neural network and random forest) model frameworks. The structural information (e.g., canopy top view area) had the strongest correlation with kc throughout the season (p ≤ 0.001; Pearson R = 0.56), while the spectral indices (e.g., normalised indices) turned less-sensitive post véraison—the onset of ripening in grapes. Combining structural and spectral information improved the model’s performance. Among the investigated predictive models, the random forest predicted kc with the highest accuracy (R2: 0.675, root mean square error: 0.062, and mean absolute error: 0.047). This UAV-based approach improves the precision of irrigation by capturing the spatial variability of kc within a vineyard. Combined with an energy balance model, the water needs of a vineyard can be computed on a weekly or sub-weekly basis for precision irrigation. The UAV-based characterisation of kc can further enhance the water management and irrigation zoning by matching the infrastructure with the spatial variability of the irrigation demand.

Evaluating deficit irrigation scheduling strategies to improve yield and water productivity of maize in arid environment using simulation

Water scarcity and rising temperatures are creating serious concerns over the sustainability of agriculture systems in arid regions of the Middle East and North Africa. The aims were (i) to calibrate and evaluate the Decision Support System for Agrotechnology Transfer (DSSAT) model using detailed experimental datasets on maize (Zea mays L.) yield and water productivity in an arid Mediterranean environment and (ii) to determine the impacts of various irrigation scheduling strategies on maize yield and water productivity in arid sandy soils and produce irrigation scheduling recommendations that maximize the marginal benefit per unit water applied. The goodness-of-fit statistics comparing the observed and simulated crop phenology, grain yield, dry matter, evapotranspiration (ET), and soil water content indicate that the model simulates these crop and soil variables reasonably well for a medium-maturity maize hybrid (~ 110 days to physiological maturity) commonly grown in the study region. Long-term simulations (1984–2018) using the well-calibrated model were performed and included three irrigation scheduling strategies: (i) soil water-based irrigation scheduling, (ii) ET-based threshold irrigation scheduling, and (iii) growth-stage ET-based irrigation scheduling. In the soil water-based, four levels of maximum allowable depletion (MAD) of available soil water content (AWC) were tested using the auto-irrigation option of DSSAT. Results indicated that MAD 50% is recommended for scheduling irrigation in arid sandy soils for potential irrigation water saving without unacceptable yield loss. The ET-based threshold consisted of a combination of four cumulative net ET threshold (ETH) triggers of 14, 21, 28, and 35 mm and five ET replacement levels of 50%, 70%, 90%, 110%, and 130% ET using the DSSAT ET-based auto-irrigation option. Based on this irrigation strategy, it is more favorable to irrigate at the high frequency of ETH 14 or 21 mm than low frequency of ETH 35 mm, even when replacing 130% ET, as high drought-stress during the crop development and reproductive stages were observed for ETH 35 mm due to the omission of key irrigation events. In the growth-stage ET-based, two levels of irrigation of 100% ET and 60% ET estimated based on the crop coefficient approach were tested with different combinations of targets during the vegetative and reproductive growth stages. Results indicated that meeting crop ET requirements during the reproductive stage is more essential than during the vegetative stage for greater yield and enhanced WUE, particularly when available seasonal irrigation water is less than seasonal full crop ET requirements. In water limited environments, reasonable yield and enhanced WUE can be achieved by applying a deficit rate of 40% ET during the vegetative stage and 80% ET during the reproductive stage or scheduling irrigation based on soil water content to ensure AWC of 30% in the top 0–30 cm soil layer throughout the growing season. Concepts developed in the present study can be adapted to effectively manage irrigation scheduling for medium-maturity maize hybrids in arid sandy soils with low AWC and organic C content.

Irrigation scheduling strategies for pepper based on evaporation and reference evapotranspiration

The experiment with drip irrigated pepper was conducted at the Rimski Šančevi experimental field of the Institute of Field and Vegetable Crops in Novi Sad in 2019. The irrigation was scheduled on the basis of the water balance method. Two methods were used to compute the daily evapotranspiration of pepper (ETd): reference evapotranspiration (ETo) and evaporation from an open water surface (Eo). Crop coefficients (kc) and corrective coefficients (k) were used to convert ETo and Eo values into ETd. Kc and k were 0.3-0.4, 0.6-0.7, 0.9-1.1, 0.8-0.9 and 0.4, 0.7, 1.0 and 0.8 for initial stage, crop development, mid season, and late season, respectively. ETo was calculated by the Hargreaves equation. Eo values were measured by a Class-A pan located at a meteorological station near the experimental plot. Irrigation started when readily available water (RAW) in the 0.3 m soil layer was completely absorbed by plants. Differences in crop yield (Y) and irrigation water use efficiency (IWUE) obtained using Eo (42.58 t ha-1, 15.20 kg m-3) and ETo (40.78 t ha-1, 14.56 kg m-3) were not statistically different. Evapotranspiration rate was 364.2 mm and 337.3 mm in Eo and ETo variant, respectively. The fact that the differences in Y and IWUE between different calculations of ETd were not statistically significant indicates that both methods can be recommended for irrigation scheduling programs for pepper in the climatic conditions of the Vojvodina region. However, priority should be given to ETo due to the easy accessibility and reliability of data.

Irrigation Scheduling to Promote Corn Productivity in Central Alabama

Agriculture is the largest consumer of water in the United States. Results from previous studies have shown that it is possible to substantially reduce irrigation amounts and maintain corn yield. The objectives of this study were to evaluate the advantages and disadvantages of two irrigation scheduling methods for corn production in Alabama. Two irrigation scheduling methods evaluated were: a) Checkbook, which is one of the conventional methods used by farmers that is based on the soil water balance estimated using water lost by evapotranspiration and its replacement through rainfall or irrigation, and b) Sensor-based, which was based on soil matric potential values recorded by soil moisture tension sensors installed in the field. The experimental field was divided into two irrigation management zones (zone A and zone B) based on soil properties of each field. During the 2014 season in zone A, significant grain yield differences were observed between the two irrigation methods. The Checkbook plots exhibited greater yield than Sensor-based plots: 10181 kg ha-1 and 9696 kg ha-1, respectively. The greater yield on the Checkbook plots could be associated with higher irrigation rate applied, 148 mm more, compared with the Sensor-based plots. In zone B, there were no significant yield differences between both irrigation methods; however, Sensor-based plots out yielded Checkbook plots, with 9673 kg ha-1 and 9584 kg ha-1, respectively. Even though the irrigation amount applied in Checkbook located in zone B was higher, 102 mm more, there were no significant yield differences. Therefore, it suggests that the Sensor-based method was promissory irrigation scheduling strategy under the conditions of zone B. In 2015, there were no significant grain yield differences between zone A and zone B when the data from the Checkbook plots were analyzed. However, the Sensor-based treatment produced a statistically significant difference of grain yield of 13597 kg ha-1 in zone A and 11659 kg ha-1 in zone B, also both zones received the same amount of irrigation. Overall results of both growing seasons indicated that the use of the Sensor-based irrigation scheduling treatment resulted in similar values of total profit per hectare when compared to Checkbook method. The Sensor-based method seems a promising strategy that could result in water and financial savings, but more research is required.

Mitigation of greenhouse gas emissions and reduced irrigation water use in rice production through water-saving irrigation scheduling, reduced tillage and fertiliser application strategies

Rice production systems are the largest anthropogenic wetlands on earth and feed more than half of the world's population. However, they are also a major source of global anthropogenic greenhouse gas (GHG) emissions. Several agronomic strategies have been proposed to improve water-use efficiency and reduce GHG emissions. The aim of this study was to evaluate the impact of water-saving irrigation (alternate wetting and drying (AWD) vs. soil water potential (SWP)), contrasting land establishment (puddling vs. reduced tillage) and fertiliser application methods (broadcast vs. liquid fertilisation) on water-use efficiency, GHG emissions and rice yield. The experiment was laid out in a randomised complete block design with eight treatments (all combinations of the three factors) and four replicates. AWD combined with broadcasting fertilisation was superior to SWP in terms of maintaining yield. However, seasonal nitrous oxide (N2O) emissions were significantly reduced by 64% and 66% in the Broadcast-SWP and Liquid fertiliser-SWP treatments, respectively, compared to corresponding treatments in AWD. The SWP also significantly reduced seasonal methane (CH4) emissions by 34 and 30% in the broadcast and liquid fertilisation treatments, respectively. Area-scaled GWPs were reduced by 48% and 54% in Broadcast-SWP and Liquid fertiliser-SWP treatments respectively compared to the corresponding treatments in AWD. Compared to AWD, the broadcast and liquid fertilisation in SWP irrigation treatments reduced yield-scaled GWPs by 46% and 37%, respectively. In terms of suitability, based on yield-scaled GWPs, the treatments can be ordered as follows: Broadcast-SWP < Broadcast-AWD = Liquid fertiliser-SWP < Liquid fertiliser-AWD. Growing-season water use was 15% lower in the SWP treatments compared with the water-saving AWD. Reduced tillage reduced additional water use during land preparation. The conclusions of this study are that improved water management and timely coordination of N fertiliser with crop demand can reduce water use, N loss via N2O emissions, and CH4 emissions.

Irrigation Scheduling Based on Wireless Sensors Output and Soil-Water Characteristic Curve in Two Soils.

Data-driven irrigation planning can optimize crop yield and reduce adverse impacts on surface and ground water quality. We evaluated an irrigation scheduling strategy based on soil matric potentials recorded by wireless Watermark (WM) sensors installed in sandy loam and clay loam soils and soil-water characteristic curve data. Five wireless WM nodes (IRROmesh) were installed at each location, where each node consisted of three WM sensors that were installed at 15, 30, and 60 cm depths in the crop rows. Soil moisture contents, at field capacity and permanent wilting points, were determined from soil-water characteristic curves and were approximately 23% and 11% for a sandy loam, and 35% and 17% for a clay loam, respectively. The field capacity level which occurs shortly after an irrigation event was considered the upper point of soil moisture content, and the lower point was the maximum soil water depletion level at 50% of plant available water capacity in the root zone, depending on crop type, root depth, growth stage and soil type. The lower thresholds of soil moisture content to trigger an irrigation event were 17% and 26% in the sandy loam and clay loam soils, respectively. The corresponding soil water potential readings from the WM sensors to initiate irrigation events were approximately 60 kPa and 105 kPa for sandy loam, and clay loam soils, respectively. Watermark sensors can be successfully used for irrigation scheduling by simply setting two levels of moisture content using soil-water characteristic curve data. Further, the wireless system can help farmers and irrigators monitor real-time moisture content in the soil root zone of their crops and determine irrigation scheduling remotely without time consuming, manual data logging and frequent visits to the field.