Journal of Irrigation and Drainage Engineering

Supplemental irrigation is often required to maintain residential landscapes in Florida, but existing and projected water shortages have led to an increased focus on reducing the amount of publically supplied potable water used for irrigation. Florida-Friendly Landscaping (FFL) has been promoted as a method to reduce irrigation, but the actual water savings has not been previously quantified. Analysis of monthly combined (indoor and outdoor) potable water billing records and parcel data for 125 FFL and 736 traditionally landscaped comparison homes in southwest Florida indicated that FFL homes used 50% less irrigation. Irrigation savings increased to 76% when considering only good examples of FFL and comparison landscapes with high-quality turfgrass. The FFL customers reduced their irrigation use ($279\mathrm{mm}/\mathrm{year}$) after their landscapes became recognized ($202\mathrm{mm}/\mathrm{year}$). Prior to recognition, these customers were already using less irrigation than their neighbors (279 versus $464\mathrm{mm}/\mathrm{year}$, respectively), indicating that those most concerned with water use were more likely to choose Florida-Friendly Landscaping. The results of this study demonstrate the ability of alternative landscapes in a hot, humid climate to conserve potable water used for irrigation; however, mechanisms for irrigation reduction in these landscapes are still unknown.

This paper aims at developing generalized higher-order synaptic neural (GHSN), i.e., generalized quadratic synaptic neural (GQSN) and generalized cubic synaptic neural (GCSN), reference evapotranspiration (${\mathrm{ET}}_o$) models corresponding to various methods. The GHSN models (GHSNs) were developed using pooled climate data of different locations under four agroecological regions (semiarid, arid, subhumid, and humid) in India. The inputs for the development of GHSNs include daily climate data of minimum and maximum air temperatures, minimum and maximum relative humidity, wind speed, solar radiation, and pan evaporation with different combinations, and the target consists of ${\mathrm{ET}}_o$ estimated by one of the methods. Comparisons of developed GHSNs with the generalized first-order neural network, i.e., generalized linear synaptic neural (GLSN) models, were made to test the relative merits of one model over the other. Comparisons were also made between GHSNs and conventional methods. Based on the comparisons, it is concluded that the GHSNs along with GLSN models performed better than their conventional methods. Comparison of GHSNs and GLSN models among themselves reveals that the GQSN followed by GCSN models performed superior to the GLSN for almost all regions. The GHSNs corresponding to one of the methods ranked first and GHSNs corresponding to another method ranked second for all regions. For semiarid and arid regions, the GHSNs corresponding to one of the methods ranked third and for subhumid and humid regions the GHSNs corresponding to another method ranked third. Further, GHSNs were applied to model development and model testing locations to test the generalizing capability. The testing results suggest that the GQSN followed by GLSN models have a better generalizing capability than GCSN for almost all regions.

One of the common methods for estimating actual evapotranspiration (${\mathrm{ET}}_a$) is the two-step approach, which relates crop-specific crop coefficients ($K_c$) to a reference surface ET, typically alfalfa or grass (${\mathrm{ET}}_r$ and ${\mathrm{ET}}_o$, respectively). Minimal, if any, study has reported $K_c$ values for water, nutrient, and both water and nutrient deficiencies. In this study, alfalfa ($K_{cr}$) and grass ($K_{co}$) reference maize (Zea mays L.) $K_c$ values were developed as a function of growing degree days (GDDs) for 0, 84, 140, 196, and $252\mathrm{kg}{\mathrm{ha}}^{-1}$ nitrogen (N) treatments under fully irrigated (FIT), limited irrigation (75% FIT), and rainfed conditions at the University of Nebraska-Lincoln South Central Agricultural Laboratory (SCAL) near Clay Center, Nebraska, for the 2011 and 2012 growing seasons. The research also investigated a stress factor ($K_{\mathrm{stress}}$) to assess the reduction in crop water use as compared with a nonlimiting water and N treatment (reference). In 2011, maximum $K_{cr}$ values ranged from 0.95 to 1.27 and occurred between GDD values of 995 and 1,163°C (late July to early August), which corresponded to the R1 to R3 growth stages, whereas in 2012 (much drier), maximum $K_{cr}$ values ranged from 0.84 to 1.19 for 75% FIT and FIT and existed between GDD values of 1,111 and 1,267°C (R2 to R4 growth stages). On average, greater $K_{cr}$ values existed at higher N rates (e.g., 196 and $252\mathrm{kg}\mathrm{N}{\mathrm{ha}}^{-1}$) compared with lower N rates. Lower N treatments typically reached their maximum $K_{cr}$ value earlier in the growing season and began to decrease towards harvest. Rainfed and 75% FIT experienced a greater reduction in $K_{\mathrm{stress}}$ as compared with FIT as well as lower N rate treatments as compared with higher N treatments. A water stress factor ($K_w$) was calculated to determine the portion of $K_{\mathrm{stress}}$ attributed with water stress alone. The monthly average values often experienced lower $K_{\mathrm{stress}}$ compared with $K_w$ values, indicating that $K_w$ alone was unable to account for the total reduction in $K_{cr}$ from a nonlimiting water and N reference. Thus, an N stress factor ($K_n$) was also quantified by assuming $K_{\mathrm{stress}}$ was the product of water and N stress (e.g., $K_{\mathrm{stress}}=K_w\times K_n$). The seasonal average $K_n$ was 1.15 in 2011 and 0.64 in 2012. Values of $K_n$ were always lower in the drier year in 2012 than in 2011, ranging from 0.45 towards the end of the season in 2012 to a maximum of 1.27 in August 2011. In general, $K_n$ decreased as N rate decreased and $K_n$ had a decreasing trend (e.g., greater N stress) throughout the growing season, especially in the drier year in 2012. The reduction in $K_n$ over time was due to the temporal reduction in readily available N as well as compounding effects of reduced N on plant growth and consequently crop water uptake over the growing season.

Using irrigation and drainage networks will lead to greater productivity and economic benefits. In this study, using a system dynamics approach, a computer model was developed that can estimate the crop yield of an irrigation network according to the quantity and quality of simulated irrigation water and to estimate the net profit of products. In order to calibrate and validate the model results, data collected from the research area of a right Abshar irrigation network was used. After statistical analysis and calculation of the root-mean-square error (RMSE), relative error, standard error, and correlation coefficient, the adjustment between the performance of measured and simulated network products was calculated. The value of these product indexes, according to the conditions on the network, were estimated as $209.98\mathrm{kg}/\mathrm{ha}$, 1.36%, 0.007, and 0.99, respectively. The results showed that the model in the simulated irrigation network, its cropping pattern, and the definition of other scenarios has a reasonable accuracy. But an initial check showed that the groundwater withdrawal rates from the aquifer are much higher than the recharge rate. Considering the importance of water resources, the two scenarios were defined as irrigation according to water requirement of crops and the irrigation based on limitation of exploiting groundwater resources, and the results were analyzed.