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HIGHLIGHTS Captured rainwater supplied nearly all the irrigation required for high tunnels in Tennessee. Solar pumping and/or gravity flow adequately supplied the pressure required for irrigation in high tunnels. Rainwater harvesting costs need to be reduced in order to be more competitive with alternate water sources. Abstract . High tunnels use clear plastic film over a large metal frame to trap solar radiation as heat in order to extend the crop growing season and reduce environmental stress. High tunnels differ from high tech greenhouses in that they use the natural soil as the growing media and use natural ventilation without fans or heaters to control the growing environment. High tunnels can provide some of the benefits of greenhouses at a much lower cost. However, the protective cover cuts-off rain water to the crop and significantly modifies the crop-water use environment. In order to reduce reliance on external sources of water, The University of Tennessee–Biosystems Engineering and Plant Science Departments implemented three types of rainwater harvesting (RWH) for high tunnels that did not require an external source of power for irrigation: gravity flow, solar battery-powered pumping, and solar transfer pumping. RWH by gravity-flow stored water captured off the high tunnels at a high enough elevation to deliver water for irrigation at very low pressure while solar battery-powered pumping delivered pressurized water directly to the irrigation system. Solar transfer pumping moved harvested rainwater to a higher elevation tank that used gravity flow to irrigate at intermediate pressures. These RWH systems were designed to store 64 mm of rainfall from the high tunnel cover (6400 L per 100 m2 of footprint area) and were able to provide 75% to 100% of the spring crop and 90% to 100% of the fall crop irrigation based on 16 experiments over six years. The RWH systems were ranked in order of increasing cost, maintenance, and complexity as follows: 1) gravity flow, 2) solar transfer pumping, and 3) solar battery-powered pumping. However, all RWH systems had high initial cost when compared to well and municipal water supplies, $12,750 to $15,950 for two 9.2- × 29-m high tunnels. Significantly lower cost rain gutters and water storage were identified but not yet tested for RWH in high tunnels. Keywords: Drip irrigation, Evapotranspiration, Gravity irrigation, Greenhouses, High tunnels, Microirrigation, Rainwater harvesting, Solar pumping.

Direct measurement of unsaturated hydraulic parameters is costly and time-consuming. Pedotransfer functions (PTFs) are typically developed to estimate soil hydraulic properties from readily available soil attributes. For the first time, in this study, we developed PTFs to estimate the soil hydraulic conductivity (log(K)) directly from measured data. We adopted the pseudo continuous neural network PTF (PCNN-PTF) approach and assessed its accuracy and reliability using two independent data sets with hydraulic conductivity measured via the evaporation method. The primary data set contained 150 international soils (6963 measured data pairs), and the second dataset consisted of 79 repacked Turkish soil samples (1340 measured data pairs). Four models with different combinations of the input attributes, including soil texture (sand, silt, clay), bulk density (BD), and organic matter content (SOM), were developed. The best performing international (root mean square error, RMSE = 0.520) and local (RMSE = 0.317) PTFs only had soil texture information as inputs when developed and tested using the same data set to estimate log(K). However, adding BD and SOM as input parameters increased the reliability of the international PCNN-PTFs when the Turkish data set was used as the test data set. We observed an overall improvement in the performance of PTFs with the increasing number of data points per soil textural class. The PCNN-PTFs consistently performed high across tension ranges when developed and tested using the international data set. Incorporating the Turkish data set into PTF development substantially improved the accuracy of the PTFs (on average close to 60% reduction in RMSE). Consequently, we recommend integrating local HYPROPTM (Hydraulic Property Analyzer, Meter Group Inc., USA) data sets into the international data set used in this study and retraining the PCNN-PTFs to enhance their performance for that specific region.

Direct measurements of soil hydraulic properties are time-consuming, challenging, and often expensive. Therefore, their indirect estimation via pedotransfer functions (PTFs) based on easily collected properties like soil texture, bulk density, and organic matter content is desirable. This study was carried out to assess the accuracy of the pseudo continuous neural network PTF (PCNN-PTF) approach for estimating the soil water retention curve of 153 international soils (a total of 12,654 measured water retention pairs) measured via the evaporation method. In addition, an independent data set from Turkey (79 soil samples with 7729 measured data pairs) was used to evaluate the reliability of the PCNN-PTF. The best PCNN-PTF showed high accuracy (root mean square error (RMSE) = 0.043 cm3 cm&minus, 3) and reliability (RMSE = 0.061 cm3 cm&minus, 3). When Turkish soil samples were incorporated into the training data set, the performance of the PCNN-PTF was enhanced by 33%. Therefore, to further improve the performance of the PCNN-PTF for new regions, we recommend the incorporation of local soils, when available, into the international data sets and developing new sets of PCNN-PTFs.

Thermal energy conservation and heat management of the crop growth environment inside high tunnels (HTs) can provide high production efficiency and better yield with low operation cost, hence extending the growing seasons and increasing the sustainability of organic farming. This study assessed the beneficial effects of independent and combined practices on temperature changes inside the HT systems located in eastern Tennessee and further examined the impact of temperature changes on crop production. Specific practices included mulching (polyethylene and biodegradable plastic films and vegetative), row covers, cover crops, and irrigation with collected rainwater or city water. Sweet pepper (Socrates) was grown in the spring seasons of 2011, 2013 and 2015, and statistical differences were analyzed in a randomized complete block design (RCBD) for total marketable yield regarding pepper weight and fruit number per plant, as well as the changes in soil and air temperature. The employment of black polyethylene mulch produced the highest pepper yield by warming the soil, but not excessively, during the day when compared with clear polyethylene mulch. While the black biodegradable mulch did not generate as much soil warming as black polyethylene, its total marketable yield was statistically similar to that for the latter mulch treatment. Also, row covers continuously provided protection from freezing during the cold nights; but, black polyethylene can act alone as an insulator of soil, evidenced by soil temperature and crop yield results. Cover crops allowed for greater heat transfer to soil and increased soil temperatures, but lead to reduced overall yield. Rainwater irrigation not only enhanced water conservation, but also it warmed the soil and tended to increase the overall pepper production, because rainwater was warmer than municipal water except when the pepper were first transplanted in the cold early spring period.

Multi-reservoir systems exploitation is known as one of the most important problems in optimizing water resources. The present study aims to improve the differential evolution (DE) algorithm, promoting the contraction performance and reflection operators, by merging in the shuffled complex evolutionary (SCE). In order to use the SCE algorithm for complex water resource problems, SCE-DE algorithm was developed as well. The performed algorithm was evaluated by solving a difficult mathematical function problem using SCE and SCE-DE methods as a benchmark. The results illustrate that SCE-DE method represents a higher speed in converging to the optimum response and is closer to the absolute optimum. Moreover, the mentioned algorithms were performed for solving the optimization problems of multi-reservoir systems, and the obtained results were compared with the LP absolute response. The obtained data represent a difference of 0.009% between the response of objective function in the SCE-DE method and the LP response. However, the response of objective function of SCE differs 1.84% from that of the LP. Comparing the present observations and the other studies, the proposed algorithm has revealed a much closer optimum response to the LP response. Therefore, we recommend SCE-DE for solving complex problem of water resources.

A high-resolution soil water retention data set (81 repacked soil samples with 7729 observations) measured by the HYPROP system was used to develop and evaluate the performance of regression parametric pedotransfer functions (PTFs). A total of sixteen soil hydraulic models were evaluated including five unimodal water retention expressions of Brooks and Corey (BC model), Fredlund and Xing (FX model), Kosugi (K model), van Genuchten with four free parameters (VG model) and van Genuchten with five free parameters (VGm model). In addition, eleven bimodal, Peters&ndash, Durner&ndash, Iden (PDI) and bimodal-PDI variants of the original expressions were studied. Six modeling scenarios (S1 to S6) were examined with different combinations of the following input predictors: soil texture (percentages of sand, silt and clay), soil bulk density, organic matter content, percent of stable aggregates and saturated water content (&theta, s). Although a majority of the model parameters showed low correlations with basic soil properties, most of the parametric PTFs provided reasonable water content estimations. The VGm parametric PTF with an RMSE of 0.034 cm3 cm&minus, 3 was the best PTF when all input predictors were considered. When averaged across modeling scenarios, the PDI variant of the K model with an RMSE of 0.045 cm3 cm&minus, 3 showed the highest performance. The best performance of all models occurred at S6 when &theta, s was considered as an additional input predictor. The second-best performance for 11 out of the 16 models belonged to S1 with soil textural components as the only inputs. Our results do not recommend the development of parametric PTFs using bimodal variants because of their poor performance, which is attributed to their high number of free parameters.

HYPROP (Hydraulic Property Analyzer) system works based on the simplified evaporation method, which determines soil hydraulic properties in the laboratory. This system simultaneously monitors the changes in weight of soil samples as well as the soil matric potential at two depths throughout a drying process governed by evaporation from the soil surface. In this study, we examined the performance of the pseudo continuous pedotranfer function (PC-PTF) to estimate the soil water retention curve (SWRC) using high resolution data measured by HYPROP system. The dataset consisted of 7963 measured water retention data points obtained from 81 Turkish soil samples from which 60%, 20% and 20% were randomly selected for training, cross-validation and test subsets, respectively. The best PC-PTF developed in this study with a mean absolute error of 0.023 m3 m−3 (a root mean square error of 0.033 m3 m−3) and a correlation coefficient of 0.96 showed promising performance considering the typical performance range (RMSE = 0.034–0.085 m3 m−3) for parametric PTFs estimating SWRC. The best PC-PTF used soil textural information, soil bulk density, the percentage of stable aggregates, soil organic matter content and the initial water content as the input attributes. PTFs developed in this study also ranked high among previously developed PC-PTFs (with RMSE ranging from 0.027 to 0.056 m3 m−3) using sparse datasets collected via the traditional equilibrium approach (i.e. sandbox apparatus /pressure plates extractor). We, therefore, recommend further application of the PC-PTF approach for development of PTFs using high resolution data obtained by HYPROP system.

Considering ground water pumping restrictions and unpredictable amount of water available for irrigation from year to year, Nebraska Panhandle producers are facing a challenge to reduce their irrigation water usage and practice deficit irrigation. Among irrigated crops in the region, dry bean (a major cash crop and critical to crop rotation systems) has relatively low water use and is capable to withstand periods of stress. Consequently, two experiments within six consecutive growing seasons (2010–2015) were conducted to determine the impacts of multiple irrigation scenarios (full irrigation, deficit irrigation, and rainfed) and two soil surface conditions (bare soil versus crop residue) on dry bean production, irrigation water use efficiency, and temporal soil water dynamic within the crop root zone. Dry bean yield ranged from 0.41 to 4.07 Mg ha−1 during the six years of the study (2010–2015). The results (2012–2015) indicated that reducing irrigation water by 25% on average increased irrigation water use efficiency (IWUE) by 26% and only caused 6% yield reduction in relative to the full irrigation treatment scenario. However, applying only 50% crop evapotranspiration requirement (ETc) resulted in significant yield reduction (30% reduction on average) in 5 out of 6 years compared to the full irrigation treatment (p

Currently, sugarbeet producers in western Nebraska are facing the challenge of reducing their irrigation water usage due to ground water pumping restrictions and the unpredictable amount of rainfall that is available from year to year. Therefore, there is an increasing interest in developing season long deficit irrigation strategies for sugarbeet, the main objective of this study. The other objectives were to determine the impact of surface residue and plant population on sugarbeet production. Three field trials were conducted in eight consecutive cropping seasons (2008–2015). The average root and sugar yield for the full irrigation treatment were equal to 71.11 and 11.08 Mg ha−1, respectively during the 6 years of the two irrigation studies (2008–2013) while applying 75% and 50% ETc on average caused 9% and 11% yield reduction, respectively (2008–2011). Plots under full irrigation treatment and rainfed showed highest and lowest water depletion, respectively. Overall based on six years of data (2008–2013), stressing moderate and severe late in growing season produced 1.03 and 1.87 Mg ha−1 more sugar while consuming about 13 mm less water than imposing same level of stress early in season. The residue covered plots produced 0.29 Mg ha−1 more sugar yield than bare soil plots on average across treatments and years. Overall, higher population rates resulted in higher sugarbeet yield. Findings of this research will deliver insight for sugarbeet irrigation management.

A three-year (2017–2019) irrigation research trial was conducted to evaluate the response of hybrid bermudagrass to a wide range of irrigation scenarios and assess the efficacy of Weathermatic Evapotranspiration-based (ET-based) smart controller for autonomous landscape irrigation management during dry seasons in inland southern California. The irrigation levels applied throughout the experiment ranged between 39% and 103% reference ET (ETo) and the irrigation frequency restrictions imposed were 3, 5 and 7 d/wk. Normalized difference vegetation index (NDVI) data were continuously collected to evaluate the response of hybrid bermudagrass [‘Tifgreen’ Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt‐Davy] to irrigation treatments. Plots were also visually assessed and scaled from 1 (dead plot) to 9 (ideal turfgrass) following the National Turfgrass Evaluation Program (NTEP) standards. Turfgrass water response function (TWRF) was introduced as a statistical regression model to estimate hybrid bermudagrass quality response (NDVI values) to irrigation levels over time. In the years 2018 (p

Maize (Zea mays L.) evapotranspiration crop coefficients (Kc) that are needed to estimate crop evapotranspiration (ETc) using the two-step approach for variable rate irrigation and nitrogen management under different soil types have not been investigated or quantified. In this research, alfalfa- and grass-reference crop coefficients (Kcr and Kco) curves were developed for fixed rate or uniform rate fertigation (FRF), variable rate fertigation (VRF) and pre-plant nitrogen (PP) management under fixed rate or uniform rate irrigation (FRI) and variable rate irrigation (VRI) for three soil types [Crete silt loam (S1), Hastings silty clay loam (2) and Hastings silt loam (S3)] in 2015, 2016 and 2017 growing seasons. Irrigation and nitrogen management strategies, as well as soil type, all influenced the Kcr and Kco values, which exhibited inter-annual variation. On average, greater variation in Kc curves between FRF, VRF and PP nitrogen treatment were observed under VRI treatments as compared with FRI. Results showed that Kc values are more dependent on the amount rather than the timing of the nitrogen application. In all three seasons, higher Kc values were observed in the FRI treatment than VRI with Kcr, ranging from 0.07 to 1.30 in FRI and 0.07 to 1.20 in VRI. Kc curves also differed between nitrogen treatments and the difference was more prominent in the VRI treatments than in the FRI in all years. In general, maximum Kc was observed in PP nitrogen treatment, followed by FRF and VRF. On a monthly average basis, maximum Kc values were observed in July and August in all soil types and minimum Kc values were observed in June. When soil types are considered, overall, the maximum Kcr value was observed in FRI-PP treatment in S1 (1.02), FRI-VRF treatment in S2 (1.06) and FRI-VRF treatment in S3 (1.02). The Kcr and Kco equations as a function of growing degree days were developed and monthly average Kcr and Kco values were tabulated for practical applications. To the best of the authors’ knowledge, this research is the first that investigated and quantified the impact of VRI and VRF strategies under FRF, VRF and PP fertilizer management strategies on maize Kc values. The Kcr and Kco values quantified in this research can aid irrigators, state agencies and other water management and agricultural professionals for more accurate crop water use determinations under different irrigation and nitrogen management strategies and different soil types.

This study focuses on the reliable parametrization of the full Soil Water Retention Curve (SWRC) from saturation to oven-dryness using high resolution but limited range measured water retention data by the Hydraulic Property Analyzer (HYPROP) system. We studied the performance of five unimodal water retention models including the Brooks and Corey model (BC model), the Fredlund and Xing model (FX model), the Kosugi model (K model), the van Genuchten constrained model with four free parameters (VG model), and the van Genuchten unconstrained model with five free parameters (VGm model). In addition, eleven alternative expressions including Peters&ndash, Durner&ndash, Iden (PDI), bimodal, and bimodal-PDI variants of the original models were evaluated. We used a data set consisting of 94 soil samples from Turkey and the United States with high-resolution measured data (a total of 9264 measured water retention data pairs) mainly via the HYPROP system and supplemented for some samples with measured dry-end data using the WP4C instrument. Among unimodal expressions, the FX and the K models with the Mean Absolute Error (MAE) values equal to 0.005 cm3 cm&minus, 3 and 0.015 cm3 cm&minus, 3 have the highest and the lowest accuracy, respectively. Overall, the alternative variants provided a better fit than the unimodal expressions. The unimodal models, except for the FX model, fail to provide reliable dry-end estimations using HYPROP data (average MAE: 0.041 cm3 cm&minus, 3, average r: 0.52). Our results suggested that only models that account for the zero water content at the oven dryness and properly shift from the middle range to dry-end (i.e., the FX model and PDI variants) can adequately represent the full SWRC using typical data obtained via the HYPROP system.

Summary A detailed understanding of soil hydraulic properties, particularly soil available water content (AWC) within the effective root zone, is needed to optimally schedule irrigation in fields with substantial spatial heterogeneity. However, it is difficult and time consuming to directly measure soil hydraulic properties. Therefore, easily collected and measured soil properties, such as soil texture and/or bulk density, that are well correlated with hydraulic properties are used as proxies to develop pedotransfer functions (PTF). In this study, multiple modeling scenarios were developed and evaluated to indirectly predict high resolution AWC maps within the effective root zone. The modeling techniques included kriging, co-kriging, regression kriging, artificial neural networks (NN) and geographically weighted regression (GWR). The efficiency of soil apparent electrical conductivity (ECa) as proximal data in the modeling process was assessed. There was a good agreement (root mean square error (RMSE) = 0.052 cm3 cm−3 and r = 0.88) between observed and point prediction of water contents using pseudo continuous PTFs. We found that both GWR (mean RMSE = 0.062 cm3 cm−3) and regression kriging (mean RMSE = 0.063 cm3 cm−3) produced the best water content maps with these accuracies improved up to 19% when ECa was used as an ancillary soil attribute in the interpolation process. The maps indicated fourfold differences in AWC between coarse- and fine-textured soils across the study site. This provided a template for future investigations for evaluating the efficiency of variable rate irrigation management scenarios in accounting for the spatial heterogeneity of soil hydraulic attributes.

Knowing the soil water retention curve (WRC) is essential for analyzing soil hydraulic behavior within the vadose zone. The van Genuchten (VG) soil hydraulic equation is one of the most frequently adopted models to parameterize the WRC. Some measured water retention points are needed to fit the VG model, but direct measurement of water content versus matric potential is expensive and time consuming. A pedotransfer function (PTF) enables indirect determination of a WRC from basic soil information. The typical method employed to derive a PTF using the VG model (VG-PTF) is to establish a mathematical relationship between the parameters of the VG model and basic soil data. However, both establishing and reusing a VG-PTF for new soils are challenging due to several reasons, such as over-parameterization, low correlation between basic soil data and VG parameters, and interdependency among parameters. In this study, a nonparametric approach based on the k nearest neighbor technique was designed and tested to establish a VG-PTF. A subset of soils from the UNSODA database (n = 554)) and a data set from Belgium (n = 69) were used as the model development and validation data sets, respectively. The proposed PTF showed reasonable accuracy and reliability and was comparable to well-known parametric VG-PTFs available in the literature.

The pedotransfer function (PTF) concept has been widely used in recent years as an indirect way to predict soil hydraulic properties, particularly the water retention curve (WRC). The pseudo continuous (PC) approach allows us to predict water content at any predefined matric head, resulting in an almost continuous WRC. When combined with powerful pattern recognition approaches, a PC-PTF can be trained to learn the shape of WRC from a discrete set of measured points, unlike traditional parametric PTFs which follow a predefined WRC shape dictated by the selected soil hydraulic equations. The purpose of this study was to investigate the impact of two elements on the performance of a PC-PTF: (i) data mining method (neural network, NN, versus support vector machine, SVM) and (ii) distribution and density of the provided water retention data in the training phase. Two datasets from Turkey and Belgium, consisting of mainly fine and coarse-textured soils, respectively, were employed. Multiple scenarios containing different combinations of measured water retention points in the training phase were defined. The lower root mean square error (RMSE) on average (0.044 cm3 cm− 3) attained with the NN-based PTF shows that it is a better option than SVM (RMSE of 0.052 cm3 cm− 3) for deriving PC-PTFs. The accuracy of PC-PTF was firmly dependent on the presence of measured water retention points in the entire range of WRC. Applying different scenarios revealed that a well distributed set of measured water retention points in the training phase could result in up to 0.03 cm3 cm− 3 reduction in RMSE values.

Production functions (PFs) are practical tools for not only irrigation scheduling but also in economic analysis as a mathematical relationship between relative grain yield and factors like evapotranspiration, irrigation water and salinity. This study was carried out in the Mashhad region of Iran during cropping years 2010 and 2011 to evaluate the performances of two data mining methods, decision tree and neural network, for deriving PFs of spring wheat under simultaneous drought and salinity stress compared with four well known regression-based PFs. The four well known PFs were: Jensen-PF (Jensen, 1968), Minhas-PF (Minhas et al., 1974), modified Stewart-PF (Stewart et al., 1977; Stegman et al., 1980), and Nairizi-PF (Nairizi and Rydzewski, 1977). Heading and flowering were the most sensitive growth stages followed by the stem elongation and booting. Salinity stress also affected grain yield and therefore was an important parameter for deriving PFs. In general, all the PFs were in agreement concerning the sensitivity of spring wheat to water stress. The neural network-based PF performed the best with a root mean square error equal to 44.27gm^-^2 while the decision tree-based PF ranked fourth out of six in terms of accuracy. The most important advantage of the neural network-based PF was the flexible number of input parameters.

Summary In this study, a new approach, which we called pseudo-continuous, to develop pedotransfer functions (PTFs) for predicting soil–water retention with an artificial neural network (ANN) was introduced and tested. It was compared with ANN PTFs developed using traditional point and parametric approaches. The pseudo-continuous approach has a continuous performance, i.e. it enables to predict water content at any desirable matric potential, but without the need to use a specific equation, such as the one by van Genuchten. Matric potential is considered as an input parameter, which enables to increase the number of samples in the training dataset with a factor equal to the number of matric potentials used to determine the water retention curve of the soil samples in the dataset. Generally, the pseudo-continuous functions performed slightly better than the point and parametric functions. The root mean square error (RMSE) of the pseudo-continuous functions when considering local data for training and testing, and with both bulk density and organic matter as extra input variables on top of sand, silt and clay content, was 0.027 m 3 m −3 compared to 0.029 m 3 m −3 for both the point and parametric PTF. The increased number of samples in the training phase and the selection of matric potential as an input variable enabling to predict water content at any desired matric potential are the most important reasons why pseudo-continuous functions would need more intention in the future. Uniformity in the training and test dataset was shown to be important in deriving PTFs. We finally recommend the use of pseudo-continuous PTFs for further improvement and development of PTFs, in particular when datasets are limited.