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To improve irrigation efficiency, it is important to optimize agriculture irrigation scheduling. The objectives of this study were to evaluate the AquaCrop model for its ability to simulate cotton in the North China Plain and optimize irrigation strategies. The AquaCrop model was calibrated using 2002–2009 data and validated using 2010–2014 data. Root mean square error (RMSE), mean absolute error (MAE) and residual coefficient method (CRM) were used to test the model performance. The model calibrated for simulating cotton yield had a prediction error statistic RMSE of 0.152 t hm−2, MAE of 0.123 t hm−2 and CRM of 0.120. On validation, the RMSE was 0.147 t hm−2, MAE was 0.094 t hm−2 and CRM was 0.092. The goodness-of-fit values for the calibration and validation data sets indicated that the model could be used to simulate cotton yield. The analysis of irrigation scenarios indicated that the highest irrigation water productivity could be obtained by applying one irrigation at the seedling stage in a wet year, two irrigations, at the seedling and squaring stages, in a normal year and three irrigations, at the seedling, squaring and flowering stages, in a dry year. These results could be useful to the government in determining reasonable, well-timed irrigation for agricultural regions.

Proper irrigation management, especially for tomatoes that are sensitive to water, is the key to ensuring sustainable tomato production. Using a low-cost sensor coupled with IoT technology could help to achieve precise control of the moisture content in the plant root-zone soil and apply water on demand with minimum human intervention. An IoT-based precision irrigation system was developed for growing Momotaro tomato seedlings inside a dark chamber. Four irrigation thresholds, 5%, 8%, 12%, and 15%, and two irrigation systems, surface and subsurface drip irrigation, were compared to assess which threshold and irrigation system referred the ideal tomato seedling growth. As a result, the 12% soil moisture threshold applied through the subsurface drip irrigation system significantly (p < 0.05) increased tomato seedling growth in soil composed of a main blend of peat moss, vermiculite, and perlite. Furthermore, in two repeated experiments, a subsurface drip irrigation system with 0.86 distribution uniformity used 10% less water than the surface drip irrigation system. The produced tomato seedlings were transplanted to open fields for further assessment. A low-power wide area networking Long Range Wide Area Network (LoRaWAN) protocol was developed with remote monitoring and controlling capability for irrigation management. Two irrigation systems, including surface and subsurface drip irrigations, were used to compare which system resulted in higher tomato yields. The results showed that the subsurface drip irrigation system with 0.74 distribution uniformity produced 1243 g/plant, while each plant produced 1061 g in the surface drip irrigation system treatment. The results also indicated that the LoRaWAN-based subsurface drip irrigation system was suitable under outdoor conditions with easy operation and robust controlling capability for tomato production.

Evaluating and comparing two different irrigation scheduling methods like soil moisture (SM) and evapotranspiration (ET)-based irrigation scheduling methods were assessed scientifically to maximize tomato yield, especially in areas where restricted irrigation facilities are available. The experimental design was arranged in a Randomized Complete Block Design (RCBD) with 100%, 75%, and 50% water requirements with three replications. The water was applied to furrows using Randomized Block Design (RBC) flume with an inflow rate of 1.62 l/s. The total amount of water applied in SM- and ET-based methods were 229.1 mm and 280 mm, respectively. The collected data were analyzed using ANOVA (statistical software) at 5% significance level. Values on agronomic characteristics, yield attributes, and yield were determined for those two methods. T1-T6 are the total treatments used in this research. Out of which, T1, T2, and T3 are taken as SM-based represented for 100%, 75%, and 50% of requirements and T4, T5, and T6 as ET-based represented for the same percentage of requirements. The results did not illustrate a huge difference in yield potential. The highest net benefit on yield was obtained at T2 (SM-based), whereas the lowest in T6 (ET-based). It was also observed that SM-based method saved around 18.2% of irrigation water compared to ET-based approach. Therefore, to cultivate high water demanding crops such as tomato in water scarce areas, SM-based irrigation scheduling method is comparatively better than ET-based as far as yield is concerned.

Nevertheless, irrigation agriculture is expanding; therefore, evaluating the performance of existing irrigation schemes and introducing improved irrigation scheduling techniques that enhance water productivity (WP) becomes an important strategy. Thus, this study aimed to investigate the effect of furrow irrigation using both gravitational and motorized irrigation water applications on WP for onion and teff crops in the Gumara watershed, northwest Ethiopia. We conducted the field investigation in 2019/20 during the dry season. We utilized CropWat as a decision-making tool to estimate crop evapotranspiration and net irrigation requirement (NIR) and to inform irrigation scheduling. We measured the field irrigation water using pumping tests for motor pump users and a Parshall flume for gravity users. The measurements were taken from a total of 30 onions in Wanzaye and 25 in Jigna, as well as 5 teff crop farmers’ fields in Bebeks. When we examined the model estimates and field records for NIR on onions and teff, we observed that farmers used significantly more water than the crops required. Onions had a seasonal NIR of 360.1 mm, according to the model. However, farmers in Wanzaye, Jigna, and Bebeks used an average of 501.05 mm, 496.8 mm, and 468.7 mm for their applications. The model estimated a NIR of 364.0 mm for teff, while the farmers applied a mean of 459.7 mm of water. Consequently, farmers over-irrigated with water depths of 141, 136.7, and 104.1 mm at Wanzaye, Jigna, and Bebeks, respectively. In Wanzaye and Jigna, farmers charged an extra cost of 3728 birr/ha and 3146 birr/ha for fuel/kerosene, respectively. The onion WP was low in Wanzaye, Bebeks, and Jigna, respectively. The result also revealed that the benefit-cost ratio of the onion was low for Wanzaye, Jigna, and Bebeks, respectively. The WP of teff grain was also found to be low. But the result showed that teff’s benefit-cost ratio was higher than that of onion.

The present study aimed at investigating scientific irrigation scheduling (SIS) for the sustainable production of olive groves. The SIS allows farmers to schedule water rotation in their fields to abate crop water stress and maximize yields, which could be achieved through the precise monitoring of soil moisture. For this purpose, the study used three kinds of soil moisture sensors, including tensiometer sensors, irrometer sensors, and gypsum blocks for precise measurement of the soil moisture. These soil moisture sensors were calibrated by performing experiments in the field and laboratory at Barani Agricultural Research Institute, Chakwal in 2018 and 2019. The calibration curves were obtained by performing gravimetric analysis at 0.3 and 0.6 m depths, thereby equations were developed using regression analysis. The coefficient of determination (R2) at 0.3 and 0.6 m depth for tensiometer, irrometer, and gypsum blocks was found to be equal to 0.98, 0.98; 0.75, 0.89; and 0.82, and 0.95, respectively. After that, a drip irrigation system was installed with the calibrated soil moisture sensors at 0.3 and 0.6 m depth to schedule irrigation for production of olive groves as compared to conventional farmer practice, thereby soil moisture profiles of these sensors were obtained to investigate the SIS. The results showed that the irrometer sensor performed as expected and contributed to the irrigation water savings between 17% and 25% in 2018 and 2019, respectively, by reducing the number of irrigations as compared toother soil moisture sensors and farmer practices. Additionally, olive yield efficiencies of 8% and 9%were observed by the tensiometer in 2018 and 2019, respectively. The outcome of the study suggests that an effective method in providing sustainable production of olive groves and enhancing yield efficiency.

An integrated sensing device for irrigation scheduling was developed to assess the soil–plant–atmosphere continuum for irrigation scheduling. A field experiment was carried out to evaluate ISDI performance and CWSI estimation across various irrigation regimes in wheat crop at WTC farm, ICAR-IARI, New Delhi, India. The experiment considered were full irrigation (FI) and various deficit irrigation levels (DI-15, DI-30, DI-45, and DI-60), receiving 15, 30, 45, and 60% less water in comparison to FI, respectively. The calibration and performance of the ISDI sensor probes was done with gravimetric methods along with time domain reflectometry (TDR) and a handheld infrared thermometer. The field calibration of the ISDI's soil moisture probe and TDR gave promising results, with R2 values ranging from 0.76 to 0.81 and 0.81 to 0.86, respectively, for soil depths up to 45 cm. ISDI's infrared sensor probe also demonstrated strong alignment with a handheld infrared thermometer (R2: 0.95), indicating reliable methods. Furthermore, a regression equation of lower baseline and upper threshold for CWSI computation was derived as (Tc–Ta)ll = 1.97 × VPD – 1.43 (R2:0.86) and 1.93 °C, respectively. It was recommended to initiate irrigation when CWSI ≥ 0.35 for wheat to achieve optimal crop yields.

A field water balance model for efficient irrigation water management of corn was developed using Excel VBA. The model consists of five sub-components or modules, namely, (1) a plant subcomponent, (2) an effective rainfall subcomponent, (3) an evapotranspiration subcomponent, (4) a soil water dynamics subcomponent for the modeling of water flow into and within the soil layers, and (5) an irrigation subcomponent for the estimation of the required amount and timing of irrigation. The model was calibrated and validated using observed data from field experiments and the results showed a reasonably good agreement between the observed and simulated soil moisture values (MAE = 5.76 mm to 12.00 mm, RMSE = 6.83 mm to 13.12 mm, NRMSE = 0.102 to 0.196, and NSE = 0.37 to 0.90). The simulations emphasized that a significant amount of water savings can be achieved when rainfall is properly accounted for in managing water in the field, and that the frequency of rainfall occurrences is as important as the magnitude of rainfall received by the crops. The wide-ranging user-friendliness and simplicity of the model developed in this study can pave the way to eliminating the barriers which cause farmers to resist advancements in their farming practices as the model can easily be used not only by researchers and scientists but also by farmers, especially those with basic knowledge of spreadsheets.

نفذت تجربة عاملية خلال الموسم 2018 في محطة السكران التابعة لدائرة زراعة حديثة، باستعمال تصميم الألواح المنشقة-المنشقة بثلاث مكررات. زرعت بذور أربعة تراكيب وراثية من الكينوا (Regalona و Q-37 و Q-21 و KVL-SR2) في الألواح الثانوية بينما توزعت خمس معاملات للشد المائي مضافا اليها معاملة المقارنة على الألواح الرئيسة: بدون شد و شد مائي في كل من مرحلة البزوغ و التفرعات و الطور العجينيى و النضج على التتابع. يتم الري بعد نفاد50-60% من الماء الجاهز. بلغ متوسط متطلبات الري للتراكيب الوراثية الأربعة بناء على جدولة الري 230.8 مم، انخفضت المتطلبات بمقدار11 و 17% تحت ظروف الشد المائي عند المراحل المتحملة للجفاف. بلغ متوسط الاستهلاك المائي الفعلي 2 و 212 مم انخفض بمقدار 17% تحت ظروف الشد المائي عند المراحل المتحملة للجفاف. تراوح حاصل الحبوب للتراكيب الوراثية الأربعة بين 3.1 و 5.0 لمعاملات الشد المائي مقارنة بـ 5.6 و 4.2 طن هـكتار-1 لمعاملات الري الاعتيادي مع ملاحظة ان التركيب الوراثي Q-21 أعطى أعلى حاصل حبوب. تراوحت كفاءة استعمال الماء الحقلي بين 1.6 و 1.1 كغم م-3 و المحصولي بين 1.38 و 2.22 كغم م3-. تشير هذه النتائج الى ان مرحلتي تكوين العرانيص و التزهير من أكثر مراحل نمو النبات تحملاً للشد المائي (نقص الري) في حين يمكن اعتبار مرحلتي التفرعات و امتلاء الحبة مرحلتين حرجتين يؤدي تعرضهما للشد المائي إلى نقصان شديد في حاصل الحبوب. A factorial experiment was conducted within split-split plot design with three replicates. Four genotypes seeds (Regalona, Q-37, KVL-SR2, and Q21) were planted in the sub plots while six water treatments distributed in the main plots. They were ordinary river irrigation (S0), stress at emergence (S1), branching (S2), at ear formation (S3), at flowering (S4), and at maturity (S5). Irrigation water was applied in the normal irrigation when 50-60% of the available water was depleted and one irrigation was omitted from the water stress treatments. Reference and actual evapotranspiration, pan evaporation, yield, crop coefficient, and water use efficiency were calculated. Mean irrigation requirement for the four genotypes based on irrigation scheduling was 230.8 mm, decreased by 14 and 17% under stress conditions of the drought tolerant stages. Grain yield ranged between 3.1 and 5 Mg ha-1 for water stress treatments compared to 5.6 and 4.2 mg. ha-1 for normal irrigation treatments. Genotype Q21 gave the highest yield and differed significantly from others. Field water use efficiency ranged between 1.6 to 1.1 kg m-3 and crop water use efficiency 1.38 to 2.22 kg m-3. KVL-SR2 and Q21 showed the highest efficiency (1.87 kg m-3). Results indicated that the stage of ear formation and flowering are the most tolerant to water stress. On the other hand, the branching, and maturity were critical stages with high reduction in yield under stress conditions.

Appropriate irrigation scheduling for efficient water use is often a challenge for small-scale farmers using drip irrigation. In a trial with 12 farmers in Sébaco, Nicaragua, two tools to facilitate irrigation scheduling were tested: the Water Chart (a table indicating required irrigation doses) and tensiometers. The study aimed at evaluating if and to what extent simple tools can reduce irrigation water use and improve water productivity in drip-irrigated vegetable (beetroot; Beta vulgaris L.) production compared with the farmers’ usual practice. Irrigation water use was substantially reduced (around 20%) when farmers irrigated according to the tools. However, farmers did not fully adhere to the tool guidance, probably because they feared that their crop would not get sufficient water. Thus they still over-irrigated their crop: between 38% and 88% more water than recommended was used during the treatment period, resulting in 91% to 139% higher water use than required over the entire growing cycle. Water productivity of beetroot production was, therefore, much lower (around 3 kg/m3) than what can be achieved under comparable conditions, although yields were decent. Differences in crop yield and water productivity among treatments were not significant. The simplified Water Chart was not sufficiently understandable to farmers (and technicians), whereas tensiometers were better perceived, although they do not provide any indication on how much water to apply. We conclude that innovations such as drip irrigation or improved irrigation scheduling have to be appropriately introduced, e.g., by taking sufficient time to co-produce a common understanding about the technologies and their possible usefulness, and by ensuring adequate follow-up support.

Urban stormwater runoff could have negative impacts on water resources and the environment. Rainwater Harvesting (RWH) can serve both as a stormwater control and water conservation measure. Cistern size and irrigation scheduling are two of the factors that directly impact the total runoff from a residential unit with a RWH system and the amount of potable water used for irrigation. The effectiveness of RWH was evaluated for four soil types; Sand, Sandy Loam, Loamy Sand, and Silty Clay, with a root zone of 15.2 cm using three irrigation scheduling methods (Evapotranspiration (ET)-based, soil moisture-based, and time-based), and five cistern sizes. Total runoff volumes and total supplemental potable water used were compared among the three irrigation scheduling systems and a control treatment without RWH. A model was developed to simulate the daily water balance for the treatments. Irrigation and runoff volumes were compared for the various scenarios. Silty clay soil resulted with 83 % more runoff than Sandy soil, while Sandy soil required on average 58 % more supplemental water than Silty Clay soil. On average, the 833 L cistern resulted with 41 % savings in water supply and 45 % reduction in total runoff. Results showed that the greatest volumes of runoff predicted were for the silty clay soil Control Treatment using a time-based irrigation scheduling method, while the least volumes calculated were for the sandy loam soil time-based irrigation scheduling treatment with 833 L cistern size. The greatest volumes of total supplemental water predicted were for sandy loam soil Control Treatment, while the least volumes were for silty clay soil ET-based irrigation scheduling treatment with 833 L cistern size. Regression equations were developed to allow for users to select a RWH cistern size based on the amount of water they want to save or runoff to reduce.