Artificial Neural Network Modeling for Investigation on the Effect of Deficit Irrigation and Nitrogen Levels on Hay Yield and Quality of Sorghum Sudangrass Hybrid

Artificial Neural Network Modeling for Investigation on the Effect of Deficit Irrigation and Nitrogen Levels on Hay Yield and Quality of Sorghum Sudangrass Hybrid | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Artificial Neural Network Modeling for Investigation on the Effect of Deficit Irrigation and Nitrogen Levels on Hay Yield and Quality of Sorghum Sudangrass Hybrid Murat KARAER, Erdem GÜLÜMSER, Yusuf Murat KARDEŞ, Hüseyin Tevfik GÜLTAŞ, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3487011/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Irrigation and fertilization are the most critical inputs to increase productivity in plant production. However, the unconscious use of water resources in agriculture and excessive fertilization create an obstacle to the sustainability of production. Deficit irrigation is an essential and sustainable production strategy applied in regions with limited water resources. Since the experiments need more labour, time, input preparation, and land, it is hard to handle the far-reaching parameters. In this regard, artificial neural networks (ANNs) can be an alternative solution. This study aims to develop an ANN modeling to be trained to forecast the effects of different irrigation water levels and fertilizer doses on the hay yield and some quality traits of herbal parts of Sorghum × Sudan grass (SS) hybrid ( Sorghum sudanense vs. Sorghum bicolor ) remaining after seed harvesting. The ANN model was developed on the limited field experiments implemented in Bilecik, Turkey, for two years in 2021 and 2022. Experiments were conducted in randomized blocks-split plots design with three replications, three irrigation levels (I100, I60, and I30), and four nitrogen treatments (N0, N50, N100, and N150 kg ha − 1 ). The hay yield, protein yield, relative feed value (RFV), water use efficiency (WUE), and irrigation water use efficiency (IWUE) were determined in this study. It was determined that the ANN structure, including one hidden layer with five neurons, could successfully be used to obtain the best practice conditions for the handled agricultural activity. According to the field experiments and ANN model, the I80 irrigation with 100 kg ha − 1 nitrogen doses would suit the feed yield and quality of Sorghum × Sudan grass hybrid. Artificial neural network deficit irrigation fertilization hay yield protein quality roughage Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Due to the increasing world population and climate change, our agricultural areas and natural resources are decreasing day by day. In particular, the pressure on water resources has been felt more in recent years. When we look at the sectoral utilization of the existing water assets in the world, the agricultural sector is the largest user of water, with approximately 70%. Therefore, it has become a global necessity to use resources efficiently in the agricultural sector. For this reason, in recent years, serious measures have been taken to ensure effective use of water and water saving in agriculture sector. Deficit irrigation technique is one of the methods that reduces the amount of water given to plants and saves water compared to full irrigation. Deficit irrigation is an essential and sustainable production strategy applied in regions with limited water resources (Geerts and Raes, 2009 ). The aim is to increase plant water use efficiency by reducing the amount of irrigation water or the number of irrigations. The plant to be irrigated with limited irrigation is exposed to water stress at specific rates during any period of the development period or throughout the season, and it is expected to save irrigation water without causing a significant decrease in yield (Kırda, 2002 ) Fertilization, along with irrigation, is one of the most critical inputs to increase productivity in plant production. While the use of an appropriate amount of fertilizer increases the yield, when it is used excessively, some of the fertilizers are washed from the soil or become unfixable in the soil, thus reducing the effectiveness of the fertilizer. Therefore, a planning should be made before applying fertilizer to the soil. There are 16 elements that are essential in plant nutrition. To effectively use these 16 important components in plant nutrition, the soil must be given the exact dosage that each of these elements needs to grow. Because the use of too much fertilizer will not increase the yield and may also cause soil pollution. That's why it's so important to avoid over-fertilizing the soil and instead provide the plant with the proper amount of fertilizer. Sorghum-sudangrass ( SS ) hybrids are a cross between Sorghum bicolor (L.) Moench and Sorghum Sudanese (Piper) Stapf and have wide adaptability. It is a plant with a sugar-rich stem and a high biomass yield. Due to its lower water and fertilizer needs, it is very suitable for cultivation in marginal areas. SS hybrids have shown a great increase in animal nutrition, especially in recent years, due to their features such as ease of cultivation, rapid settlement, growth and rich nutrient content. For an agricultural activity's field experiments to produce findings that are acceptable, additional time, work, land, and money are required. An ANN model sensitively measures the degree of influence since it can successfully yield agreeable predictions with high scores (Arslan, 2011 ; Arslan, 2014 ). In this study, artificial neural network (ANN) modeling was first developed and used to forecast the effects of different irrigation water levels and fertilizer doses on the hay yield and some quality traits of the herbal parts of the SS hybrid remaining after seed harvesting. Thus, more precise results can be obtained with fewer field experiments. 2. Material and Methods Field experiments were implemented in the Application and Research Station (40° 6` N, 30°.0` E) at the University of Bilecik Seyh Edebali, in Bilecik, Turkey. The studies were carried out in the 2021 and 2022 growing seasons. Based on long-year meteorological data, the average temperature in Bilecik is 12.5°C, and the annual average rainfall is 459.3 mm. For the 2021 and 2022 tomato growing seasons, when the study was conducted, the total rainfall was 94.7 mm and 116.3 mm, respectively. Soil analysis was done before the study started and some physical and chemical soil properties were determined. According to the results, the land will have a loamy soil structure and other soil properties are given in Table 1 . Table 1 Some properties of the experiment area soil Depth (cm) Texture Field capacity PW(%) Volume weight (g/cm 3 ) PH Organic Matter Phosphorus P 2 O 5 (kg/da) Potassium K 2 O (kg/da) 0–30 CL 27.87 1.26 7.77 1.18 26.74 116.28 30–60 L 24.57 1.21 7.81 1.24 27.45 91.59 60–90 L 26.67 1.27 7.71 2.07 21.02 96.42 2.1. Experimental Design and Treatments In the study, Aneto sorghum x sudangrass hybrid (SS) was used as crop material. The Aneto variety is drought resistant, rapid growing and of high silage quality. The experiments were carried out using a three-replication randomized block-split plot design. The plot measures 6 x 2.8 m (16.8 m2), with a plant spacing of 5 cm on each row and a row spacing of 70 cm overall. There are four rows each parcel. The study was designed with three irrigation subjects (100% (I100), 60% (I60), and 30% (I30) of the evaporation measured in the Class A Pan) for the main plots and four nitrogen doses (0 kg ha − 1 (N0), 50 kg ha − 1(N50), 100 kg ha − 1 (N100), 150 kg ha − 1 (N150 )) for the subplots. All plots applied 80 kg of P2O5 of DAP base fertilizer per hectare during planting. Considering the nitrogen we gave with the base fertilizer while fertilizing applications, the missing amounts were completed to 50, 100, and 150 kg per ha and given to other parcels. Irrigation was done in the form of supplementary irrigation during the three critical periods determined for the plant. The first irrigation was done when the plants reached 30–35 cm in height, the second at the beginning of flowering, and the third when the panicle emerged. Irrigation is calculated as in Eq. 1 according to the amount of cumulative evaporation occurring in the class A pan during the specified critical periods (Kanber,1984): $$I=A\times kcp\times Ep\times P$$ 1 where I is the irrigation amount (mm), A is the parcel area (m 2 ), kcp plant-pan coefficient (0,0.30, 0.60, 1.00) E p is the cumulative evaporation amount from class A-pan (mm), P is the percentage of vegetation. The soil water content was measured gravimetrically at 0.3 m depth to 1.2 m throughout the growing season. Crop evapotranspiration is calculated using a water balance equation (Shen et al., 2019 ): $$ETa=I+P+W\pm \varDelta S-D-R$$ 2 where I is the irrigation amount (mm), P is the seasonal amount of precipitation (mm), W is the groundwater flow into the root zone (mm), ΔS is the change in soil moisture content (mm), D deep drainage losses (mm) and R are the surface runoff. In the equation, the P -value is taken from the Bilecik State Meteorology Station. W was ignored as the groundwater level was 15m below the ground surface, which was not deep enough to affect the growth of Sorghum Sudan grass. In the drip irrigation method, capillary rise (R) is considered zero since there is no surface flow or groundwater in the area. Since no irrigation is subject to exceeding the field capacity, the deep infiltration (D) value was also taken as zero (Hanks,1974). 2.2. Water Productivity Water use efficiency and irrigation water use efficiency values are the most important variables to consider when analyzing correlations between plant-water yields. The yield values acquired per unit of water applied to the plant are shown by irrigation water use efficiency, and the yield values obtained versus seasonal plant water consumption are shown by water use efficiency. These values are calculated from the equations determined by Howell et al. ( 1990 ): $$IWUE:\frac{Y}{I}$$ 3 $$WUE:\frac{Y}{ETa}$$ 4 where IWUE is the irrigation water use efficiency (kg m − 3 ), Y is the yield (kg da − 1 ), I is the volume of seasonal irrigation water applied (m 3 da − 1 ), WUE is the water use efficiency (kg m − 3 ), and ETa is the actual seasonal evapotranspiration (m 3 da − 1 ). 2.3. Hay yield and quality analysis The plants were harvested at the seed maturation stage, the seeds were separated, and the remaining plant parts were used in the measurements and analyses. The hay yield was determined after drying the samples at 65°C until constant weight. Crude protein ratio, acid detergent fiber (ADF) and neutral detergent fiber (NDF) were determined by using Near Reflectance Spectroscopy. Relative feed value (RFV) is estimated according to the following equations adapted from Rohweder et al. ( 1978 ): $$DDM=88.9–(0.779\bullet ADF)$$ 5 $$DMI=\frac{120 }{NDF }$$ 6 $$TDN=96.35–ADF\bullet 1.15$$ 7 $$RFV=\frac{DDM\bullet DMI}{1.29}$$ 8 where DDM is the digestibility of dry matter (%), DMI is dry matter intake (%), TDN is the total digestible nutrient (%), and RFV is the relative feed value (%). 2.4. Statistical analysis of the field experiments Yield and other determined quality parameters were subjected to analysis of variance (ANOVA) using Minitab 19 software. The significance of irrigation and nitrogen doses was determined using the F test. When the F-test was significant, the Tukey test (P < 0.05) was used to compare the group means of irrigation and nitrogen dose treatments and their interactions. 2.5. ANN modelling In the study, the feed-forward back-propagation learning algorithm, which is most widely used, was handled in the multi-layer neural network (MLNN) architecture, as well as the Levenberg-Marguardt (LM) algorithm, which was selected as the training algorithm since it has successful scores (Tugcu and Arslan, 2017 ). All the training and testing values were normalized in the range of 0.3 and 0.7 to scale all parameters to the equivalent degree for faster and more sensitive results (Boukelia et al., 2016 ). The non-linear transfer function, namely the logarithmic sigmoid (logsig), was used to forecast the complex relations for the related activity (Arat and Arslan, 2017 ). The logsig function for the inputs is given as: $$f\left(\text{n}\right)=\frac{1}{1+{e}^{-\text{n}}}$$ 9 where n is the weighted sum given by: $${\text{n}}_{\text{j}}=\sum _{\text{i}=1}^{\text{k}}{\text{w}}_{\text{i},\text{j}}{\text{y}}_{\text{i}}+{\text{b}}_{\text{j}}$$ 10 Here, w is the weight, y is the output value, and b is the bias. Seventy-five percent of the experiments were randomly selected for the training of the ANN model; the remaining part was used in the testing stage. The accuracy of the network was separately measured for the training and testing stages by the coefficient of multiple determinations ( R 2 ), mean percentage error ( MPE ), and co-variation ( CoV ). In terms of the ANN output ( \({\text{y}}_{\text{A}\text{N}\text{N}}\) ), experimental data ( \({\text{y}}_{\text{a}\text{c}\text{t}\text{u}\text{a}\text{l}}\) ) and the mean experimental data ( \({\stackrel{-}{\text{y}}}_{\text{a}\text{c}\text{t}\text{u}\text{a}\text{l}}\) ), R 2 , MPE and CoV for a number of k data are respectively given by: $${\text{R}}^{2}=1-\frac{\sum _{\text{i}=1}^{\text{k}}{\left({\text{y}}_{\text{A}\text{N}\text{N}}-{\text{y}}_{\text{a}\text{c}\text{t}\text{u}\text{a}\text{l}}\right)}^{2}}{\sum _{\text{i}=1}^{\text{k}}{\left({\text{y}}_{\text{a}\text{c}\text{t}\text{u}\text{a}\text{l}}-{\stackrel{-}{\text{y}}}_{\text{a}\text{c}\text{t}\text{u}\text{a}\text{l}}\right)}^{2}}$$ 11 $$MPE=\frac{\left(\frac{\sum _{\text{i}=1}^{\text{k}}{\text{y}}_{\text{A}\text{N}\text{N}}-{\text{y}}_{\text{a}\text{c}\text{t}\text{u}\text{a}\text{l}}}{\left|Max\left({\text{y}}_{\text{a}\text{c}\text{t}\text{u}\text{a}\text{l}}\right)-Min\left({\text{y}}_{\text{a}\text{c}\text{t}\text{u}\text{a}\text{l}}\right)\right|}\right)}{k}\times 100$$ 12 $$CoV=\frac{\sum _{\text{i}=1}^{\text{k}}\left({\text{y}}_{\text{A}\text{N}\text{N}}-{\stackrel{-}{\text{y}}}_{\text{A}\text{N}\text{N}}\right)\left({\text{y}}_{\text{a}\text{c}\text{t}\text{u}\text{a}\text{l}}-{\stackrel{-}{\text{y}}}_{\text{a}\text{c}\text{t}\text{u}\text{a}\text{l}}\right)}{k}\times 100$$ 13 The ANN topology comprised three layers: input, hidden and output. In the training stage, different numbers of neurons were run to obtain the best structure. According to this, the best structure was obtained as the topology with five neurons. The ANN structure is given in Fig. 1 . With the increase in the neuron number, it was determined that the forecasting sensitivity decreased, although the learning sensitivity increased. The main reason for this event is memorizing the results by the network. The comparison of ANN results and experimental data are given in Fig. 2 . The results of the statistical evaluation are given in Table 2 . Table 2 Validation of the ANN results. Training Hay yield Protein yield RFV IWUE WUE R 2 0.996 0.942 0.971 0.983 0.991 MPE 2.057 6.878 4.036 3.926 2.880 CoV 2.191 7.179 1.113 2.755 1.589 Testing R 2 0.955 0.900 0.786 0.961 0.952 MPE 7.219 14.820 18.111 11.885 7.951 CoV 4.690 8.598 2.777 9.455 4.620 According to statistical evaluation, R 2 values were obtained as 0.996, 0.942, 0.971, 0.983 and 0.991 for the training stages of hay yield, protein yield, RFV, IWUE and WUE, respectively. These values were recorded as 0.955, 0.900, 0.786, 0.961 and 0.952 for the testing stages of hay yield, protein yield, RFV, IWUE and WUE, respectively. The maximum MPE was determined as 18.111% in the testing stage of RFV. The maximum CoV was determined as 9.455% in the testing stage of IWUE. The results show that the developed ANN model has an acceptable ability to forecast agricultural activities. 3. Result and Discussion The effects of irrigation level, fertilizer doses and their interactions with each other on hay yield and protein yield were found to be statistically significant (Table 3 ). According to the interactions, the highest hay yield was obtained from I 100 x N 100 (28.20 t ha − 1 ), while the lowest was obtained from I 30 x N 0 (11.09 t ha − 1 ) and I 30 x N 50 (11.92 t ha − 1 ) treatment. When we look at the applications separately, the highest hay yield was obtained from I100 irrigation and the N150 fertilizer dose. Protein yield ranged between 0.93–2.37 t ha − 1 (Table 3 ). The highest protein yield was obtained from the I60*N150 interaction, and the lowest protein yield was obtained from the I30*N0 interaction. The greatest values were found from the I100 irrigation subject and N100, N150 fertilizer doses when we examined the effects of the applications on the protein yield separately. Mineral fertilizers and irrigation play a vital role in increasing crop yield. The results of the study showed us that with the increase in both irrigation level and fertilizer dose, weed yield and protein yield increased. Previous studies indicated a linear relationship between the amount of fertilizer and irrigation with yield (Ferré and Faci, 2009 ; Hussein and Sabbour, 2014 ; Hussein and Alva, 2014 ). Hussein and Sabbour ( 2014 ) reported that the hay yield of Sorghum at different irrigation and fertilization levels varied between 10.3–16.8 t ha − 1 . The variation in results for the same trait between studies can be attributed to differences in growing conditions, cultivars, cultural treatments, and ecology. Table 3 Irrigation x nitrogen doses interaction affects forage yields of SS hybrid IL Hay yield (t ha − 1 )** N0 N50 N100 N150 Mean** I30 11.09 h 11.92 h 12.65 gh 11.56 h 11.80 c I60 15.23 fg 16.53 ef 20.20 cd 22.90 bc 18.71 b I100 18.59 de 19.81 d 24.00 b 28.10 a 22.62 a Mean** 14.97 c 16.09 c 18.95 b 20.89 a IL Protein yield (t ha − 1 )** N0 N50 N100 N150 Mean** I30 0.93 f 1.19 ef 1.50 de 1.57 cde 1.30 c I60 1.30 ef 147.06 e 2.32 ab 2.37 a 187 b I100 1.92 bcd 1.94 abc 2.24 ab 2.12 ab 2.06 a Mean** 1.38 b 1.53 b 2.02 a 2.02 a **: p < 0.01 . Year:**; Irrigation level (IL):**; Fertilization doses (FD); ILxDS:**; SS: S orghum x sudangrass The separate treatments and interactions had a significant effect on RFV (Table 4 ). According to the interactions, the highest RFV was determined at 92.17 (I 30 × N 150 ) and 89.39 (I60 × N 150 ), while the lowest was 71.95 (I 100 × N 15 ). RFV is calculated with ADF and NDF and reveals the digestibility of the plant. Therefore, it is desirable to have a high RFV. In the study, the SS plant reached the highest RFV values at N 100 and N 150 fertilizer doses and I 30 and I 60 irrigation levels. Kaplan et al. ( 2014 ) that the increasing water levels also increased stem ratios, thus increasing ADF and NDF contents. Accordingly, I 100 had the lowest RFV value in this study. If the RFV exceeds 151, it is the beginning quality standard. If RFV is between 151–125, it is the first quality standard. If RFV is between 124–103, it is the second quality standard. If RFV is between 102–87, it is the third quality standard. If RFV is between 86–75, it is the fourth quality standard. If RFV is lower than 75 IF, it is the fifth quality standard in terms of forage quality (Rohweder et al., 1978 ). The RFV values determined in the study showed that the examined SS hybrid samples were between the fourth and third quality classes (Table 4 ). Table 4 Irrigation x nitrogen doses interaction affects on RFV of SS hybrid IL Relative feed value (RFV)** N0 N5 N10 N15 Mean** I30 79.00 cd 82.30 bc 82.75 bc 92.17 a 84.06 a I60 78.45 cd 78.67 cd 80.88 c 89.39 ab 81.85 a I100 79.77 c 78.54 cd 79.69 cd 71.95 d 77.49 b Mean** 79.07 b 79.84 b 81.11 ab 84.50 a **: p < 0.01 . Year:**; Irrigation level (IL):**; Fertilization doses (FD); ILxDS:**; SS: S orghum x sudangrass. Irrigation water use efficiency (IWUE) and water use efficiency (WUE) are the most important parameters for evaluating irrigation practices. Water use efficiency (WUE) refers to hay yield per unit of water used by the plant, and irrigation water use efficiency (IWUE) refers to hay yield per unit of water applied to the plant (Table 5 and Fig. 3 ). The table shows that the IWUE value increased as irrigation water decreased. The highest IWUE value of 6.35 kg m − 3 was obtained from I 30 N 100 , and the lowest IWUE value of 2.88 kg m − 3 was obtained from I100N0. When we look at the WUE values, the highest value was obtained from I 60 × N 150 as 4.10 kg m − 3 , and the lowest was obtained from I100 N0 as 2.30 kg m − 3 . When we look at the averages of irrigation subjects, the highest IWUE value was obtained for I30 and WUE value was obtained for I60 irrigation subjects. As the amount of irrigation water decreases, the increase in water productivity is an essential parameter for effective water use. When we compare irrigation issues, the issue of I60 comes to the fore. In studies on this subject, researchers have reached similar results and reported that water productivity increases as the amount of irrigation water decreases (Aydınsakir and Erdurmus, 2021, Farhadi et al., 2022 , Khalaf et al., 2019 ). Table 5 Irrigation water use and water use efficiency values of SS hybrid Irrigation levels N doses IWUE (kg m − 3 ) WUE (kg m − 3 ) I30 N0 5.57 ab 2.95 cde N5 5.98 ab 3.17 bcd N10 6.35 a 3.36 bc N15 5.8 ab 3.07 bcd I60 N0 3.82 de 2.73 def N5 4.15 cd 2.96 cde N10 5.07 bc 3.62 ab N15 5.73 ab 4.10 a I100 N0 2.88 f 2.30 f N5 3.07 ef 2.45 ef N10 3.72 def 2.97 cde N15 4.36 cd 3.48 bc **: p < 0.01. Year:**; Irrigation level (IL):**; Fertilization doses (FD); ILxDS:**; SS: Sorghum x sudangrass The ANN results for hay yield are given in Fig. 4 . According to the ANN outputs, the hay yield increases with the increase in irrigation and fertilizer, as expected. However, the hay yield decreases after N = 12 kg ha − 1 for I30. This means that the N cannot disperse sufficiently on the land. After I40, the irrigation effect on the hay yield shows a linear increase with the N dose. For I100, the hay yield has an increasing tendency with the N dose. This means that the N has an effective dispersion for the higher irrigation rates. Indeed, irrigation and fertilization show a positive correlation regarding plant growth. The ANN results for protein yield are given in Fig. 5 . The variation in protein yield has the same tendency as hay yield. However, protein yield starts to decrease after 80 kg ha-1 nitrogen dose for the highest irrigation level. (I100). The ANN results for RFV are given in Fig. 6 . The RFV for I40 decreases till N = 80 kg ha − 1 , then increases with the increase of the N doses. The RFV for I50 decreases till N = 40 kg ha − 1 , then increases with the increase of the N dose. I90 is the best practice for RFV. However, the lower irrigation level (I30) is the best for the higher N doses (N > 150 kg ha − 1 ). From the RFV point of view I100 is not a suitable practice after N = 20 kg ha − 1 . As seen in Fig. 7 , high N doses for the IWUE value are a suitable practice for low irrigation levels. I30 is the best practice for IWUE, although IWUE has decreasing tendency after N = 100 kg ha − 1 . I100 would be the best practice for the higher N dose since it has a more prominent increasing tendency after N = 60 kg ha − 1 . When we look at the WUE values in Fig. 8 . I40 is the best practice, although WUE has decreasing tendency after N = 120 kg ha − 1 . I50 would be the best practice till N = 60 kg ha − 1 . I100 would be the best practice for the higher N doses since it has a more prominent increasing tendency. 4. Conclusion In this study, an ANN model was developed to determine the forage yield and quality of SS hybrid plant parts that remained after seed harvesting. The developed ANN model investigated the effect of fertilizer and irrigation on the hay yield, protein yield, RVF, IWUE and WUE. It was determined that the structured ANN model could successfully observe these effects with limited land experiments. The best ANN model was obtained for the structure with one hidden layer, including five neurons. On the other hand, increasing hay yield, protein yield, and decreasing RFV with increasing irrigation levels. Accordingly, when yield, RFV, and water use efficiency are evaluated together, it is concluded that N10 and I100 levels would be suitable for the plant. However, when the field studies and the ANN model were evaluated together, it was determined that the I80 irrigation water with 100 kg t ha − 1 nitrogen doses would be suitable for the feed yield and quality of the Sorghum × Sudan grass hybrid. This means that the exact yield and quality can be obtained with 20% less irrigation given to the plants. In conclusion, the data obtained are of great importance in terms of the effective use of water resources that have decreased in recent years and contributing to the farmer's economy. Declarations Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Murat Karaer, Erdem Gülümser, Yusuf Murat Kardeş, Hüseyin Tevfik Gültaş, Hanife Mut. The first draft of the manuscript was written by all authors. The ANN modelling was performed by Oğuz Arslan. All authors read and approved the final manuscript. Funding This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No. TOVAG 122O683. Availability of Data and Materials Datasets are available upon request. Ethical Approval The authors declare that the manuscript is original and has not been published in any journal. Consent to Participate Not applicable. Consent to Publish The authors declare their consent to publication of the manuscript in “Water Resources Management” journal. Competing Interests The authors have no relevant financial or non-financial interests to disclose. References Arat H., Arslan O (2017). 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Deficit irrigation scheduling based on plant growth stages showing water stress tolerance. Deficit Irrigation Practice. Water Reports 22. FAO, Rome, pp. 3-10. Rohweder D, Barnes RF, Jorgensen N (1978). Proposed hay grading standards based on laboratory analyses for evaluating quality. J. Anim. Sci., 47(3): 747-759. https://doi.org/10.2527/jas1978.473747x Shen Q, Ding R, Du T, Tong L, Li S (2019). Water use effectiveness is enhanced using film mulch through increasing transpiration and decreasing evapotranspiration. Water, 11(6): 1153. Tugcu A, Arslan O (2017). Optimization of geothermal energy aided absorption refrigeration system—GAARS: A novel ANN-based approach. Geothermics 65: 210-221. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3487011","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":243673539,"identity":"0df3864f-d0ad-48b3-83bf-fc213b2e7d49","order_by":0,"name":"Murat KARAER","email":"","orcid":"","institution":"Bilecik Seyh Edebali University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Murat","middleName":"","lastName":"KARAER","suffix":""},{"id":243673540,"identity":"8ec72e95-2b7a-4808-8352-db128ed5646e","order_by":1,"name":"Erdem GÜLÜMSER","email":"","orcid":"","institution":"Bilecik Seyh Edebali University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Erdem","middleName":"","lastName":"GÜLÜMSER","suffix":""},{"id":243673541,"identity":"237e0420-c016-45bb-9ec5-a3a3876130ac","order_by":2,"name":"Yusuf Murat KARDEŞ","email":"","orcid":"","institution":"Bilecik Seyh Edebali University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yusuf","middleName":"Murat","lastName":"KARDEŞ","suffix":""},{"id":243673542,"identity":"5e1ca3b8-7d05-41d9-b22d-257548becf66","order_by":3,"name":"Hüseyin Tevfik GÜLTAŞ","email":"","orcid":"","institution":"Bilecik Seyh Edebali University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hüseyin","middleName":"Tevfik","lastName":"GÜLTAŞ","suffix":""},{"id":243673543,"identity":"b0d21db7-e783-4017-a123-d04b9cb27656","order_by":4,"name":"Hanife MUT","email":"","orcid":"","institution":"Bilecik Seyh Edebali University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hanife","middleName":"","lastName":"MUT","suffix":""},{"id":243673544,"identity":"4ba0d106-f0df-4039-9fa6-f53ddd80eab5","order_by":5,"name":"Oğuz ARSLAN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie3Rv0vEMBTA8RcCr0u8rDkK57+QUiiCQv+V10WXIoXCLQreVJeia/8IB8F/IFLodJ6rcCCCa4eCIA4qxh/gYFtudMh3C82HNC8ALtd/bgLAHkHvAHC0S9yA2D08BK1+idiAoA+gfk4YIVJehB0c38fo3TRHWaZm8hQJunkNsW96ybRqtYYmTwpxuL+utApVjYZVqxrEhHqJvltqAiRCSKO10Cq55N6CbxWWDPzZJzHwTjHKNsotOfkib2PktgwWrCBWqDTilpDmaDgbIdMKc0jOKClUG/r2LkFVI12XqwMhlkMTq6+67pliKdPgKXvd25bnTfDwMt+deWU/AWXH8mcyBsZeUprBTy6Xy+X67gNwgE0vN0PEdAAAAABJRU5ErkJggg==","orcid":"","institution":"Bilecik Seyh Edebali University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Oğuz","middleName":"","lastName":"ARSLAN","suffix":""}],"badges":[],"createdAt":"2023-10-24 17:14:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3487011/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3487011/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":45592230,"identity":"bd89f89b-3c31-4a2b-9586-f147ebdd94ee","added_by":"auto","created_at":"2023-10-31 20:50:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74531,"visible":true,"origin":"","legend":"\u003cp\u003eThe structure of the used ANN topology.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3487011/v1/ed811f613b04818830bfdbaa.png"},{"id":45593213,"identity":"c75b56a2-2310-4986-86f9-aca871afef42","added_by":"auto","created_at":"2023-10-31 20:58:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85135,"visible":true,"origin":"","legend":"\u003cp\u003eThe comparison of experimental and ANN results.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3487011/v1/d568b8c239fdd0a728d1d1c1.png"},{"id":45592231,"identity":"930fad7a-bbe3-46e5-8fcc-d28d797453d2","added_by":"auto","created_at":"2023-10-31 20:50:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58153,"visible":true,"origin":"","legend":"\u003cp\u003eAccording to the average irrigation water levels, Irrigation water use and water use efficiency values of SS hybrid.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3487011/v1/21395cc308fad0d8ae9101a8.png"},{"id":45592232,"identity":"0bc08dc1-0295-4294-9981-41f53a4e74d9","added_by":"auto","created_at":"2023-10-31 20:50:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40313,"visible":true,"origin":"","legend":"\u003cp\u003eANN results for hay yield.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3487011/v1/bf7e81795052d19d73f6713e.png"},{"id":45592233,"identity":"374605ae-3e19-4284-8fa1-6fb206f5b445","added_by":"auto","created_at":"2023-10-31 20:50:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46177,"visible":true,"origin":"","legend":"\u003cp\u003eANN results for protein yield.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3487011/v1/0106345ff669f40d4e3cf20b.png"},{"id":45594588,"identity":"62970f0a-4740-47fe-ba4f-1079a6e744f9","added_by":"auto","created_at":"2023-10-31 21:06:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":44726,"visible":true,"origin":"","legend":"\u003cp\u003eANN results for RFV.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3487011/v1/4258afe9b10244898d07560b.png"},{"id":45593212,"identity":"b9dceb6b-94c0-4468-9abe-327c06602916","added_by":"auto","created_at":"2023-10-31 20:58:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":44802,"visible":true,"origin":"","legend":"\u003cp\u003eANN results for IWUE.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3487011/v1/dbf81cc2a8c5c9965fefc99b.png"},{"id":45592237,"identity":"ddf0e93b-af71-4481-8ac8-9fc0153eb6f0","added_by":"auto","created_at":"2023-10-31 20:50:38","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":48031,"visible":true,"origin":"","legend":"\u003cp\u003eANN results for WUE.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3487011/v1/dc609db974b798979c6b17a7.png"},{"id":45887422,"identity":"3f1896b0-9c4e-4e3d-93e2-080e4a409bc6","added_by":"auto","created_at":"2023-11-05 13:07:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":916456,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3487011/v1/272d85c8-341e-485c-ab83-1592e089e15f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Artificial Neural Network Modeling for Investigation on the Effect of Deficit Irrigation and Nitrogen Levels on Hay Yield and Quality of Sorghum Sudangrass Hybrid","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDue to the increasing world population and climate change, our agricultural areas and natural resources are decreasing day by day. In particular, the pressure on water resources has been felt more in recent years. When we look at the sectoral utilization of the existing water assets in the world, the agricultural sector is the largest user of water, with approximately 70%. Therefore, it has become a global necessity to use resources efficiently in the agricultural sector.\u003c/p\u003e \u003cp\u003eFor this reason, in recent years, serious measures have been taken to ensure effective use of water and water saving in agriculture sector. Deficit irrigation technique is one of the methods that reduces the amount of water given to plants and saves water compared to full irrigation.\u003c/p\u003e \u003cp\u003eDeficit irrigation is an essential and sustainable production strategy applied in regions with limited water resources (Geerts and Raes, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The aim is to increase plant water use efficiency by reducing the amount of irrigation water or the number of irrigations. The plant to be irrigated with limited irrigation is exposed to water stress at specific rates during any period of the development period or throughout the season, and it is expected to save irrigation water without causing a significant decrease in yield (Kırda, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFertilization, along with irrigation, is one of the most critical inputs to increase productivity in plant production. While the use of an appropriate amount of fertilizer increases the yield, when it is used excessively, some of the fertilizers are washed from the soil or become unfixable in the soil, thus reducing the effectiveness of the fertilizer. Therefore, a planning should be made before applying fertilizer to the soil. There are 16 elements that are essential in plant nutrition. To effectively use these 16 important components in plant nutrition, the soil must be given the exact dosage that each of these elements needs to grow. Because the use of too much fertilizer will not increase the yield and may also cause soil pollution. That's why it's so important to avoid over-fertilizing the soil and instead provide the plant with the proper amount of fertilizer.\u003c/p\u003e \u003cp\u003eSorghum-sudangrass (\u003cem\u003eSS\u003c/em\u003e) hybrids are a cross between \u003cem\u003eSorghum bicolor\u003c/em\u003e (L.) Moench and \u003cem\u003eSorghum Sudanese\u003c/em\u003e (Piper) Stapf and have wide adaptability. It is a plant with a sugar-rich stem and a high biomass yield. Due to its lower water and fertilizer needs, it is very suitable for cultivation in marginal areas. SS hybrids have shown a great increase in animal nutrition, especially in recent years, due to their features such as ease of cultivation, rapid settlement, growth and rich nutrient content.\u003c/p\u003e \u003cp\u003eFor an agricultural activity's field experiments to produce findings that are acceptable, additional time, work, land, and money are required. An ANN model sensitively measures the degree of influence since it can successfully yield agreeable predictions with high scores (Arslan, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Arslan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In this study, artificial neural network (ANN) modeling was first developed and used to forecast the effects of different irrigation water levels and fertilizer doses on the hay yield and some quality traits of the herbal parts of the SS hybrid remaining after seed harvesting. Thus, more precise results can be obtained with fewer field experiments.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cp\u003eField experiments were implemented in the Application and Research Station (40\u0026deg; 6` N, 30\u0026deg;.0` E) at the University of Bilecik Seyh Edebali, in Bilecik, Turkey. The studies were carried out in the 2021 and 2022 growing seasons. Based on long-year meteorological data, the average temperature in Bilecik is 12.5\u0026deg;C, and the annual average rainfall is 459.3 mm. For the 2021 and 2022 tomato growing seasons, when the study was conducted, the total rainfall was 94.7 mm and 116.3 mm, respectively. Soil analysis was done before the study started and some physical and chemical soil properties were determined. According to the results, the land will have a loamy soil structure and other soil properties are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSome properties of the experiment area soil\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTexture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eField capacity\u003c/p\u003e \u003cp\u003ePW(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVolume weight (g/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOrganic Matter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003cp\u003eP\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(kg/da)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePotassium K\u003csub\u003e2\u003c/sub\u003eO (kg/da)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e26.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e116.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e27.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e21.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Experimental Design and Treatments\u003c/h2\u003e \u003cp\u003eIn the study, Aneto sorghum x sudangrass hybrid (SS) was used as crop material. The Aneto variety is drought resistant, rapid growing and of high silage quality. The experiments were carried out using a three-replication randomized block-split plot design. The plot measures 6 x 2.8 m (16.8 m2), with a plant spacing of 5 cm on each row and a row spacing of 70 cm overall. There are four rows each parcel. The study was designed with three irrigation subjects (100% (I100), 60% (I60), and 30% (I30) of the evaporation measured in the Class A Pan) for the main plots and four nitrogen doses (0 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e(N0), 50 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e1(N50), 100 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (N100), 150 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e(N150 )) for the subplots. All plots applied 80 kg of P2O5 of DAP base fertilizer per hectare during planting. Considering the nitrogen we gave with the base fertilizer while fertilizing applications, the missing amounts were completed to 50, 100, and 150 kg per ha and given to other parcels. Irrigation was done in the form of supplementary irrigation during the three critical periods determined for the plant. The first irrigation was done when the plants reached 30\u0026ndash;35 cm in height, the second at the beginning of flowering, and the third when the panicle emerged.\u003c/p\u003e \u003cp\u003eIrrigation is calculated as in Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e according to the amount of cumulative evaporation occurring in the class A pan during the specified critical periods (Kanber,1984):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$I=A\\times kcp\\times Ep\\times P$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere I is the irrigation amount (mm), A is the parcel area (m\u003csup\u003e2\u003c/sup\u003e), kcp plant-pan coefficient (0,0.30, 0.60, 1.00) E\u003csub\u003ep\u003c/sub\u003e is the cumulative evaporation amount from class A-pan (mm), P is the percentage of vegetation.\u003c/p\u003e \u003cp\u003eThe soil water content was measured gravimetrically at 0.3 m depth to 1.2 m throughout the growing season. Crop evapotranspiration is calculated using a water balance equation (Shen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$ETa=I+P+W\\pm \\varDelta S-D-R$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere I is the irrigation amount (mm), P is the seasonal amount of precipitation (mm), W is the groundwater flow into the root zone (mm), ΔS is the change in soil moisture content (mm), D deep drainage losses (mm) and R are the surface runoff. In the equation, the \u003cem\u003eP\u003c/em\u003e-value is taken from the Bilecik State Meteorology Station. W was ignored as the groundwater level was 15m below the ground surface, which was not deep enough to affect the growth of Sorghum Sudan grass. In the drip irrigation method, capillary rise (R) is considered zero since there is no surface flow or groundwater in the area. Since no irrigation is subject to exceeding the field capacity, the deep infiltration (D) value was also taken as zero (Hanks,1974).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Water Productivity\u003c/h2\u003e \u003cp\u003eWater use efficiency and irrigation water use efficiency values are the most important variables to consider when analyzing correlations between plant-water yields. The yield values acquired per unit of water applied to the plant are shown by irrigation water use efficiency, and the yield values obtained versus seasonal plant water consumption are shown by water use efficiency. These values are calculated from the equations determined by Howell et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1990\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$IWUE:\\frac{Y}{I}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$WUE:\\frac{Y}{ETa}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere IWUE is the irrigation water use efficiency (kg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Y is the yield (kg da\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), I is the volume of seasonal irrigation water applied (m\u003csup\u003e3\u003c/sup\u003e da\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), WUE is the water use efficiency (kg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), and ETa is the actual seasonal evapotranspiration (m\u003csup\u003e3\u003c/sup\u003e da\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Hay yield and quality analysis\u003c/h2\u003e \u003cp\u003eThe plants were harvested at the seed maturation stage, the seeds were separated, and the remaining plant parts were used in the measurements and analyses. The hay yield was determined after drying the samples at 65\u0026deg;C until constant weight. Crude protein ratio, acid detergent fiber (ADF) and neutral detergent fiber (NDF) were determined by using Near Reflectance Spectroscopy. Relative feed value (RFV) is estimated according to the following equations adapted from Rohweder et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1978\u003c/span\u003e):\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$DDM=88.9\u0026ndash;(0.779\\bullet ADF)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$DMI=\\frac{120 }{NDF }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$TDN=96.35\u0026ndash;ADF\\bullet 1.15$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$RFV=\\frac{DDM\\bullet DMI}{1.29}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere DDM is the digestibility of dry matter (%), DMI is dry matter intake (%), TDN is the total digestible nutrient (%), and RFV is the relative feed value (%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analysis of the field experiments\u003c/h2\u003e \u003cp\u003eYield and other determined quality parameters were subjected to analysis of variance (ANOVA) using Minitab 19 software. The significance of irrigation and nitrogen doses was determined using the F test. When the F-test was significant, the Tukey test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was used to compare the group means of irrigation and nitrogen dose treatments and their interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. ANN modelling\u003c/h2\u003e \u003cp\u003eIn the study, the feed-forward back-propagation learning algorithm, which is most widely used, was handled in the multi-layer neural network (MLNN) architecture, as well as the Levenberg-Marguardt (LM) algorithm, which was selected as the training algorithm since it has successful scores (Tugcu and Arslan, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). All the training and testing values were normalized in the range of 0.3 and 0.7 to scale all parameters to the equivalent degree for faster and more sensitive results (Boukelia et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The non-linear transfer function, namely the logarithmic sigmoid (logsig), was used to forecast the complex relations for the related activity (Arat and Arslan, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The logsig function for the inputs is given as:\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$f\\left(\\text{n}\\right)=\\frac{1}{1+{e}^{-\\text{n}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere n is the weighted sum given by:\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$${\\text{n}}_{\\text{j}}=\\sum _{\\text{i}=1}^{\\text{k}}{\\text{w}}_{\\text{i},\\text{j}}{\\text{y}}_{\\text{i}}+{\\text{b}}_{\\text{j}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, w is the weight, y is the output value, and b is the bias. Seventy-five percent of the experiments were randomly selected for the training of the ANN model; the remaining part was used in the testing stage. The accuracy of the network was separately measured for the training and testing stages by the coefficient of multiple determinations (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e), mean percentage error (\u003cem\u003eMPE\u003c/em\u003e), and co-variation (\u003cem\u003eCoV\u003c/em\u003e). In terms of the ANN output (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{y}}_{\\text{A}\\text{N}\\text{N}}\\)\u003c/span\u003e\u003c/span\u003e), experimental data (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{y}}_{\\text{a}\\text{c}\\text{t}\\text{u}\\text{a}\\text{l}}\\)\u003c/span\u003e\u003c/span\u003e) and the mean experimental data (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\stackrel{-}{\\text{y}}}_{\\text{a}\\text{c}\\text{t}\\text{u}\\text{a}\\text{l}}\\)\u003c/span\u003e\u003c/span\u003e), \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e, \u003cem\u003eMPE\u003c/em\u003e and \u003cem\u003eCoV\u003c/em\u003e for a number of \u003cem\u003ek\u003c/em\u003e data are respectively given by:\u003cdiv id=\"Equ11\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ11\" name=\"EquationSource\"\u003e\n$${\\text{R}}^{2}=1-\\frac{\\sum _{\\text{i}=1}^{\\text{k}}{\\left({\\text{y}}_{\\text{A}\\text{N}\\text{N}}-{\\text{y}}_{\\text{a}\\text{c}\\text{t}\\text{u}\\text{a}\\text{l}}\\right)}^{2}}{\\sum _{\\text{i}=1}^{\\text{k}}{\\left({\\text{y}}_{\\text{a}\\text{c}\\text{t}\\text{u}\\text{a}\\text{l}}-{\\stackrel{-}{\\text{y}}}_{\\text{a}\\text{c}\\text{t}\\text{u}\\text{a}\\text{l}}\\right)}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ12\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ12\" name=\"EquationSource\"\u003e\n$$MPE=\\frac{\\left(\\frac{\\sum _{\\text{i}=1}^{\\text{k}}{\\text{y}}_{\\text{A}\\text{N}\\text{N}}-{\\text{y}}_{\\text{a}\\text{c}\\text{t}\\text{u}\\text{a}\\text{l}}}{\\left|Max\\left({\\text{y}}_{\\text{a}\\text{c}\\text{t}\\text{u}\\text{a}\\text{l}}\\right)-Min\\left({\\text{y}}_{\\text{a}\\text{c}\\text{t}\\text{u}\\text{a}\\text{l}}\\right)\\right|}\\right)}{k}\\times 100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e12\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ13\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ13\" name=\"EquationSource\"\u003e\n$$CoV=\\frac{\\sum _{\\text{i}=1}^{\\text{k}}\\left({\\text{y}}_{\\text{A}\\text{N}\\text{N}}-{\\stackrel{-}{\\text{y}}}_{\\text{A}\\text{N}\\text{N}}\\right)\\left({\\text{y}}_{\\text{a}\\text{c}\\text{t}\\text{u}\\text{a}\\text{l}}-{\\stackrel{-}{\\text{y}}}_{\\text{a}\\text{c}\\text{t}\\text{u}\\text{a}\\text{l}}\\right)}{k}\\times 100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e13\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe ANN topology comprised three layers: input, hidden and output. In the training stage, different numbers of neurons were run to obtain the best structure. According to this, the best structure was obtained as the topology with five neurons. The ANN structure is given in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith the increase in the neuron number, it was determined that the forecasting sensitivity decreased, although the learning sensitivity increased. The main reason for this event is memorizing the results by the network. The comparison of ANN results and experimental data are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results of the statistical evaluation are given in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidation of the ANN results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHay yield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein yield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRFV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIWUE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWUE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMPE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTesting\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMPE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to statistical evaluation, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e values were obtained as 0.996, 0.942, 0.971, 0.983 and 0.991 for the training stages of hay yield, protein yield, RFV, IWUE and WUE, respectively. These values were recorded as 0.955, 0.900, 0.786, 0.961 and 0.952 for the testing stages of hay yield, protein yield, RFV, IWUE and WUE, respectively. The maximum \u003cem\u003eMPE\u003c/em\u003e was determined as 18.111% in the testing stage of RFV. The maximum \u003cem\u003eCoV\u003c/em\u003e was determined as 9.455% in the testing stage of IWUE. The results show that the developed ANN model has an acceptable ability to forecast agricultural activities.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result and Discussion","content":"\u003cp\u003eThe effects of irrigation level, fertilizer doses and their interactions with each other on hay yield and protein yield were found to be statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). According to the interactions, the highest hay yield was obtained from I\u003csub\u003e100\u003c/sub\u003e x N\u003csub\u003e100\u003c/sub\u003e (28.20 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), while the lowest was obtained from I\u003csub\u003e30\u003c/sub\u003e x N\u003csub\u003e0\u003c/sub\u003e (11.09 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and I\u003csub\u003e30\u003c/sub\u003e x N\u003csub\u003e50\u003c/sub\u003e (11.92 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) treatment. When we look at the applications separately, the highest hay yield was obtained from I100 irrigation and the N150 fertilizer dose. Protein yield ranged between 0.93\u0026ndash;2.37 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The highest protein yield was obtained from the I60*N150 interaction, and the lowest protein yield was obtained from the I30*N0 interaction. The greatest values were found from the I100 irrigation subject and N100, N150 fertilizer doses when we examined the effects of the applications on the protein yield separately. Mineral fertilizers and irrigation play a vital role in increasing crop yield. The results of the study showed us that with the increase in both irrigation level and fertilizer dose, weed yield and protein yield increased.\u003c/p\u003e \u003cp\u003ePrevious studies indicated a linear relationship between the amount of fertilizer and irrigation with yield (Ferr\u0026eacute; and Faci, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hussein and Sabbour, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hussein and Alva, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Hussein and Sabbour (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reported that the hay yield of Sorghum at different irrigation and fertilization levels varied between 10.3\u0026ndash;16.8 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The variation in results for the same trait between studies can be attributed to differences in growing conditions, cultivars, cultural treatments, and ecology.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIrrigation x nitrogen doses interaction affects forage yields of \u003cem\u003eSS\u003c/em\u003e hybrid\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eHay yield (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eN100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN150\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.09\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.92\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e12.65\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.56\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e11.80\u003c/b\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.23\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.53\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e20.20\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.90\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e18.71\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.59\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.81\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e24.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e22.62\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e14.97\u003c/b\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e16.09\u003c/b\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e18.95\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e20.89\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eIL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eProtein yield (t ha\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eN50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eN100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eN150\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eMean**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.19\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.57\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.30\u003c/b\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e147.06\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.32\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e187\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.92\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.94\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.24\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.12\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.06\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.38\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.53\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.02\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.02\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003e**: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e. \u003cem\u003eYear:**; Irrigation level (IL):**; Fertilization doses (FD); ILxDS:**; SS: S\u003c/em\u003eorghum x sudangrass\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe separate treatments and interactions had a significant effect on RFV (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). According to the interactions, the highest RFV was determined at 92.17 (I\u003csub\u003e30 \u0026times;\u003c/sub\u003e N\u003csub\u003e150\u003c/sub\u003e) and 89.39 (I60 \u0026times; N\u003csub\u003e150\u003c/sub\u003e), while the lowest was 71.95 (I\u003csub\u003e100\u003c/sub\u003e \u0026times; N\u003csub\u003e15\u003c/sub\u003e). RFV is calculated with ADF and NDF and reveals the digestibility of the plant. Therefore, it is desirable to have a high RFV. In the study, the SS plant reached the highest RFV values at N\u003csub\u003e100\u003c/sub\u003e and N\u003csub\u003e150\u003c/sub\u003e fertilizer doses and I\u003csub\u003e30\u003c/sub\u003e and I\u003csub\u003e60\u003c/sub\u003e irrigation levels. Kaplan et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) that the increasing water levels also increased stem ratios, thus increasing ADF and NDF contents. Accordingly, I\u003csub\u003e100\u003c/sub\u003e had the lowest RFV value in this study. If the RFV exceeds 151, it is the beginning quality standard. If RFV is between 151\u0026ndash;125, it is the first quality standard. If RFV is between 124\u0026ndash;103, it is the second quality standard. If RFV is between 102\u0026ndash;87, it is the third quality standard. If RFV is between 86\u0026ndash;75, it is the fourth quality standard. If RFV is lower than 75 IF, it is the fifth quality standard in terms of forage quality (Rohweder et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). The RFV values determined in the study showed that the examined SS hybrid samples were between the fourth and third quality classes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIrrigation x nitrogen doses interaction affects on RFV of \u003cem\u003eSS\u003c/em\u003e hybrid\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eRelative feed value (RFV)**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.00\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.30\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.75\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.17\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e84.06\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.45\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.67\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.88\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.39\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e81.85\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.77\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.54\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.69\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.95\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e77.49\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e79.07\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e79.84\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e81.11\u003c/b\u003e\u003csup\u003e\u003cb\u003eab\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e84.50\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e**: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e. \u003cem\u003eYear:**; Irrigation level (IL):**; Fertilization doses (FD); ILxDS:**; SS: S\u003c/em\u003eorghum x sudangrass.\u003c/p\u003e \u003cp\u003eIrrigation water use efficiency (IWUE) and water use efficiency (WUE) are the most important parameters for evaluating irrigation practices. Water use efficiency (WUE) refers to hay yield per unit of water used by the plant, and irrigation water use efficiency (IWUE) refers to hay yield per unit of water applied to the plant (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The table shows that the IWUE value increased as irrigation water decreased. The highest IWUE value of 6.35 kg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e was obtained from I\u003csub\u003e30\u003c/sub\u003e N\u003csub\u003e100\u003c/sub\u003e, and the lowest IWUE value of 2.88 kg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e was obtained from I100N0. When we look at the WUE values, the highest value was obtained from I\u003csub\u003e60\u003c/sub\u003e \u0026times; N\u003csub\u003e150\u003c/sub\u003e as 4.10 kg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, and the lowest was obtained from I100 N0 as 2.30 kg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. When we look at the averages of irrigation subjects, the highest IWUE value was obtained for I30 and WUE value was obtained for I60 irrigation subjects. As the amount of irrigation water decreases, the increase in water productivity is an essential parameter for effective water use. When we compare irrigation issues, the issue of I60 comes to the fore. In studies on this subject, researchers have reached similar results and reported that water productivity increases as the amount of irrigation water decreases (Aydınsakir and Erdurmus, 2021, Farhadi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Khalaf et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIrrigation water use and water use efficiency values of \u003cem\u003eSS\u003c/em\u003e hybrid\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrrigation levels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN doses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWUE (kg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWUE (kg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eI30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.57 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.95 cde\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.98 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.17 bcd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.35 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.36 bc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.07 bcd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eI60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.82 de\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.73 def\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.15 cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.96 cde\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.07 bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.62 ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.73 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.10 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eI100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88 f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.30 f\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.07 ef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.45 ef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.72 def\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.97 cde\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.36 cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.48 bc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e**: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Year:**; Irrigation level (IL):**; Fertilization doses (FD); ILxDS:**; SS: Sorghum x sudangrass\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ANN results for hay yield are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. According to the ANN outputs, the hay yield increases with the increase in irrigation and fertilizer, as expected. However, the hay yield decreases after N\u0026thinsp;=\u0026thinsp;12 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for I30. This means that the N cannot disperse sufficiently on the land. After I40, the irrigation effect on the hay yield shows a linear increase with the N dose. For I100, the hay yield has an increasing tendency with the N dose. This means that the N has an effective dispersion for the higher irrigation rates. Indeed, irrigation and fertilization show a positive correlation regarding plant growth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ANN results for protein yield are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The variation in protein yield has the same tendency as hay yield. However, protein yield starts to decrease after 80 kg ha-1 nitrogen dose for the highest irrigation level. (I100).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ANN results for RFV are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The RFV for I40 decreases till N\u0026thinsp;=\u0026thinsp;80 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, then increases with the increase of the N doses. The RFV for I50 decreases till N\u0026thinsp;=\u0026thinsp;40 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, then increases with the increase of the N dose. I90 is the best practice for RFV. However, the lower irrigation level (I30) is the best for the higher N doses (N\u0026thinsp;\u0026gt;\u0026thinsp;150 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). From the RFV point of view I100 is not a suitable practice after N\u0026thinsp;=\u0026thinsp;20 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, high N doses for the IWUE value are a suitable practice for low irrigation levels. I30 is the best practice for IWUE, although IWUE has decreasing tendency after N\u0026thinsp;=\u0026thinsp;100 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. I100 would be the best practice for the higher N dose since it has a more prominent increasing tendency after N\u0026thinsp;=\u0026thinsp;60 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen we look at the WUE values in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. I40 is the best practice, although WUE has decreasing tendency after N\u0026thinsp;=\u0026thinsp;120 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. I50 would be the best practice till N\u0026thinsp;=\u0026thinsp;60 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. I100 would be the best practice for the higher N doses since it has a more prominent increasing tendency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn this study, an ANN model was developed to determine the forage yield and quality of SS hybrid plant parts that remained after seed harvesting. The developed ANN model investigated the effect of fertilizer and irrigation on the hay yield, protein yield, RVF, IWUE and WUE. It was determined that the structured ANN model could successfully observe these effects with limited land experiments. The best ANN model was obtained for the structure with one hidden layer, including five neurons. On the other hand, increasing hay yield, protein yield, and decreasing RFV with increasing irrigation levels. Accordingly, when yield, RFV, and water use efficiency are evaluated together, it is concluded that N10 and I100 levels would be suitable for the plant. However, when the field studies and the ANN model were evaluated together, it was determined that the I80 irrigation water with 100 kg t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e nitrogen doses would be suitable for the feed yield and quality of the Sorghum \u0026times; Sudan grass hybrid. This means that the exact yield and quality can be obtained with 20% less irrigation given to the plants. In conclusion, the data obtained are of great importance in terms of the effective use of water resources that have decreased in recent years and contributing to the farmer's economy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Murat Karaer, Erdem G\u0026uuml;l\u0026uuml;mser, Yusuf Murat Kardeş, H\u0026uuml;seyin Tevfik G\u0026uuml;ltaş, Hanife Mut. The first draft of the manuscript was written by all authors. The ANN modelling was performed by Oğuz Arslan. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No. TOVAG 122O683.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u0026nbsp;\u003c/strong\u003eDatasets are available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003eThe authors declare that the manuscript is original and has not been published in any journal.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u0026nbsp;\u003c/strong\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u0026nbsp;\u003c/strong\u003eThe authors declare their consent to publication of the manuscript in \u0026ldquo;Water Resources Management\u0026rdquo; journal.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArat H., Arslan O (2017). Optimization of district heating system aided by geothermal heat pump: A novel multistage with multilevel ANN modelling. Appl. Therm. Eng. 111: 608\u0026ndash;623.\u003c/li\u003e\n\u003cli\u003eArslan O (2011). Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34. Energy 36: 2528-2534.\u003c/li\u003e\n\u003cli\u003eArslan O (2014). ANN-based determination of optimum working conditions of residential combustors with respect to optimum insulation. Energy Sources A: Recovery Util. Environ. Eff. 36: 2603-2612.\u003c/li\u003e\n\u003cli\u003eAydinsakir K, Erdurmus C (2021). Influence of Different Drip Irrigation Methods and Irrigation Levels on Sweet Sorghum. Feb Fresenıus Envıronmental Bulletın, 1465.\u003c/li\u003e\n\u003cli\u003eBate-Smith EC (1975). Phytochemistry of proanthocyanidins. Phytochemistry, 14: 1107-1113.\u003c/li\u003e\n\u003cli\u003eBoukelia TE, Arslan O, Mecibah, MS\u0026Ccedil; (2016). ANN-based optimization of a parabolic trough solar thermal power plant. Appl. Therm. Eng. 107: 1210-1218.\u003c/li\u003e\n\u003cli\u003eFarhadi A, Paknejad F, Golzardi F, Ilkaee MN, Aghayari, F (2022). Effects of limited irrigation and nitrogen rate on the herbage yield, water productivity, and nutritive value of sorghum silage. Commun Soil Sci Plant Anal, 53(5): 576-589. https://doi.org/10.1080/00103624.2021.2017959\u003c/li\u003e\n\u003cli\u003eFerr\u0026eacute; I, Faci JM (2009). Deficit Irrigation in Maize for Reducing Agricultural Water in a Mediterranean Environment. Agric. Water Manag. 96: 383-394. https://doi.org/10.1016/j.agwat.2008.07.002\u003c/li\u003e\n\u003cli\u003eGeerts S, Raes D (2009). Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas. Agric. Water Manag. 96:1275\u0026ndash;1284. https://doi.org/10.1016/j.agwat.2009.04.009\u003c/li\u003e\n\u003cli\u003eHanks RJ (1974). Model for predicting plant yield as influenced by water use 1. J. Agron. 66(5): 660-665. https://doi.org/10.2134/agronj1974.00021962006600050017x\u003c/li\u003e\n\u003cli\u003eHowell TA, Cuenca RH, Solomon KH (1990). Crop yield response management of farm irrigation systems.In: Hofman, G:J., et al. (Eds.), Management of Farm Irrigation Systems. ASAE, St. Joseph, Ml, p.311\u0026ndash;312.\u003c/li\u003e\n\u003cli\u003eHussein MM, Alva AK (2014). Growth, Yield and Water Use Effeciency of Forage Sorghum as Affected by Npk Fertilizer and Deficit Irrigation. Am. J. Plant Sci. 5, 2134-2140. http://dx.doi.org/10.4236/ajps.2014.513225.\u003c/li\u003e\n\u003cli\u003eHussein MM, Sabbour MM (2014). Irrigation intervals and nitrogen fertilizer on yield and water use effeciency of sorghum fodder. Int J Sci Res, 3: 404-410.\u003c/li\u003e\n\u003cli\u003eKanber R (1984). Irrigation of first and second product peanuts by utilizing open water surface evaporation in \u0026Ccedil;ukurova conditions. Regional Groundwater Research Institute Publications 114: 64-93.\u003c/li\u003e\n\u003cli\u003eKaplan M, Kamalak A, Kasra AA, Guven I (2014). Effect of maturity stages on potential nutritive value, methane production and condensed tannin content of Sanguisorba minor. Kafkas Univ. Vet. Faculty J. 20: 445\u0026ndash;449.\u003c/li\u003e\n\u003cli\u003eKhalaf AA, Issazadeh L, Abdullah ZA, Hassanpour J (2019). Growth and Yield Assessment of Two Types of Sorghum-Sudangrass Hybrids as Affected by Deficit Irrigation. Int. J. Agric. Eng., 13(7): 214-218.\u003c/li\u003e\n\u003cli\u003eKırda C (2002). Deficit irrigation scheduling based on plant growth stages showing water stress tolerance. Deficit Irrigation Practice. Water Reports 22. FAO, Rome, pp. 3-10.\u003c/li\u003e\n\u003cli\u003eRohweder D, Barnes RF, Jorgensen N (1978). Proposed hay grading standards based on laboratory analyses for evaluating quality. J. Anim. Sci., 47(3): 747-759. https://doi.org/10.2527/jas1978.473747x\u003c/li\u003e\n\u003cli\u003eShen Q, Ding R, Du T, Tong L, Li S (2019). Water use effectiveness is enhanced using film mulch through increasing transpiration and decreasing evapotranspiration. Water, 11(6): 1153. \u003c/li\u003e\n\u003cli\u003eTugcu A, Arslan O (2017). Optimization of geothermal energy aided absorption refrigeration system\u0026mdash;GAARS: A novel ANN-based approach. Geothermics 65: 210-221.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial neural network, deficit irrigation, fertilization, hay yield, protein quality, roughage","lastPublishedDoi":"10.21203/rs.3.rs-3487011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3487011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIrrigation and fertilization are the most critical inputs to increase productivity in plant production. However, the unconscious use of water resources in agriculture and excessive fertilization create an obstacle to the sustainability of production. Deficit irrigation is an essential and sustainable production strategy applied in regions with limited water resources. Since the experiments need more labour, time, input preparation, and land, it is hard to handle the far-reaching parameters. In this regard, artificial neural networks (ANNs) can be an alternative solution. This study aims to develop an ANN modeling to be trained to forecast the effects of different irrigation water levels and fertilizer doses on the hay yield and some quality traits of herbal parts of Sorghum \u0026times; Sudan grass (SS) hybrid (\u003cem\u003eSorghum sudanense\u003c/em\u003e vs. \u003cem\u003eSorghum bicolor\u003c/em\u003e) remaining after seed harvesting. The ANN model was developed on the limited field experiments implemented in Bilecik, Turkey, for two years in 2021 and 2022. Experiments were conducted in randomized blocks-split plots design with three replications, three irrigation levels (I100, I60, and I30), and four nitrogen treatments (N0, N50, N100, and N150 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The hay yield, protein yield, relative feed value (RFV), water use efficiency (WUE), and irrigation water use efficiency (IWUE) were determined in this study. It was determined that the ANN structure, including one hidden layer with five neurons, could successfully be used to obtain the best practice conditions for the handled agricultural activity. According to the field experiments and ANN model, the I80 irrigation with 100 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e nitrogen doses would suit the feed yield and quality of Sorghum \u0026times; Sudan grass hybrid.\u003c/p\u003e","manuscriptTitle":"Artificial Neural Network Modeling for Investigation on the Effect of Deficit Irrigation and Nitrogen Levels on Hay Yield and Quality of Sorghum Sudangrass Hybrid","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-31 20:50:34","doi":"10.21203/rs.3.rs-3487011/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07f40078-1213-4545-930f-227e917dfd55","owner":[],"postedDate":"October 31st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-05T12:59:25+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-31 20:50:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3487011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3487011","identity":"rs-3487011","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}