
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

72746
10.1038/s41598-024-72746-9
Article
Prediction of fresh herbage yield using data mining techniques with limited plant quality parameters
http://orcid.org/0000-0001-5894-8986
Çelik Şenol senolcelik@bingol.edu.tr

1
http://orcid.org/0000-0002-9341-3503
Tutar Halit 2
http://orcid.org/0000-0002-1621-0892
Gönülal Erdal 3
http://orcid.org/0000-0002-7880-8697
Er Hasan 4
1 https://ror.org/03hx84x94 grid.448543.a 0000 0004 0369 6517 Biometry and Genetic Unit, Department of Animal Science, Faculty of Agriculture, Bingol University, 12000 Bingöl, Turkey
2 https://ror.org/03hx84x94 grid.448543.a 0000 0004 0369 6517 Department of Field Crops, Faculty of Agriculture, Bingol University, 12000 Bingöl, Turkey
3 Bahri Dagdas International Agriculture Research Institute, 42000 Konya, Turkey
4 https://ror.org/03hx84x94 grid.448543.a 0000 0004 0369 6517 Department of Biosystems Engineering, Faculty of Agriculture, Bingol University, 12000 Bingöl, Turkey
13 9 2024
13 9 2024
2024
14 213967 6 2024
10 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The purpose of this study was to ascertain the fresh herbage yield, fertilizer dosage, and plant characteristics of the Sorghum-Sudangrass hybrid grown in arid and semi-arid regions, as well as their interrelationships. For this reason, data from the Sorghum-Sudangrass hybrid were used to assess the predictive performance of several data mining techniques, including CHAID, CART, MARS, and Bagging MARS. Plant traits were measured in Konya and Sanliurfa during 2021 and 2022. The descriptive statistical values were calculated as follows: plant height 306.27 cm, stem diameter 9.47 mm, fresh herbage yield 10852.51 kg/da, crude protein ratio 9.66%, acid detergent fiber 33.39%, neutral detergent fiber 51.85%, acid detergent lignin 9.76%, dry matter digestibility 62.88%, dry matter intake 2.34%, and relative feed value 114.68 (average values). The predictive capacities of the fitted models were assessed using model fit statistics such as the coefficient of determination (R²), adjusted R², root mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), and Akaike Information Criterion (AIC). With the lowest values for RMSE, MAPE, SD ratio, and AIC (246, 1.926, 0.085, and 845, respectively), and the highest R² value (0.993) and adjusted R² value (0.989), the MARS algorithm was determined to be the best model for characterizing fresh herbage yield. As a solid alternative to other data mining techniques, the MARS algorithm was shown to be the most appropriate model for forecasting fresh herbage production.

Keywords

Data mining algorithms
Fertilizer dose
Fresh herbage yield
Sorghum-sudangrass hybrid
Subject terms

Plant sciences
Statistics
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Sorghum-Sudangrass hybrid was developed by crossing Sorghum (Sorghum bicolor (L.) Moench) and Sudangrass (Sorghum sudanense (Piper) Stapf) species belonging to the family Poaceae sp1,2. Sorghum-Sudangrass hybrids are a prominent species among warm climate crops in terms of high biomass yield, drought tolerance, and resistance to some abiotic stress conditions. Sorghum is a warm climate plant that ranks fifth among the world cereals after wheat, rice, maize, and barley in terms of cultivation area and production3–5.

An annual warm-season fodder, Sorghum-Sudangrass hybrid is a hybrid that confers benefits in terms of soil and water conservation. It offers significant heterosis by combining the benefits of both parents’ high grass and big leaf output, enhanced tillering, excellent regeneration, and high nutritional content. This hybrid is fed to cattle, sheep, geese, ducks, fowl, and other animals. The Sorghum-Sudangrass hybrid exhibits a well-developed root system, strong ecological adaptability, and good soil capacity. It also offers a wide range of development and utilization prospects in animal husbandry and environmental protection, particularly in the expansion of the breeding sector in agricultural areas6–8.

Sorghum and its hybrids give better results than similar species in soils with low plant nutrient content, and it is a plant whose yield increases significantly when fertilization is applied. It has been determined in previous studies that the biomass yield of Sorghum increases significantly, especially when nitrogen fertilization is applied, and that Sorghum uses the applied nitrogen very effectively9.

Using data mining technologies, extensive datasets—whether agronomical, genomic, or meteorological—are transformed into actionable insights, facilitating easier and more effective decision-making processes to enhance the precision and efficiency of farming activities. Data mining can be defined as the process of extracting meaningful information from large datasets, and it is becoming increasingly important in various fields, including agriculture. In the agricultural sector, data mining techniques are used for various purposes, such as predicting crop yields, analyzing soil properties, forecasting diseases, and developing agricultural decision support systems10–17. Data mining methods have been widely used and popularised in recent years to predict plant yield and various characteristics of the plant by considering plant characteristics and to classify plants according to their species18,19.

There are various studies on field crops and other plants using data mining methods. Determination of organic food knowledge level20, classification of fruit types of Onobrychis plant21, disease prediction in paddy plant22, sugarcane yield prediction23, analysis of soil behavior and prediction of crop yield24 and prediction of annual yields of important crops25 are some of them.

This study was conducted over a two-year period in 2021 and 2022 in Konya, a province located in Türkiye’s Central Anatolia Region with a semi-arid climate. The aim of the research was to predict the herbage yield and quality of the Sorghum-Sudangrass hybrid plant using data mining algorithms, based on various plant characteristics and factors affecting herbage yield and quality.

Materials and methods

Study area and data sets

Field studies were conducted in two different locations in Türkiye, Konya and Sanliurfa. Konya which shows semi-arid climate characteristics, is located at an average altitude of 1006 m above sea level and is located at the coordinates 37°51’39 “N 32°33’27 “E. Sanliurfa which has arid climate characteristics, is located at coordinates 36°56’08 “N 40°04’10 “E and 398 m above sea level. As a result of the soil analyses performed in both locations, it was determined that the test areas had salt-free, calcareous, clayey-loamy texture with low organic matter (Table 1).

Table 1 Soil analysis results of the experimental fields.

Location/
Parameters examined	Depth (cm)	Texture	pH	EC (dSm−1)	CaCO3 (%)	Organic matter (%)	P2O5
(kg da−1)	K2O
(kg da−1)	
Konya	0–30	Clay-Loam	7.8	0.77	33.4	1.7	13.5	97.0	
Sanliurfa	0–30	Clay-Loam	7.9	0.02	49.8	2.4	15.0	127.2	

In both locations, the vegetation period is four months in total, from June to September. The minimum, maximum and average temperatures recorded in Konya and Sanliurfa during the vegetation period of 2021 and 2022 are similar to the temperatures in long years. The total rainfall recorded in Konya for 2021 and 2022 is 44.0 and 44.4 mm, respectively, which is lower than the long-term rainfall (52.6). In Sanliurfa, the long-term average total rainfall was 14.5 mm, while the total rainfall during the vegetation period was 7.6 mm in 2021 and no rainfall in 2022 (Table 2).

Table 2 Mean values of meteorological parameters for the growing period of Sorghum-Sudangrass hybrid.

Year	Months	Konya	Sanliurfa	
June	July	Aug	Sep	June	July	Aug	Sep	
(1929–2020)	Tmean (oC)	20.1	23.5	23.3	17.5	28.1	32.0	31.6	27.2	
Tmax (oC)	27.6	31.2	32.9	26.2	34.7	38.8	38.4	34.0	
Tmin (oC)	11.9	16.1	15.4	9.1	20.5	24.3	24.0	20.0	
Precipitation (mm)	25.7	7.0	6.3	13.6	4.3	2.0	3.6	4.6	
2021	Tmean (oC)	19.0	24.8	24.0	17.7	28.9	33.8	32.7	27.3	
Tmax (oC)	27.3	32.2	31.8	25.2	35.9	40.6	39.2	33.9	
Tmin (oC)	11.2	16.6	15.6	9.9	21.9	27.2	26.5	20.9	
Precipitation (mm)	19.5	0.1	8.0	16.4	0.3	0.0	7.3	0.0	
2022	Tmean (oC)	20.8	22.6	24.8	19.5	29.6	33.4	31.8	29.1	
Tmax (oC)	28.6	29.9	33.5	28.9	35.9	39.7	38.7	35.1	
Tmin (oC)	13.1	15.1	15.2	9.3	23.1	27.0	25.3	23.1	
Precipitation (mm)	28.4	1.6	5.6	8.8	0.0	0.0	0.0	0.0	

Experimental details

Nutrima variety was used as material. The field experiment was designed according to the randomized block design with three replicates. Each plot has four rows, the distance between rows is 45 cm and the row length is five m. Before sowing, a compound fertilizer containing nitrogen, phosphorus, and potassium was applied as a basal fertilizer at a rate of 10 kg per decare. N fertilizer was applied at six rates (N0: 0 kg/da, N5: 5 kg/da, N10: 10 kg/da, N15: 15 kg/da, N20: 20 kg/da, N25: 25 kg/da) when the plant height was 40–50 cm. Sowing was done in the first week of June and harvesting was done in the last week of September. Each plot was mown and weighed. Then, wet grass yield was determined by converting to decare. Ten plants were randomly selected from the mowed plants and analyzed. Crude protein ratio (CP), acid detergent fiber (ADF) and neutral detergent fiber (NDF) ratios were determined by NIRS device. Dry matter digestible (DMD = 88.9 - (0.779 x %ADF))26, dry matter intake (DMI = 120 / (%NDF)) and relative feed value (RFV = (DDM x DMI) / 1.29)27 were calculated from the determined ADF and NDF.

Data mining

Chi-square automatic interaction detection (CHAID)

Using the classification tree methodology known as the CHAID method, the sample is initially split into at least two subgroups based on the most significant risk factor (the one with the highest Chi-square value). This process is then repeated, with each subgroup being further divided by the next most significant risk factor. At each stage of the analysis, the most important variable is selected in a stepwise manner, continuing until no more significant risk variables remain28. In other words, a node is split if it has a statistically significant P-value; if not, it is considered a terminal node. P-values less than 0.05 were regarded as statistically significant29.

Classification and regression trees (CART)

When the class to which the data belongs is the expected result, it is known as classification tree analysis. When the expected result may be regarded as a real number, regression tree analysis is used30,31.

CART constructs the tree by recursively dividing the variable space based on the impurity of the variables, continuing this process until the termination condition is met. The Gini impurity measures how often a randomly selected element from the set would be incorrectly labeled if its label were assigned randomly according to the label distribution within the subset. A pseudo technique is as follows32:

Step 1: First, begin with the root node (t = 1).

Step 2: Look for a split s* that provides the cleanest decrease in impurity within the set of all potential possibilities.

Step 3: Use the split s* to divide node 1 (t = 1) into two nodes (t = 2, t = 3).

Step 4: Until the tree growth rules are satisfied, carry out the split search procedure again (t = 2, t = 3) as described in stages 1–3.

Multivariate adaptive regression spline (MARS)

Friedman proposed the multivariate adaptive regression spline (MARS) as a nonparametric regression technique in 199133,34.

A regression double can typically be reported as (Xi, Yi), where Yi is the dependent variable and Xi is one or more independent variable(s). According to the MARS model, there exists one or more split points, denoted as ti, for each independent variable. There are two equations: the left side-basis function equation for Xi < ti and the right side-basis function (BF) equation for Xi ≥ ti. The following Eqs. 1 and 2 show how the left and right basis functions are represented mathematically35:1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\left[-\left({X}_{i}-{t}_{i}\right)\right]}_{+}^{q}=\left\{\begin{array}{c}{\left({t}_{i}-{X}_{i}\right)}^{q}\:\:\:\:If\:{X}_{i}<{t}_{i}\\\:0\:\:\:\:\:\:otherwise\end{array}\right.$$\end{document}

2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\left[+\left({X}_{i}-{t}_{i}\right)\right]}_{+}^{q}=\left\{\begin{array}{c}{\left({X}_{i}-{t}_{i}\right)}^{q}\:\:\:\:If\:{X}_{i}\ge\:{t}_{i}\\\:0\:\:\:\:\:\:otherwise\end{array}\right.$$\end{document}

where the degree of smoothness of the outcome function estimate is defined by q (≥ 0), the power to which the splines are raised.

The approximated MARS function in a MARS method is made up of a linear combination of basic functions that are determined by hinge functions or by taking a product of hinge functions. This is one way to write the MARS model36.

The following formula represents the overfitted MARS model, which is significantly larger than the ideal model and is created by adding specific basis functions Eq. 3 under the conditions33:3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:f\left(x\right)={a}_{0}+\sum\:_{m=1}^{M}{a}_{m}{B}_{m}\left(x\right)={a}_{0}+\sum\:_{m=1}^{M}{a}_{m}\prod\:_{k=1}^{{K}_{m}}\left[{b}_{km}\left({X}_{v(k,m)}-{t}_{km}\right)\right]\:$$\end{document}

The number of basis functions in this equation, M, is given by =1,2,.,.M The number of interactions is represented by the quantity Km, while the value of bkm is ± 1. The regression coefficients are represented by am, and the constant term in the model is marked by a0. The m-th basis function is (x). The independent variables are labeled by the v(k,m), and the tkm displays the knot value on the relevant variables33.

The knots for the basis function are the refraction spots in each basis function. For \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{b}_{km}$$\end{document}, truncated linear functions of the following kind represent the simplest form Eqs. 4 and 5:4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:b(x/t)={\left[+\left(x-t\right)\right]}_{+}=max\left\{+\left(x-t\right),0\right\}$$\end{document}

or.

5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:b(x/t)={\left[-\left(x-t\right)\right]}_{+}=max\left\{-\left(x-t\right),0\right\}$$\end{document}

where the location t is called the knot of the basis function.

MARS applies the Generalized Cross-Validation (GCV) criteria to remove duplicate BFs. The following is how GCV Eq. 6 is expressed37.6 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:GCV=\frac{\frac{1}{N}\sum\:_{i=1}^{n}{\left[{y}_{i}-f{\prime\:}\left({x}_{i}\right)\right]}^{2}}{{\left[1-\frac{C\left(B\right)}{N}\right]}^{2}}\:$$\end{document}

Based on the lowest GCV, the current MARS prediction model with interaction term was created38. In the MARS modeling, a ten-fold cross validation was taken into consideration as a resampling strategy.

The total number of points in the data is denoted by N, and the complexity penalty C(B), which rises in proportion to the number of BFs in the model Eq. 7 is found as39.

7 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:C(B) = (B+1)+d(B)\:$$\end{document}

In this case, d is the penalty value applied to find the optimal knot value. Another name for d is Degrees of Freedom. According to Friedman (1991), the 2 ≤ d ≤ 4 range is the most appropriate penalty value.

Bootstrap aggregating multivariate adaptive regression splines (bagging MARS)

The bagging algorithm for MARS modeling is as follows40.

Create a bootstrap sample set of consisting of {(y_i, x_i ),i = 1,2,…,n} and replicate the data to obtain and replicate the data to obtain £_i*=(y_i*,x_i*),i = 1,2,…n or termed (£(B)).

Perform MARS modeling on (£(B)).

Predict the response variable using the MARS model that was generated.

Repeat steps 1–3 until bootstrap replication is complete.

Make predictions on the response variable using a set of predictions that frequently appear on each observation using bootstrap replication (majority vote).

Calculate the bagging MARS model’s prediction classification accuracy.

Goodness of fit criteria

To compare the prediction performance of the CHAID, CART, MARS, and Bagging MARS algorithms in 10 cross-validations, the following goodness of fit criteria were determined41,42:

Pearson correlation coefficient (r) between the actual and predicted BW values,

Akaike information criterion (AIC) calculated as Eq. (8): 8 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:AIC=nln\left[\frac{1}{n}{\left({y}_{i}-{y}_{ip}\right)}^{2}\right]+2k$$\end{document}

Root-mean-square error (RMSE) given by the following formula Eq.(9): 9 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:RMSE=\sqrt{\frac{1}{n}\sum\:_{i=1}^{n}{({y}_{i}-{y}_{ip})}^{2}}$$\end{document}

Standard deviation ratio (SDratio) Eq. (10): 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{SD}_{ratio}=\frac{{s}_{m}}{{s}_{d}}$$\end{document}

Mean absolute percentage error (MAPE) Eq. (11): 11 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:MAPE=\frac{1}{n}\sum\:_{i=1}^{n}\left|\frac{{y}_{i}-{y}_{ip}}{{y}_{i}}\right|0.100$$\end{document}

Coefficient of determination Eq. (12): 12 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{R}^{2}=1-\frac{\sum\:_{i=1}^{n}{({y}_{i}-{y}_{ip})}^{2}}{\sum\:_{i=1}^{n}{({y}_{i}-{\stackrel{-}{y}}_{i})}^{2}}$$\end{document}

Adjusted coefficient of determination Eq. (13): 13 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Adj.R}^{2}=1-\frac{\frac{1}{n-k-1}\sum\:_{i=1}^{n}{({y}_{i}-{y}_{ip})}^{2}}{\frac{1}{n-1}\sum\:_{i=1}^{n}{({y}_{i}-{\stackrel{-}{y}}_{i})}^{2}}$$\end{document}

where: n is the number of cases in a set, k is the number of model parameters, yi is the actual (observed) value of an output variable (FHY), yip is the predicted value of an output variable (FHY), sm is the standard deviation of model errors, sd is the standard deviation of an output variable (FHY).

The adjusted coefficient of determination, as a goodness-of-fit criterion, can be used for algorithms with different numbers of inputs. This metric helps to account for differences in input variables. In the MARS algorithm, ‘k’ is defined as the number of terms.

Statistical evaluations for the CHAID and CART algorithms were conducted using IBM SPSS 26, while the MARS and Bagging MARS algorithms were specified and analyzed using the R Studio program.

Results

Descriptive statistics, including the plant characteristics, of Sorghum-Sudangrass hybrid in 2021 and 2022 years bred in two different cities in Türkiye are given in Table 3.

Table 3 Descriptive statistics regarding plant characteristics of Sorghum-Sudangrass hybrid.

PH (cm)	
Fertilizer (kg/da)	0	5	10	15	20	25	Total	
N	12	12	12	12	12	12	72	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{X}$$\end{document}	276.667	284.083	301.417	319.917	321.833	333.75	306.278	
s	46.742	36.771	33.435	35.38	17.979	18.42	38.119	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{s}_{\stackrel{-}{x}}$$\end{document}	13.493	10.615	9.652	10.213	5.19	5.317	4.492	
SD (mm)	
Fertilizer	0	5	10	15	20	25	Total	
N	12	12	12	12	12	12	72	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{X}$$\end{document}	8.258	8.742	8.908	9.408	10.925	10.6	9.474	
s	4.067	4.048	3.21	2.889	3.497	3.124	3.516	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{s}_{\stackrel{-}{x}}$$\end{document}	1.174	1.169	0.927	0.834	1.009	0.902	0.414	
FHY (kg/da)	
Fertilizer	0	5	10	15	20	25	Total	
N	12	12	12	12	12	12	72	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{X}$$\end{document}	9338.25	9894.917	11114.58	10821.92	11715.33	12230.08	10852.51	
s	4117.168	3491.431	2831.147	1335.598	2003.822	2389.042	2922.367	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{s}_{\stackrel{-}{x}}$$\end{document}	1188.524	1007.889	817.282	385.554	578.454	689.657	344.404	
CP (%)	
Fertilizer	0	5	10	15	20	25	Total	
N	12	12	12	12	12	12	72	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{X}$$\end{document}	9.3	9.842	9.417	9.25	10.833	9.367	9.668	
s	0.924	1.031	0.821	1.018	1.085	1.001	1.101	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{s}_{\stackrel{-}{x}}$$\end{document}	0.267	0.298	0.237	0.294	0.313	0.289	0.13	
ADF (%)	
Fertilizer	0	5	10	15	20	25	Total	
N	12	12	12	12	12	12	72	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{X}$$\end{document}	30.658	31.225	34.883	36.508	31.258	35.842	33.396	
s	2.122	2.622	1.473	1.257	1.404	1.561	2.981	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{s}_{\stackrel{-}{x}}$$\end{document}	0.613	0.757	0.425	0.363	0.405	0.45	0.351	
NDF (%)	
Fertilizer	0	5	10	15	20	25	Total	
N	12	12	12	12	12	12	72	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{X}$$\end{document}	46.683	54.467	55.367	55.833	42.642	56.108	51.85	
s	2.501	3.009	2.603	1.726	1.626	2.909	5.783	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{s}_{\stackrel{-}{x}}$$\end{document}	0.722	0.869	0.751	0.498	0.469	0.84	0.682	
ADL (%)	
Fertilizer	0	5	10	15	20	25	Total	
N	12	12	12	12	12	12	72	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{X}$$\end{document}	9.783	9.242	9.242	9.925	10.275	10.15	9.769	
s	0.266	1.299	0.795	0.993	1.055	0.595	0.957	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{s}_{\stackrel{-}{x}}$$\end{document}	0.077	0.375	0.229	0.287	0.305	0.172	0.113	
DMD (%)	
Fertilizer	0	5	10	15	20	25	Total	
N	12	12	12	12	12	12	72	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{X}$$\end{document}	65.017	64.583	61.733	60.458	64.55	60.967	62.885	
s	1.651	2.036	1.134	0.991	1.1	1.223	2.324	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{s}_{\stackrel{-}{x}}$$\end{document}	0.477	0.588	0.327	0.286	0.318	0.353	0.274	
DMI (%)	
Fertilizer	0	5	10	15	20	25	Total	
N	12	12	12	12	12	12	72	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{X}$$\end{document}	2.575	2.217	2.167	2.15	2.808	2.133	2.342	
s	0.154	0.127	0.098	0.052	0.108	0.115	0.282	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{s}_{\stackrel{-}{x}}$$\end{document}	0.045	0.037	0.028	0.015	0.031	0.033	0.033	
RFV	
Fertilizer	0	5	10	15	20	25	Total	
N	12	12	12	12	12	12	72	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{X}$$\end{document}	130.067	110.742	104	100.842	141.083	101.4	114.689	
s	10.312	8.95	6.496	4.274	7.386	6.852	17.202	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{s}_{\stackrel{-}{x}}$$\end{document}	2.977	2.584	1.875	1.234	2.132	1.978	2.027	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{X}$$\end{document}: Mean, s: Standard deviation, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{s}_{\stackrel{-}{x}}$$\end{document} : Standard error. FHY: Fresh herbage yield, PH: Plant height, SD: Stem diameter, CPR: Crude Protein Ratio, ADF: Acid detergent fiber, NDF: Neutral detergent fiber, ADL: Acid detergent lignin, DMD: Dry matter digestibility, DMI: Dry matter intake, RFV: Relative feed value.

The correlation coefficients for the plant characteristics of the Sorghum-Sudangrass hybrid are presented in Fig. 1.

Fig. 1 Correlation coefficients between plant characteristics.

CHAID Algorithm

The CHAID algorithm’s parent node/child node ratio was adjusted to 12:6 to assess the impact of various factors on fresh herbage yield. Figure 2 shows a schematic of the regression tree produced by the CHAID method. The number of folds in the cross-validation was set at 10.

Fig. 2 CHAID regression tree diagram for fresh herbage yield estimation.

When Fig. 1 is analysed, the first variable affecting the fresh herbage yield (FHY) (kg/da) is the SD variable and it is divided into 5 nodes. These are SD ≤ 4.600, 4.600 < SD ≤ 5.900, 5.900 < SD ≤ 6.500, 6.500 < SD ≤ 8.300 and SD > 8.300. These are SD ≤ 4.600, 4.600 < SD ≤ 5.900, 5.900 < SD ≤ 6.500, 6.500 < SD ≤ 8.300 and SD > 8.300. 5900 < SD ≤ 6500 (node 4) recorded 10195.073 kg and 9744.286 kg wet grass yield in 2021 and 2022, respectively. The highest mean FHY value was 13329.167 kg with SD > 8.300.

CART algorithm

The parent node/child node ratio of the CART algorithm was adjusted to 12:6 to evaluate the influence of different parameters on fresh herbage yield. A schematic of the regression tree created by the CART method is displayed in Fig. 3.

Fig. 3 CART regression tree diagram for fresh herbage yield estimation.

As seen in Fig. 2, the FHY node (Node 0) is divided into two nodes with the SD variable. SD ≤ 9.3 and SD > 9.3. SD ≤ 9.3 (Node 1) was divided into SD ≤ 5.6 and SD > 5.6. The SD > 5.6 node is divided into SD ≤ 6.25 and SD > 6.25. FHY = 6082 kg when SD ≤ 6.25 and FHY = 9886.056 kg when SD > 6.25. When SD > 9.3 and ADF ≤ 31.8, FHY = 14386.778 kg, while when SD > 9.3, ADF > 31.8 and dose > 22.5, FHY = 14444.5 kg. When ADF > 31.8, dose ≤ 22.5 and dose > 12.5, FHY = 12057.556 kg. When ADF > 31.8, dose ≤ 22.5, dose ≤ 12.5 and dose ≤ 7.5, FHY = 12067.833 kg. In case of ADF > 31.8, dose ≤ 22.5, dose ≤ 12.5 and dose > 7.5, FHY = 13796.167 kg.

The maximum number of basic functions in the MARS modelling was defined at the first step as 26 and the MARS model was constructed by using an interaction order of 5.

It was performed − 1 penalty and 10 three-fold cross-validation definitions in the package `earth` of R studio free software to improve the predictive accuracy of the MARS algorithm.

MARS prediction equation was achieved at the smallest estimates of GCV which is calculated as the ratio of RSS to (sample size) for penalty = -1.

As a non-parametric regression method, MARS is a remarkable statistical tool that, without requiring any assumptions about the distribution of the predictors, calculates the appropriate cut-off values (FHY values) for the influential variables and their interactions. Therefore, it can be concluded that the MARS algorithm’s process, which in this case yields a predicted accuracy of 100% or nearly 100%, provides researchers with valuable insights for identifying predictors that influence FHY parameters.

As a result of the MARS algorithm, the variables with the highest positive effect on the FHY dependent variable are 5011 * max (0, ADL − 9.8), 4469*max (0, 9.8 - ADL), 4397*province Sanliurfa, 2613 * max (0, ADF − 33.2) and 964 * max (0, NDF − 49), respectively. According to this information, when ADL > 9.8, FHR increases by 5011 kg, while when ADL ≤ 9.8, FHR increases by 4469 kg. In this case, ADL should be > 9.8 to obtain more FHY. Fresh herbage yield per decare (FHY) of Sorghum-Sudangrass hybrid grown in Sanliurfa was 4397 kg higher than that of Sorghum-Sudangrass hybrid grown in Konya. When ADF > 33.2, FHY increases by 2613 kg. When NDF > 49, there was an increase of 964 kg in FHY.

Std. Error, t and p values including the parameter coefficients for the MARS algorithm are presented in Table 4.

Table 4 Results of MARS algorithm in the prediction of FHY of Sorghum-Sudangrass hybrid.

Coefficients	Estimate	Std. Error	t value	Pr(>|t|)	
(Intercept)	1.901e + 06	3.829e + 05	4.965	9.87e-06***	
bx[, -1]h(SD-11.3)	1.150e + 02	1.034e + 02	1.112	0.271812	
bx[, -1]h(11.3-SD)	-5.045e + 02	1.125e + 02	-4.485	4.83e-05 ***	
bx[, -1]h(PH-320)	-6.421e + 01	1.298e + 01	-4.946	1.05e-05 ***	
bx[, -1]h(320-PH)	-1.166e + 02	5.546e + 01	-2.102	0.041047 *	
bx[, -1]h(PH-320)*dose	2.675e + 00	7.637e-01	3.503	0.001037 **	
bx[, -1]h(ADL-9.8)	5.011e + 03	1.104e + 03	4.538	4.07e-05 ***	
bx[, -1]h(9.8-ADL)	4.469e + 03	03 1.251e + 03	3.572	0.000844 ***	
bx[, -1]h(dose-10)	-3.185e + 02	1.908e + 02	-1.670	0.101753	
bx[, -1]h(10-dose)	-3.554e + 02	2.675e + 01	-13.287	< 2e-16 ***	
bx[, -1]h(ADF-33.2)	2.613e + 03	4.382e + 02	5.964	3.28e-07 ***	
bx[, -1]h(33.2-ADF)	-2.732e + 02	2.398e + 02	-1.139	0.260479	
bx[, -1]year	-9.399e + 02	1.900e + 02	-4.948	1.05e-05 ***	
bx[, -1]h(NDF-49)	9.640e + 02	3.393e + 02	2.842	0.006671 **	
bx[, -1]h(49-NDF)	-4.164e + 03	5.622e + 02	-7.407	2.24e-09 ***	
bx[, -1]h(RFV-99.3)	5.201e + 02	1.746e + 02	2.978	0.004617 **	
bx[, -1]h(99.3-RFV)	-5.495e + 02	2.206e + 02	-2.491	0.016400 *	
bx[, -1]ADL*h(dose-10)	3.739e + 01	1.968e + 01	1.900	0.063684 .	
bx[, -1]h(320-PH)*RFV	-9.440e-02	2.078e-01	-0.454	0.651704	
bx[, -1]provincSanliurfa	4.397e + 03	5.097e + 02	8.626	3.60e-11 ***	
bx[, -1]CPR*h(ADF-33.2)	-2.141e + 02	4.048e + 01	-5.290	3.30e-06 ***	
bx[, -1]CPR*h(ADL-9.8)	-6.706e + 02	1.126e + 02	-5.954	3.39e-07 ***	
bx[, -1]PH*h(9.8-ADL)	-1.645e + 01	4.046e + 00	-4.066	0.000185 ***	
bx[, -1]h(49-NDF)*ADL	1.671e + 02	3.322e + 01	5.031	7.91e-06 ***	
bx[, -1]h(320-PH)*ADF	3.979e + 00	1.053e + 00	3.778	0.000453 ***	
bx[, -1]h(RFV-117.6)	2.056e + 02	6.930e + 01	2.967	0.004755 **	

The equation for FHY, which incorporates the interaction effects of the model’s coefficients, is detailed below.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{l}{\rm{FHY = 1}}.{\rm{9e + 06 + 4397*province}}{\mkern 1mu} {\rm{Sanliurfa,\,}} {\rm{ - 940*year - 117*max}}({\rm{0}},{\rm{320 - PH}}) \\\quad \quad \quad{\rm{ - 64}}.{\rm{2*max}}({\rm{0}},{\rm{PH - 320}}) {\rm{ - 505*max}}({\rm{0}},{\rm{11}}.{\rm{3 - SD}}){\rm{ + 115*max}}({\rm{0}},{\rm{SD - 11}}.{\rm{3}}) \\\quad \quad \quad {\rm{ - 273*max}}({\rm{0}},{\rm{33}}.{\rm{2 - ADF}}) {\rm{ + 2613*max}}({\rm{0}},{\rm{ADF - 33}}.{\rm{2}}) {\rm{ - 4164*max}}({\rm{0}},{\rm{49 - NDF}}) \\\quad \quad \quad {\rm{ + 964*max}}({\rm{0}},{\rm{NDF - 49}} {\rm{ + 4469*max}}({\rm{0}},{\rm{9}}.{\rm{8 - ADL}}) {\rm{ + 5011*max}}({\rm{0}},{\rm{ADL - 9}}.{\rm{8}})\\\quad \quad \quad {\rm{ - 549*max}}({\rm{0}},{\rm{99}}.{\rm{3 - RFV}}) {\rm{ + 520*max}}({\rm{0}},{\rm{RFV - 99}}.{\rm{3}}) {\rm{ + 206*max}}({\rm{0}},{\rm{RFV - 118}})\\\quad \quad \quad {\rm{ - 355*max}}({\rm{0}},{\rm{10 - dose}}) {\rm{ - 319*max}}({\rm{0}},{\rm{dose - 10}}) {\rm{ + 3}}.{\rm{98*max}}({\rm{0}},{\rm{320 - PH}}){\rm{*ADF}}\\\quad \quad \quad {\rm{ - 16}}.{\rm{4*PH*max}}({\rm{0}},{\rm{9}}.{\rm{8 - ADL}}) {\rm{ - 0}}.{\rm{0944*max}}({\rm{0}},{\rm{320 - PH}}){\rm{*RFV}}\\\quad \quad \quad {\rm{ + 2}}.{\rm{68*max}}({\rm{0}},{\rm{PH - 320}}){\rm{*dose}} {\rm{ - 214*CPR*max}}({\rm{0}},{\rm{ADF - 33}}.{\rm{2}})\\\quad \quad \quad {\rm{ - 671*CPR*max}}({\rm{0}},{\rm{ADL - 9}}.{\rm{8}}) {\rm{ + 167*max}}({\rm{0}},{\rm{49 - NDF}}){\rm{*ADL}} {\rm{ + 37}}.{\rm{4*ADL*max}}({\rm{0}},{\rm{dose - 10}})\end{array}$$\end{document}

In Fig. 4, the observed values and the estimated values produced by the MARS algorithm are displayed. It can be seen that the actual values and estimated values are closely aligned, indicating compatibility. The MARS algorithm identified the proportional relevance of the variables that predict fresh herbage yield (FHY) (Fig. 5).

Fig. 4 The agreement between the actual FHY values and the MARS predictions.

Fig. 5 Graph of relative importance (for FHY).

When analyzing Fig. 5, the variables contributing the most to FHY are shown in terms of their relative significance. The variables that made the largest contributions are stem diameter (SD), crude protein ratio (CPR), plant height (PH), acid detergent fiber (ADF), dry matter digestibility (DMD), acid detergent lignin (ADL), fertilizer dose, relative feed value (RFV), neutral detergent fiber (NDF), and dry matter intake (DMI). Among these, SD, CPR, and PH were the most significant contributors. ADF and DMD variables were equally significant. The variable with the smallest contribution was DMI.

It is also feasible to estimate fresh herbage yield by assigning different values to the plant attributes that represent the independent variables in the MARS algorithm equation. For example, when PH = 300, SD = 10, CPR = 9, ADF = 32, NDF = 50, ADL = 8.5, DMD = 61, DMI = 2.3, RFV = 120, dose = 15, year = 2022, and province="Konya”, FHY = 11363.07 kg can be achieved.

Includes the Bagging MARS algorithm’s comprehensive results and prediction equation.

The Prediction Equation Results for the Bagging MARS Algorithm for FHY.

FHY= (20689.34-9324.099 * Konya province − 2192.739 * h(SD-6.9) − 751.6922 * h(10.9-SD) + 1295644 * h(SD-10.9) + 4973.741 * h(9.8-ADL) + 4802.771 * h(ADL-9.8) + 55.14607 * h(RFV-104.8) + 42.90222 * h(126.7-RFV) − 270.5514 * h(10-dose) − 130913.9 * h(dose-10) − 639.8304 * h(SD-10.9)*year − 615.7416 * CPR*h(ADL-9.8) − 69.80065 * NDF*h(9.8-ADL) − 138.3531 * h(9.8-ADL)*dose + 169.2941 * h(10-dose)* Sanliurfa province + 64.82691 * h(dose-10)*year + 3,134,002 − 1544.348 * year + 203.6301 * h(340-PH) + 106.3064 * h(PH-340) − 2672.91 * h(6.9-SD) − 1036.498 * h(9.4-CPR) − 15301.13 * h(CPR-9.4) + 16364.9 * h(ADL-10.2) − 43.01511 * PH*h(ADL-10.2) − 2.674476 * h(340-PH)*DMD − 915.1036 * SD*h(10.2-ADL) + 226.1718 * h(SD-6.9)*DMI + 25.36655 * h(SD-6.9)*dose + 229.3569 * h(CPR-9.4)*DMD + 5981.118 * h(10.2-ADL)* Sanliurfa province + 2.356236 * h(9.8-ADL)*year − 3.311797 * h(ADL-9.8)*year + 1,480,017 − 721.7671 * year − 52.82158 * h(PH-325) − 996.2647 * h(SD-7.1) − 3266.265 * h(11-SD) − 392.2808 * h(CPR-9.1) − 1269.662 * h(ADL-9.8) − 134.3266 * h(99.7-RFV) − 121.9365 * h(15-dose) − 27.86617 * PH*h(9.8-ADL) + 36.52813 * h(SD-11)*dose + 1.118935 * h(8.2-SD)*year + 762.8022 * CPR*h(9.8-ADL))/3.

According to the results of the Bagging MARS algorithm, in the first bootstrap, an increase in fresh herbage yield (FHY) can be expected for cases with stem diameter (SD) > 10.9 cm, acid detergent lignin (ADL) > 9.8 cm, relative feed value (RFV) > 104.8 cm, RFV ≤ 126.7, and fertilizer dose ≤ 10, particularly in the Sanliurfa province and during the years 2021 and 2022, where the dose was > 10.

In the second bootstrap, an increase in FHY can be anticipated for cases with plant height (PH) > 340 cm, ADL > 10.2 cm, SD > 6.9 cm, crude protein ratio (CPR) > 9.4, ADL ≤ 10.2, especially in the Sanliurfa province during 2021 and 2022, where ADL was ≤ 9.8.

In the third bootstrap, an increase in FHY can be expected for cases with SD > 11 cm and the fertilizer dose, SD ≤ 8.2 cm and years, ADL ≤ 9.8, and CPR.

Figure 6 displays a graph comparing the estimated values derived by the Bagging MARS algorithm with the observed values (FHY). Table 5 presents the goodness-of-fit criteria findings. All of the algorithms provided fairly accurate FHY predictions. MARS was shown to be the superior algorithm in terms of predictive accuracy, followed by Bagging MARS, CART, and CHAID.

Fig. 6 The consistency of projected and real FHY values.

Table 5 Predictive performance of MARS, Bagging MARS, CART and CHAID types.

	CHAID	CART	MARS	Bagging MARS	
r	0.946	0.985	0.996	0.986	
R2	0.895	0.970	0.993	0.971	
Adj. R2	0.893	0.953	0.989	0.954	
RMSE	938.811	498.001	246	496.585	
MAPE	7.295	4.035	1.926	3.586	
SDratio	0.324	0.170	0.085	0.171	
AIC	1369	1241	845	946	

Discussion

Due to the lack of data mining studies on the effect of plant characteristics on herbage yield, a detailed discussion could not be made. However, the results of other studies using statistical methods to examine the impact of various factors on grass yield could be discussed. For example, a study found that as plant maturity advanced in four forage Sorghum cultivars (Early Sumac, Leotti, Nes, and Rox), the leaf proportion, protein content, NDF, ADF, cellulose content, and hemicellulose content tended to decrease. On the other hand, factors such as plant height, dry matter, fresh forage yield, panicle proportion, protein yield, lignin content, and RFV tended to increase with advanced maturity43.

In a different study, principal component analysis indicated that Sorghum fodder production and quality increased with plant maturity. The study suggested that harvesting forage between the grain soft dough and grain hard dough stages might be advisable for maximizing both forage production and quality44.

A study by45 investigated the relationships between ADF and NDF ratios, RFV, ME (Metabolic Energy) values, and other characteristics, such as plant height, panicle height, leaf ratio, stem ratio, panicle ratio, green grass yield, hay yield, and crude protein yields of Sorghum-Sudangrass hybrid varieties. The results showed a strong correlation between crude protein ratio and the following: leaf ratio with ADF, NDF, RFV, and ME; ADF with NDF; RFV with leaf ratio, ADF, and NDF; and ME with leaf ratio, ADF, NDF, and RFV. However, the results of this study differed, as ADF, NDF, and RFV variables showed a low correlation with the crude protein ratio.

In another study, the dry herbage yields of Sorghum-Sudangrass hybrid pastures were found to be 10.0 tons/ha. Regarding the herbage quality of the Sorghum-Sudangrass hybrid pasture, crude protein was 11.33%, NDF was 61.13%, ME was 2.43 Kcal/kg KM, ADF was 31.58%, ADL was 2.99%, and DMD was 67.27%46.

47 revealed a significant positive association between green herbage yield (GHY) and ADF and ash. Dry matter content (DMC) was significantly and positively correlated with crude protein (CP) and digestible organic matter (DOM), while it showed a strong negative correlation with ash, NDF, and ADF. ADF exhibited a significant positive correlation with NDF and ash, while showing a notable negative correlation with CP and DOM. There was also a significant inverse relationship between NDF and CP and DOM.

In another study, trials for the primary crop, Hungarian vetch (Vicia pannonica Crantz.), and the second crop, hybrid cultivars of Sorghum and Sudangrass, were set up using a randomized complete block design with four replications. The second crops were sown and harvested during the starch stage following the main crop’s harvest. Statistically significant differences among the varieties were negligible across the growing years. The average herbage yield for the two years ranged from 4.840 to 7.772 kg/ha, with Pioneer Corn having the maximum herbage yield at 7.772 kg/ha. The RFV of the Sorghum and maize cultivars ranged from 93.12 to 125.1748.

Biplot analysis was used in a study to evaluate the potential silage of stay-green Sorghum genotypes. Green herbage yields ranged from 13.40 to 65.96 t/ha, pH ranged from 3.92 to 4.25, dry matter ratios from 24.26 to 35.83%, crude protein ratios from 3.44 to 7.03%, ADF ratios from 27.46 to 52.01%, NDF ratios from 40.80 to 69.12%, and crude ash ratios from 5.89 to 15.14%. Biplot analysis revealed positive correlations between acetic, butyric, and propionic acids, pH, ash, and protein levels, as well as methane, ME (metabolic energy), OMD (organic matter digestibility), and gas-methane production. Negative associations were found between gas production, ADF, NDF, herbage yield, crude protein, organic acids, pH, and crude ash49.

In a different study, the number of tillers and plant height showed a positive and statistically significant correlation with fresh herbage yield (r = 0.998** and 0.613**, respectively). Conversely, stem diameter was negatively correlated with fresh herbage yield (r=-0.010). Additionally, stem diameter and plant height had a negligible positive correlation (r = 0.197)50. These findings differ from the results of the present study.

In a study, a system based on machine learning algorithms (TensorFlow with Convolutional Neural Networks and Linear Regression) was used to estimate farm yields on a Sorghum field. These algorithms enabled the detection of different Sorghum ears in an image and the estimation of their weight. On the dataset, an average accuracy of 74.5% was achieved for Sorghum detection, and an average precision of 99% was obtained for weight estimation51. Different findings were obtained from this study using various statistical methods.

In the study by52, an Artificial Neural Network (ANN) modeling was applied to estimate the effects of different irrigation water levels and fertilizer doses on the forage yield and some quality characteristics of the Sorghum-Sudangrass hybrid. The results showed that irrigation and fertilization were significant in terms of yield and quality characteristics. Yield increased depending on the irrigation and fertilization dose. The highest Relative Feed Value (RFV) was obtained from the I30 × N150 (92.17) interaction. These results partially resemble the findings of the present study.

There may be differences between the results obtained from similar studies. This may be due to the use of different statistical methods, as well as cultivation and ecological conditions in different regions.

Conclusion

This study examined the efficacy of the CHAID, CART, MARS, and Bagging MARS techniques for predicting the fresh herbage yield of a Sorghum-Sudangrass hybrid. Plant characteristics, years, different fertilizer doses, and locations were used in constructing the models. The outcomes were compared using several comparative statistics, including the standard deviation ratio (SD ratio), root mean squared error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R²), and adjusted R². The results of the MARS algorithm indicate that fourteen predictor variables—plant height, acid detergent fiber, acid detergent lignin, neutral detergent fiber, relative feed value, crude protein ratio, dry matter intake, dry matter digestibility, stem diameter, fertilizer dose, year, and province—affect the fresh herbage yield in the Sorghum-Sudangrass hybrid. The variables contributing the most to fresh herbage yield are stem diameter, crude protein ratio, and plant height, respectively. As Bagging MARS is a method used to increase the predictive accuracy of the MARS model, the number of bootstrap samples was set to three. However, when predicting the fresh herbage yield of the Sorghum-Sudangrass hybrid, the MARS algorithm outperformed the others. The factors determining fresh herbage yield in Sorghum-Sudangrass hybrid were ranked as follows: SD and year for the CHAID algorithm; SD, ADF, and dose for the CART algorithm; and SD, CPR, and PH for the MARS algorithm. The performance results, from best to worst, were: MARS > Bagging MARS > CART > CHAID. It was concluded that data mining techniques are highly useful in agricultural data for estimating variables and understanding the relationships between plant attributes.

Acknowledgements

As the authors, we are grateful to Agrovizyon Tohumculuk Tarım San. Tic. Ltd. Şti. for donating the sorghum x sudan grass hybrid seed used in this study.

Author contributions

S.C. Writing – review & editing, Statistical analysis, H.T. Writing, Investigation, Conceptualization, E.G. Writing, Investigation, Methodology, H.E. Writing – review & editing, Investigation.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Corresponding author (e-mail): senolcelik@bingol.edu.tr.

Competing interests

The authors declare no competing interests.

Compliance with ethical standards

This research was conducted following all relevant institutional, national, and international guidelines and legislation. The collection of plant materials was carried out in accordance with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Santos HRDO Forage yield and nutritive value of hay from sorghum-sudangrass hybrids Res. Soc. Dev. 2020 9 11 e95991110508 10.33448/rsd-v9i11.10508
Santos, H. R. D. O. et al. Forage yield and nutritive value of hay from sorghum-sudangrass hybrids. Res. Soc. Dev. 9(11), e95991110508. 10.33448/rsd-v9i11.10508 (2020).10.33448/rsd-v9i11.10508
2. Wang W Effects of feeding different Sorghum-sudangrass hybrids silages on growth performance, fatty acid composition of longissimus dorsi muscle, ruminal bacteria and volatile fatty acid formation of weaned small-tailed Han lambs Anim. Feed Sci. Technol. 2024 307 115851 10.1016/j.anifeedsci.2023.115851
Wang, W. et al. Effects of feeding different Sorghum-sudangrass hybrids silages on growth performance, fatty acid composition of longissimus dorsi muscle, ruminal bacteria and volatile fatty acid formation of weaned small-tailed Han lambs. Anim. Feed Sci. Technol. 307, 115851. 10.1016/j.anifeedsci.2023.115851 (2024).10.1016/j.anifeedsci.2023.115851
3. Sato S Clemente T Dweikat I Identification of an elite sorghum genotype with high in vitro performance capacity Vitro Cell. Dev. Biology-Plant 2004 40 57 60 10.1079/IVP2003475
Sato, S., Clemente, T. & Dweikat, I. Identification of an elite sorghum genotype with high in vitro performance capacity. Vitro Cell. Dev. Biology-Plant. 40, 57–60. 10.1079/IVP2003475 (2004).10.1079/IVP2003475
4. Khalil, I. A. Dry farming in crops and cropping in pakistan (higher education commission, 2008).
5. Suhas PW Rossella Albrizio V Nageswara R Sorghum Crop yield response to water: fao irrigation and drainage paper 66 2012 Rome Food And Agriculture Organization Of The United Nations
Suhas, P. W., Rossella Albrizio, V. & Nageswara, R. Sorghum. In Crop yield response to water: fao irrigation and drainage paper 66 (Food And Agriculture Organization Of The United Nations, Rome, 2012).
6. Han LY Li J Na RS Yu Z Zhou H Effect of two additives on the fermentation, in vitro digestibility and aerobic security of sorghum–sudangrass hybrid silages Grass Forage Sci. 2015 70 185 194 10.1111/gfs.12092
Han, L. Y., Li, J., Na, R. S., Yu, Z. & Zhou, H. Effect of two additives on the fermentation, in vitro digestibility and aerobic security of sorghum–sudangrass hybrid silages. Grass Forage Sci. 70, 185–194. 10.1111/gfs.12092 (2015).10.1111/gfs.12092
7. Mahmoudzadeh VM Oad R Sorghum-sudangrass water productivity under subsurface drip irrigation Irrig. Sci. 2018 67 5 702 712 10.1002/ird.2278
Mahmoudzadeh, V. M. & Oad, R. Sorghum-sudangrass water productivity under subsurface drip irrigation. Irrig. Sci. 67(5), 702–712. 10.1002/ird.2278 (2018).10.1002/ird.2278
8. Peng JL Kim M Kim K Sung K Climatic suitability mapping and driving factors detection for whole crop maize and sorghum–sudangrass hybrid production in the south area of the Korean peninsula and Jeju island Grassl. Sci. 2020 66 207 214 10.1111/grs.12270
Peng, J. L., Kim, M., Kim, K. & Sung, K. Climatic suitability mapping and driving factors detection for whole crop maize and sorghum–sudangrass hybrid production in the south area of the Korean peninsula and Jeju island. Grassl. Sci. 66, 207–214. 10.1111/grs.12270 (2020).10.1111/grs.12270
9. Khaleduzzaman ABM Enamul Haq Hazary M Emdadul Haque M Shafiqul Islam M Nitrogen and phosphorus fertilization for jumbo (sorghum bicolor x sorghum Sudanese) forage production and evaluation by using near infrared reflectance spectroscopy Int. J. Agron. Plant. Prod. 2013 4 3576 3582
Khaleduzzaman, A. B. M., Enamul Haq Hazary, M., Emdadul Haque, M. & Shafiqul Islam, M. Nitrogen and phosphorus fertilization for jumbo (sorghum bicolor x sorghum Sudanese) forage production and evaluation by using near infrared reflectance spectroscopy. Int. J. Agron. Plant. Prod. 4, 3576–3582 (2013).
10. Matei O Rusu T Petrovan A Mihuţ G A data mining system for real time soil moisture prediction Procedia Eng. 2017 181 837 844 10.1016/j.proeng.2017.02.475
Matei, O., Rusu, T., Petrovan, A. & Mihuţ, G. A data mining system for real time soil moisture prediction. Procedia Eng. 181, 837–844. 10.1016/j.proeng.2017.02.475 (2017).10.1016/j.proeng.2017.02.475
11. Harper L AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture Database 2018 10.1093/database/bay088 30239679
Harper, L. et al. AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture. Database. 10.1093/database/bay088 (2018). bay088.30239679 10.1093/database/bay088
12. Hussain T Anothai J Nualsri C Soonsuwon W Application of CSM-CERES-Rice in scheduling irrigation and simulating effect of drought stress on upland rice yield Indian J. Agricultural Res. 2018 52 2 140 145 10.18805/IJARe.A-321
Hussain, T., Anothai, J., Nualsri, C. & Soonsuwon, W. Application of CSM-CERES-Rice in scheduling irrigation and simulating effect of drought stress on upland rice yield. Indian J. Agricultural Res. 52(2), 140–145. 10.18805/IJARe.A-321 (2018).10.18805/IJARe.A-321
13. Deepa N Ganesan K Decision-making tool for crop selection for agriculture development Neural Comput. Appl. 2019 31 1215 1225 10.1007/s00521-017-3154-x
Deepa, N. & Ganesan, K. Decision-making tool for crop selection for agriculture development. Neural Comput. Appl. 31, 1215–1225. 10.1007/s00521-017-3154-x (2019).10.1007/s00521-017-3154-x
14. El Bilali, H., Bottalico, F., Ottomano Palmisano, G. & Capone, R. Information and communication technologies for smart and sustainable agriculture. In 30th Scientific-Experts Conference of Agriculture and Food Industry: Answers for Forthcoming Challenges in Modern Agriculture (pp. 321–334). Springer International Publishing. (2020). 10.1007/978-3-030-40049-1_41
15. Saiz-Rubio V Rovira-Más F From smart farming towards agriculture 5.0: a review on crop data management Agronomy 2020 10 2 207 10.3390/agronomy10020207
Saiz-Rubio, V. & Rovira-Más, F. From smart farming towards agriculture 5.0: a review on crop data management. Agronomy. 10(2), 207. 10.3390/agronomy10020207 (2020).10.3390/agronomy10020207
16. He L Fruit yield prediction and estimation in orchards: a state-of-the-art comprehensive review for both direct and indirect methods Comput. Electron. Agric. 2022 195 106812 10.1016/j.compag.2022.106812
He, L. et al. Fruit yield prediction and estimation in orchards: a state-of-the-art comprehensive review for both direct and indirect methods. Comput. Electron. Agric. 195, 106812. 10.1016/j.compag.2022.106812 (2022).10.1016/j.compag.2022.106812
17. Parolini G Weather, climate, and agriculture: historical contributions and perspectives from agricultural meteorology Wiley Interdisciplinary Reviews: Clim. Change 2022 13 3 e766 10.1002/wcc.766
Parolini, G. Weather, climate, and agriculture: historical contributions and perspectives from agricultural meteorology. Wiley Interdisciplinary Reviews: Clim. Change. 13(3), e766. 10.1002/wcc.766 (2022).10.1002/wcc.766
18. Macuácua JC Centeno JAS Amisse C Data mining approach for dry bean seeds classification Smart Agricultural Technol. 2023 5 100240 10.1016/j.atech.2023.100240
Macuácua, J. C., Centeno, J. A. S. & Amisse, C. Data mining approach for dry bean seeds classification. Smart Agricultural Technol. 5, 100240. 10.1016/j.atech.2023.100240 (2023).10.1016/j.atech.2023.100240
19. Emami S Rezaverdinejad V Dehghanisanij H Emami H Elbeltagi A Data mining predictive algorithms for estimating soil water content Soft. Comput. 2024 28 6 4915 4931 10.1007/s00500-023-09208-3
Emami, S., Rezaverdinejad, V., Dehghanisanij, H., Emami, H. & Elbeltagi, A. Data mining predictive algorithms for estimating soil water content. Soft. Comput. 28(6), 4915–4931. 10.1007/s00500-023-09208-3 (2024).10.1007/s00500-023-09208-3
20. Kadirhanoğulları İH Kadirhanoğulları M Kara MK Kumlay A Determining organic food knowledge level in Iğdir KSU J. Agric. Nat. 2022 25 4 882 889 10.18016/ksutarimdoga.vi.890284
Kadirhanoğulları, İ. H., Kadirhanoğulları, M., Kara, M. K. & Kumlay, A. Determining organic food knowledge level in Iğdir. KSU J. Agric. Nat. 25(4), 882–889. 10.18016/ksutarimdoga.vi.890284 (2022).10.18016/ksutarimdoga.vi.890284
21. Kızgın MS Çambay Z Sepet H Özçelik STA Uyanık H Classification of fruit types of onobrychis with machine learning approach Firat Univ. J. Sci. 2023 35 2 87 96
Kızgın, M. S., Çambay, Z., Sepet, H., Özçelik, S. T. A. & Uyanık, H. Classification of fruit types of onobrychis with machine learning approach. Firat Univ. J. Sci. 35(2), 87–96 (2023).
22. Kumar SR Kumar KR A study on paddy crops disease prediction using data mining techniques Singaporean J. Sci. Res. (SJSR) 2015 7 1 336 347
Kumar, S. R. & Kumar, K. R. A study on paddy crops disease prediction using data mining techniques. Singaporean J. Sci. Res. (SJSR). 7(1), 336–347 (2015).
23. Hammer RG Sentelhas PC Mariano JC Sugarcane yield prediction through data mining and crop simulation models Sugar Tech. 2020 22 2 216 225 10.1007/s12355-019-00776-z
Hammer, R. G., Sentelhas, P. C. & Mariano, J. C. Sugarcane yield prediction through data mining and crop simulation models. Sugar Tech. 22(2), 216–225. 10.1007/s12355-019-00776-z (2020).10.1007/s12355-019-00776-z
24. Paul, M., Vishwakarma, S. K. & Verma, A. Analysis of soil behaviour and prediction of crop yield using data mining approach. In 2015 International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 766–771), IEEE (2015).
25. Devika B Ananthi B Analysis of crop yield prediction using data mining technique to predict annual yield of major crops Int. Res. J. Eng. Technol. 2018 5 12 1460 1465
Devika, B. & Ananthi, B. Analysis of crop yield prediction using data mining technique to predict annual yield of major crops. Int. Res. J. Eng. Technol. 5(12), 1460–1465 (2018).
26. Oddy, V. H., Robards, G. E. & Low, S. G. Prediction of in vivo dry matter digestibility from the fibre and nitrogen content of a feed. 395–398 (1983).
27. Sheaffer, C. C. et al. Acide detergent fiber, neutral detergent fiber concentration and relative feed value. In North American Alfalfa Improvement Conference, Minneapolis (1995).
28. Kass GV An explanatory technique for investigating large quantities of categorical data J. Royal Stat. Soc. Ser. C (Applied Statistics) 1980 29 119 127 10.2307/2986296
Kass, G. V. An explanatory technique for investigating large quantities of categorical data. J. Royal Stat. Soc. Ser. C (Applied Statistics). 29, 119–127. 10.2307/2986296 (1980).10.2307/2986296
29. Atieh MA Predicting peri-implant disease: Chi-square automatic interaction detection (CHAID) decision tree analysis of risk indicators J. Periodontol. 2019 90 8 834 846 10.1002/JPER.17-0501 30730061
Atieh, M. A. et al. Predicting peri-implant disease: Chi-square automatic interaction detection (CHAID) decision tree analysis of risk indicators. J. Periodontol. 90(8), 834–846. 10.1002/JPER.17-0501 (2019).30730061 10.1002/JPER.17-0501
30. Harper PR A review and comparison of classification algorithms for medical decision making Health Policy 2005 71 315 331 10.1016/j.healthpol.2004.05.002 15694499
Harper, P. R. A review and comparison of classification algorithms for medical decision making. Health Policy. 71, 315–331. 10.1016/j.healthpol.2004.05.002 (2005).15694499 10.1016/j.healthpol.2004.05.002
31. Stothers L Guevaraa R Macna A Classification of male lower urinary tract symptoms using mathematical modeling and a regression tree algorithm of noninvasive nearinfrared spectroscopy parameters Europan Urol. 2009 57 179 362 10.1016/j.eururo.2009.05.004
Stothers, L., Guevaraa, R. & Macna, A. Classification of male lower urinary tract symptoms using mathematical modeling and a regression tree algorithm of noninvasive nearinfrared spectroscopy parameters. Europan Urol. 57, 179–362. 10.1016/j.eururo.2009.05.004 (2009).10.1016/j.eururo.2009.05.004
32. Soman, K. P., Diwakar, S. & Ajay, V. Insight into data mining-theory and practice. Prentice Hall of India, New Delhi, ISBN: 81-203-2897-3 (2006).
33. Friedman JH Multivariate adaptive regression splines Annals Stat. 1991 19 1 67 10.1214/aos/1176347963
Friedman, J. H. Multivariate adaptive regression splines. Annals Stat. 19, 1–67. 10.1214/aos/1176347963 (1991).10.1214/aos/1176347963
34. Deconinck E Coomans D Vander Heyden Y Exploration of linear modelling techniques and their combination with multivariate adaptive regression splines to predict gastro-intestinal absorption of drugs J. Pharm. Biomed. Anal. 2007 43 1 119 130 10.1016/j.jpba.2006.06.022 16859855
Deconinck, E., Coomans, D. & Vander Heyden, Y. Exploration of linear modelling techniques and their combination with multivariate adaptive regression splines to predict gastro-intestinal absorption of drugs. J. Pharm. Biomed. Anal. 43(1), 119–130. 10.1016/j.jpba.2006.06.022 (2007).16859855 10.1016/j.jpba.2006.06.022
35. Jalali-Heravi M Asadollahi-Baboli M Mani-Varnosfaderani A Shuffling multivariate adaptive regression splines and adaptive neuro-fuzzy inference system as tools for QSAR study of SARS inhibitors J. Pharm. Biomed. Anal. 2009 50 853 860 10.1016/j.jpba.2009.07.009 19665859
Jalali-Heravi, M., Asadollahi-Baboli, M. & Mani-Varnosfaderani, A. Shuffling multivariate adaptive regression splines and adaptive neuro-fuzzy inference system as tools for QSAR study of SARS inhibitors. J. Pharm. Biomed. Anal. 50, 853–860. 10.1016/j.jpba.2009.07.009 (2009).19665859 10.1016/j.jpba.2009.07.009
36. Ju X Chen VCP Rosenberger JM Liu F Expert systems with applications Expert Syst. Appl. 2021 171 114565 10.1016/j.eswa.2021.114565
Ju, X., Chen, V. C. P., Rosenberger, J. M. & Liu, F. Expert systems with applications. Expert Syst. Appl. 171, 114565. 10.1016/j.eswa.2021.114565 (2021).10.1016/j.eswa.2021.114565
37. Kornacki, J. & Cwik, J. Statistical Learning Systems (in Polish) (WNT, 2005).
38. Akin M Eyduran SP Eyduran E Reed BM Analysis of macro nutrient related growth responses using multivariate adaptive regression splines Pl Cell. Tissue Organ. Cult. (PCTOC) 2020 140 661 670 10.1007/s11240-019-01763-8
Akin, M., Eyduran, S. P., Eyduran, E. & Reed, B. M. Analysis of macro nutrient related growth responses using multivariate adaptive regression splines. Pl Cell. Tissue Organ. Cult. (PCTOC). 140, 661–670. 10.1007/s11240-019-01763-8 (2020).10.1007/s11240-019-01763-8
39. Naser AH Badr AH Henedy SN Ostrowski KA Imran H Application of multivariate adaptive regression splines (MARS) approach in prediction of compressive strength of eco-friendly concrete Case Stud. Constr. Mater. 2022 17 e01262 10.1016/j.cscm.2022.e01262
Naser, A. H., Badr, A. H., Henedy, S. N., Ostrowski, K. A. & Imran, H. Application of multivariate adaptive regression splines (MARS) approach in prediction of compressive strength of eco-friendly concrete. Case Stud. Constr. Mater. 17, e01262. 10.1016/j.cscm.2022.e01262 (2022).10.1016/j.cscm.2022.e01262
40. Otok BW Putra RY Sutikno Yasmirullah SDP Bootstrap aggregating multivariate adaptive regression spline for observational studies in diabetes cases Syst. Reviews Pharm. 2020 11 8 406 413 10.31838/srp.2020.8.59
Otok, B. W., Putra, R. Y. & Sutikno Yasmirullah, S. D. P. Bootstrap aggregating multivariate adaptive regression spline for observational studies in diabetes cases. Syst. Reviews Pharm. 11(8), 406–413. 10.31838/srp.2020.8.59 (2020).10.31838/srp.2020.8.59
41. Willmott C Matsuura K Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance Climate Res. 2005 30 79 82 10.3354/cr030079
Willmott, C. & Matsuura, K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 30, 79–82. 10.3354/cr030079 (2005).10.3354/cr030079
42. Takma C Atil H Aksakal V Comparison of multiple linear regression and artificial neural network models goodness of fit to lactation milk yields Kafkas Universitesi Veteriner Fakultesi Dergisi 2012 18 941 944 10.9775/kvfd.2012.6764
Takma, C., Atil, H. & Aksakal, V. Comparison of multiple linear regression and artificial neural network models goodness of fit to lactation milk yields. Kafkas Universitesi Veteriner Fakultesi Dergisi. 18, 941–944. 10.9775/kvfd.2012.6764 (2012).10.9775/kvfd.2012.6764
43. Atis I Konuskan O Duru M Gozubenli H Yilmaz S Effect of harvesting time on yield, composition and forage quality of some forage sorghum cultivars Int. J. Agric. Biol. 2012 14 879 886
Atis, I., Konuskan, O., Duru, M., Gozubenli, H. & Yilmaz, S. Effect of harvesting time on yield, composition and forage quality of some forage sorghum cultivars. Int. J. Agric. Biol. 14, 879–886 (2012).
44. Khalilian ME Habibi D Golzardi F Aghayari F Khazaei A Effect of maturity stage on yield, morphological characteristics, and feed value of sorghum (Sorghum bicolor (L.)) Moench] cultivars Cereal Res. Commun. 2022 50 4 1095 1104 10.1007/s42976-022-00244-7
Khalilian, M. E., Habibi, D., Golzardi, F., Aghayari, F. & Khazaei, A. Effect of maturity stage on yield, morphological characteristics, and feed value of sorghum (Sorghum bicolor (L.)) Moench] cultivars. Cereal Res. Commun. 50(4), 1095–1104. 10.1007/s42976-022-00244-7 (2022).10.1007/s42976-022-00244-7
45. Aydemir SK Turhal K Correlation analyses of herbage yield and quality components in certain sorghum×sudangrass (sorghum bicolor L.× sorghum sudanense staph.) Hybrid cultivars Turkish J. Agriculture-Food Sci. Technol. 2018 6 4 495 499 10.24925/turjaf.v6i4.495-499.1818
Aydemir, S. K. & Turhal, K. Correlation analyses of herbage yield and quality components in certain sorghum×sudangrass (sorghum bicolor L.× sorghum sudanense staph.) Hybrid cultivars. Turkish J. Agriculture-Food Sci. Technol. 6(4), 495–499. 10.24925/turjaf.v6i4.495-499.1818 (2018).10.24925/turjaf.v6i4.495-499.1818
46. Parlak AÖ Gökkuş A Alatürk F Hanoğlu H Tölü C Herbage yield and quality of wheat stubble and sorghum sudan-grass pastures Sci. Papers Ser. Agron. 2016 59 374 377
Parlak, A. Ö., Gökkuş, A., Alatürk, F., Hanoğlu, H. & Tölü, C. Herbage yield and quality of wheat stubble and sorghum sudan-grass pastures. Sci. Papers Ser. Agron. 59, 374–377 (2016).
47. Farhadi A Paknejad F Golzardi F Ilkaee MN Aghayari F Effects of Limited Irrigation and Nitrogen Rate on the Herbage Yield, Water Productivity, and Nutritive Value of Sorghum Silage Commun. Soil Sci. Plant Anal. 2022 53 5 576 589 10.1080/00103624.2021.2017959
Farhadi, A., Paknejad, F., Golzardi, F., Ilkaee, M. N. & Aghayari, F. 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. 10.1080/00103624.2021.2017959 (2022).10.1080/00103624.2021.2017959
48. Budak F Aydemir SK Determination and comparison of yield and yield components of sorghum (Sorghum Bicolor L.), Sudan grasses (Sorghum Sudanense L.), sorghum sudangrass hybrids (Sorghum Bicolor x Sorghum Bicolor Var. Sudanense) and corn (Zea Mays L.) varieties grown as a second crop on western transition zone after Hungarian vetch (Vicia Pannonica Crantz) Fresenius Environ. Bull. 2017 26 8 5153 5162
Budak, F. & Aydemir, S. K. Determination and comparison of yield and yield components of sorghum (Sorghum Bicolor L.), Sudan grasses (Sorghum Sudanense L.), sorghum sudangrass hybrids (Sorghum Bicolor x Sorghum Bicolor Var. Sudanense) and corn (Zea Mays L.) varieties grown as a second crop on western transition zone after Hungarian vetch (Vicia Pannonica Crantz). Fresenius Environ. Bull. 26(8), 5153–5162 (2017).
49. Kaplan M Arslan M Kale H Kara K Kokten K GT Biplot analysis for silage potential, nutritive value, gas and methane production of stay-green grain sorghum shoots Ciencia E Investigacin Agrar. 2017 44 3 230 238 10.7764/rcia.v44i3.1802
Kaplan, M., Arslan, M., Kale, H., Kara, K. & Kokten, K. GT Biplot analysis for silage potential, nutritive value, gas and methane production of stay-green grain sorghum shoots. Ciencia E Investigacin Agrar. 44(3), 230–238. 10.7764/rcia.v44i3.1802 (2017).10.7764/rcia.v44i3.1802
50. Ahmed IM Rajab MN Estimate of genetic parameters and correlation coefficient in Sudan grass (Sorghum sudanense, (Piper) Staff) J. Plant. Prod. 2017 8 9 935 938 10.21608/jpp.2017.40915
Ahmed, I. M. & Rajab, M. N. Estimate of genetic parameters and correlation coefficient in Sudan grass (Sorghum sudanense, (Piper) Staff). J. Plant. Prod. 8(9), 935–938. 10.21608/jpp.2017.40915 (2017).10.21608/jpp.2017.40915
51. Zannou, J. G. N. & Houndji, V. R. Sorghum yield prediction using machine learning. 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART) (pp. 1–4). IEEE 978-1-5386-5541-2/18 (2019).
52. Karaer, M. et al. Artificial neural network modeling for investigation on the effect of deficit irrigation and nitrogen levels on yield and quality of hay remaining after seed harvest of sorghum sudangrass hybrid. Commun. Soil Sci. Plant Anal. 55(17), 2565–2577. 10.1080/00103624.2024.2369201 (2024).
