# Determination of Land Suitability Criteria for Maize Hybrid in Boalemo Regency Based on Optimum Yield and Selected Land Quality

Authors: Nurdin; Asda Rauf; Yunnita Rahim; Echan Adam; Nikmah Musa; Fitriah Suryani Jamin; Suyono Dude; Rival Rahman; Hidayat Arismunandar Katili
Journal: Applied and Environmental Soil Science
 (2023)

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## Abstract

The significant effect of land quality on maize production has not been fully considered in the existing land suitability criteria. Therefore, this study aims to determine land suitability criteria for hybrid maize in Boalemo Regency based on the optimum yield and land quality. It was carried out in Boalemo Regency, Indonesia, where the land unit of 67 units was surveyed to obtain land characteristics data. A partial least square of structural equation model (PLS-SEM) with SmartPLS 8.0 was used to select a robust land quality controlling hybrid maize yield, while the boundary line method was used to determine optimum yield and differentiating of land suitability criteria. The result showed that land qualities that define the optimum yield of hybrid maize were root conditions, nutrient availability, nutrient retentions, land preparation, and erosion hazard. The soil characteristics were effective depth, coarse material, organic C, total N, K exchangeable, slopes, soil erosion, rock outcrops, and surface rocks. Furthermore, the highest optimum yield of 8.54 ton/ha was achieved by the total N and slopes for a very suitable class (S1), while the lowest of 5.58 ton/ha was obtained by exchangeable K for class S1. This showed that the combination of PLS-SEM and boundary line analysis was a better approach to developing new land suitability criteria for hybrid maize.


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## Body

## 1. Introduction

Food security and farmer prosperity are global concerns; this makes every country increase crop production as well as farmers’ income. An important issue for countries with developing economies is ensuring food security, where the agricultural sector plays a strategic role in increasing food availability [1]. Although the global food system has placed maize (Zea mays L.) as the leading cereal crop [2], its productivity is being disrupted by land degradation, water scarcity, and climate change [3].In Indonesia, the wet tropical climate with rainfall and high temperatures tends to reduce the quality of land and agricultural products [4]. This country ranked 8th among the maize-producing nations with a contribution of 2.19% and 2.42% of the world’s total harvested area [5]. However, the main problem is the relatively low level of yield in several regions because the achievement of maize production has not been followed by an increase in yield per unit area [6]. This is indicated by the average yield in 2020, which was up to 5.57 ton/ha [5]. According to a previous investigation, maize production in Indonesia can reach between 10 and 12 ton/ha [7, 8], thereby making the country the 21st leading importer in the world.In Indonesia, Gorontalo Province is one of the centers of maize production, where the hybrid type is the most widely grown species [9]. The maize production in the province reached 1.8 million tons in 2021 [10], with several export advantages and competitiveness [11]. Furthermore, the planting of hybrid, composite, and local maize types has reached more than 98.90%, 0.68%, and 0.41% only, respectively [12], including Boalemo Regency.Boalemo Regency ranks third as a maize-producing center in Gorontalo Province with a contribution of 18.90% [13]. The maize plant dominates the use of agricultural land in this district by 37.43% [14]; therefore, the commodity has competitive and comparative advantages with a PCR value of 0.80 and 0.91 DRC. This is because maize plant is supported by land area, climatic conditions, production facilities, as well as market guarantees, and the basic price of buying corn from the government [15]. In 2021, the average hybrid and local maize yields in the regency reached 5.20 tons/ha [16] and 2.34–3.30 tons/ha, respectively [17]. This indicated that the productivity of hybrid maize is still higher than local maize [18], but with lower achievement compared to the national maize production of 5.57 tons/ha [5], and has not yet reached the target of 5.60 tons/ha in 2021 [19]. The production of hybrid maize in Gorontalo can reach 9.78–13.11 tons/ha [20] because it is often cultivated on land that does not meet the required qualities [6]. Therefore, there is a need to determine land quality-based hybrid maize land suitability criteria for site-specific land use planning in Boalemo District.Maize is usually grown on land with low yield potential [21] and soil fertility, thereby causing low productivity [22]. Moreover, land productivity is determined by quality and characteristics [23, 24], while land quality has a close relationship with maize yields [25]. The land quality affecting the optimum yield of maize needs to be determined [26] and increased by using hybrid varieties that have high yields. This makes it necessary to evaluate the suitability of the hybrid maize in a region to ensure optimal production. The land suitability criteria for hybrid maize are not yet available because the current criterion is the general suitability of maize plants without distinguishing between hybrids and inbreds. Therefore, there is a need to make land suitability criteria for hybrid maize plants.A previous study has shown that land quality has a significant effect on suitability for certain uses [27]. Meanwhile, land suitability is also important due to the continuous increase in the demand for agricultural land [28]. The land suitability criteria for existing maize fields are still general [29], and there are no specific criteria for hybrid maize varieties. The class assessment outcomes obtained using the existing criteria are relatively many and are not in line with the actual field results [30]. The current criteria consist of 3 components, namely, land quality, characteristics, and ranges of land characteristic values to determine its suitability. Therefore, the problem in developing criteria is choosing land quality, characteristics, and determining the range of land characteristic values associated with suitability classes, namely, suitable, somewhat suitable, marginally suitable, and not suitable.The selection of land quality and characteristics can be carried out through the partial least square of the structural equation model (PLS-SEM), while the range limits are being determined by the boundary line method. Land qualities and characteristics in the current criteria can be used temporarily since structural equation model analysis with partial least squares produces better indicators and models than other multivariate analyses [31–35]. This is because the variant-based PLS-SEM has a higher level of flexibility and the size of the sample used is relatively small ranging from 30 to 100 [36–39]. The use of PLS-SEM to determine land characteristics and qualities that control maize crop yields is still relatively rare, except for Syaf [40] on older cocoa plants, maize composite [41], and on local varieties [6]. The boundary line method can help determine nutrient adequacy concentrations and the optimum yield range of a plant that affects nutrients, as well as other land characteristics [42, 43]. Currently, the land suitability criteria for maize plants have not been determined using the boundary line method, except by Ridayanti et al. [44], although the investigation was not specific to hybrid maize.After obtaining the quality and characteristics of land affecting maize yields with PLS-SEM, the boundary line method can be used to determine the optimum yield as well as land suitability criteria simultaneously. This is carried out by drawing the intersection of the boundary line at the yield and projecting with the land characteristics [29]. Therefore, this study aims to determine land suitability criteria for hybrid maize in Boalemo Regency based on the optimum yield and land quality.


## 2. Materials and Methods

### 2.1. Study Area

The study area extends from 0°28′5.6″–0″57′30.02″ N to 122°8′34.25″–122°43′10.41″E (Figure1) on a scale of 1 : 40,000, which is located in the agricultural land of Boalemo Regency, Gorontalo Province, Indonesia. The maximum air temperature was 28.19°C and the minimum was 26.79°C with an average of 28.01°C. Meanwhile, the maximum rainfall was 1,849 mm and the minimum was 1,246 mm with an average of 1,478 mm [45]. The wet and dry seasons last for 3 months and 5 months, respectively. The soil mapping carried out by Ritung et al. [46] at a scale of 1 : 50,000 become the initial reference for determining 35 soil units, where each unit has information on land characteristics, namely, effective depth, drainage, texture, pH, cation exchange capacity, base saturation, landform, parent material, relief, and land unit area. Meanwhile, the new soil unit is 1 : 40,000 in scale, and there has been a change in the agricultural land use existing. This indicated that the slope class of 8–15% or hilly is more dominant in the study area with a percentage of 29.77% and slopes >40% or mountainous which is only 2.67%. Furthermore, the dry land is dominant with a value of 59.86% and a little shrub which was only 9.21%, while the dominant Typic Haplustalfs of soil subgroup classification was 22.47%; then, the Fluventic Haplustepts was 21.31% and very little Vertic Haplustepts of soil subgroup classification was 0.04% only (Figure 1).Figure 1
Study area.


### 2.2. Dataset Collection for Land Quality and Land Characteristics

The quality and characteristics of the land in this study refer to Wahyunto et al. [47], consisting of 10 land qualities and 24 characteristics. The set of temperature land quality is determined from the characteristics of the annual average air temperature, while the land quality water availability is determined from the characteristics of annual rainfall, wet months, dry months, and the length of the growth period (LGP). Land quality oxygen availability is determined from soil drainage characteristics, rooting conditions are determined from the soil texture, coarse material, and soil effective depth, and nutrient retention is identified from the pH value, C-organic, cation exchange capacity (CEC), and base saturation. Furthermore, the available nutrient is determined from the characteristics of total N, P, and exchangeable K, the sodicity is obtained from the exchangeable sodium percentage (ESP), while erosion hazard is determined from slopes and soil erosion. The quality of the flood-hazard land is determined by identifying the height and the duration of the inundation, while preparation is carried out from the characteristics of outcrops and surface rocks. The selection of this set of land qualities and characteristics is based on the availability of data and their impact on maize production [26].Data on average annual air temperature and rainfall for 10 years (2010–2021) were collected from different climate stations, namely, the Bandungrejo with 0°41′ N–122°38′ E, the elevation 40 m asl, while Harapan has 0°42′ N–122°29′ E and an elevation of 37 m asl. It also includes Lakeya Rain Post with 0°42.82′ N–122°32.07′ E, 32 m asl, Mohiyolo has 0°46.41′ N–122°26.41′ E and an elevation of 39 m asl, Saritani 0°46.45′ N–122°20.40′ E, with 26 m asl, Tangkobu 0°37.25′ N–122°36.36′ E, 25 m asl, Bubaa 0°31.36′ N–122°33.39′ E, 16 m asl, Wonggahu 0°38′ N–122°33′ E, 35 m asl, and Sambati Rain Post with 0°31.184′ N–122°27.074′ E, and an elevation of 9 m asl managed by BWS II Sulawesi. Furthermore, these data determined wet months (>200 mm) and dry months (<100 mm), which refers to the Oldeman and Darmiyati criteria [48]. The land water balance was determined using the Thornwaite and LGP methods based on the number of surplus and deficit rainy days [49].Based on the previous soil unit [46], then these soil units were detailed again by adding 32 of soil units to be surveyed and observed, making up to 67 soil units in the area as shown in the legend Figure 1. Soil characteristics such as drainage, coarse material, effective soil depth, slope, inundation height and duration, rock outcrops, and surface rocks were determined by conducting soil profile descriptions and direct observation on 67 pedons referring to the description guidelines in the field [50]. Meanwhile, soil erosion was determined by the USLE method [51]. Other soil characteristics were further analyzed in the soil laboratory using samples from each pedon.Soil samples were dried for 3 days and sieved through a 2-mesh sieve. The method of soil physics laboratory analysis was carried out according to the procedures by Kurnia et al. [52]. Based on this procedure, soil texture was analyzed in terms of sand, clay, and silt fractions using the pipette method, while soil moisture storage was evaluated using the gravimetric method that can be applied in water balance analysis. The method of soil chemistry laboratory analysis was carried out according to the procedures by Eviyati and Sulaiman [53]. The soil pH was determined with a pH meter in a 1 : 2.5 soil and water solution, while the organic C content was assessed using the Walkley and Black method. The total N was assessed using the Kjeldahl method, while the available P content was measured using the Olsen method. The basic cations and CEC were extracted with 1N NH4OAc pH 7.0 (ammonium acetate) on a dry sample of 105°C. The base saturation was determined by calculating the percentage of basic cations number with CEC, and ESP was evaluated using the percentage ratio of sodium to CEC [54, 55]. Subsequently, the data from the chemical analysis were averaged to a depth of 0–30 cm using the weighted averaging technique. The framework of this study is presented in Figure 2.Figure 2
Research framework.


### 2.3. Dataset Collection for Hybrid Maize Yield

The areas currently planted with hybrid maize were identified and blocks with a size of 2.5 m × 2.5 m were made in each map unit (Figure1). Maize plants in each block passed through standardized management according to farmers’ technology. After harvesting, weighting was carried out to obtain hybrid maize yield data from the results of tiles on each land unit. Subsequently, the results were calculated using the following formula [56]:(1)Yt=HxA6.25m2.Meanwhile, productivity is calculated using the following formula [56]:(2)Ytha−1=Hx1.64x56.73100,where Y is the hybrid maize yield, H is the tile yield (kg), A is the maize area 1 per hectare (ha), and 1.64 and 56.73 are the constants.


### 2.4. Selection of Land Quality and Land Characteristics

The quality and characteristics of the land in the suitability criteria are used as presented in Tables1 and 2, which show brief statistics. Generally, most data are relatively homogenous, except for coarse material, available P, slopes, soil erosion, height and inundation, as well as rock outcrops and surface rocks. The selection used the partial least squares of the structural equation model (PLS-SEM) refers to Hair et al. [38] with tools SmartPLS, where land quality and characteristics were selected as the latent and manifest variables, respectively. The analysis in PLS-SEM has 2 main steps, namely, the measurement model (outer model) and the structural model test (inner model).Table 1
Latent variables and indicators used in this study.
Latent variablesIndicatorsData sourcesNotationLand qualityNotationLand characteristicsX1Temperature (t)X1.1Temperature[45]X2Water availability (wa)X2.1Rainfall[45]X2.2Wet monthRainfall >200 mmX2.3Dry monthRainfall <100 mmX2.4Long growth period (LGP)Water balance (Thornwaite method), soil moisture storage (gravimetric method), water surplus, and deficit daysX3Oxygen availability (oa)X3.1DrainageSoil survey and land observationX4Rooting condition (rc)X4.1TexturePipet methodX4.1.1Sand fractionX4.1.2Silt fractionX4.1.3ClayX4.2Coarse materialSoil survey and land observationX4.3Effective depthX5Nutrient retention (nr)X5.1pH, H2OpH meter (1 : 2.5)X5.2pH, KClX5.3Organic CWalkley and Black methodX5.4Cation exchange capacity (CEC)1N NH4OAc, pH 7.0, extractedX5.5Base saturationCalculationX6Nutrient availability (na)X6.1TotalNKjeldahl methodX6.2P availabilityOlsen methodX6.3K exchangeable1N NH4OAc, pH 7.0, extractedX7Sodicity (xn)X7.1Exchangeable sodium percentage (ESP)CalculationX8Erosion hazard (eh)X8.1SlopesSoil survey and land observationX8.2Soil erosionX9Flooding hazard (fh)X9.1Inundation heightSoil survey and land observationX9.2Inundation periodX10Land preparation (lp)X10.1Rock outcropsSoil survey and land observationX10.2Surface rockYHybrid maize yieldY.1Hybrid maize yieldTile box methodsTable 2
Brief statistics of land quality and characteristics.
Latent variables/indicatorsUnitnMinMedianMeanMaxSDX1 (temperature)X1.1 (temperature)°C6726.7927.8028.0128.190.63X2 (water availability)X2.1 (rainfall)Mm671,246.001,533.421,478.001,849.00232.69X2.2 (wet month)Month670.001.031.003.000.85X2.3 (dry month)Month672.003.394.005.001.06X2.4 (LGP)Day67211.00246.00214.00304.0044.54X3 (oxygen availability)X3.1 (drainage)Class670.003.764.006.001.82X4 (rooting conditions)X4.1 (texture)Class671.002.212.005.000.99X4.1.1 (sand fraction)%675.0041.5843.0081.3318.51X4.1.2 (silt fraction)%677.3327.3124.5051.5011.54X4.1.3 (clay)%6711.3331.9030.0056.3312.72X4.2 (coarse material)%675.0017.2710.0070.0016.58X4.3 (effective depth)Cm6710.0074.5574.00160.0036.40X5 (nutrient retention)X5.1 (pH, H2O)675.005.925.907.150.52X5.2 (pH, KCl)674.355.245.176.600.56X5.3 (organic C)%670.410.850.772.350.39X5.4 (CEC)Cmol(+)/kg678.9424.8922.4359.5711.41X5.5 (base saturation)%6745.0356.2252.8581.899.76X6 (nutrient availability)X6.1 (totalN)%670.040.090.080.270.04X6.2 (P availability)Mg/kg670.738.623.7758.6712.61X6.3 (K exchangeable)Cmol(+)/kg670.070.390.241.920.42X7 (sodicity)X7.1 (ESP)%670.767.066.2024.175.62X8 (erosion hazard)X8.1 (slopes)%671.009.586.0025.007.29X8.2 (soil erosion)Tons/ha/year673.66334.51110.271772.43439.08X9 (flooding hazard)X9.1 (inundation height)Cm670.007.580.0050.0017.10X 9.2 (inundation period)Day670.000.640.005.001.52X10 (land preparation)X10.1 (rock outcrops)%670.006.640.0045.0011.56X10.2 (surface rock)%670.006.580.0045.0011.59Y (hybrid maize yield)Ton/ha672.854.954.688.071.15n, the number of the land unit; min, minimum; max, maximum; SD, standard deviation; LGP, long growth period; CEC, cation exchange capacity; ESP, exchangeable sodium percentage.Step 1 consists of validity and reliability tests, wherein the validity test is conducted with convergent and discriminant validity. The convergent validity is in form of outer loadings (loading factor) and average variance extracted (AVE), while discriminant validity is in form of cross-loading and the Fornell–Larcker criterion. Meanwhile, the reliability test uses composite reliability and Cronbach’s alpha.Convergent validity was observed from the magnitude of the outer loading and the AVE value of each indicator on the latent variable. The validity was calculated according to the following equation [57–60]:(3)xi=λxiξ1+δi,yi=λyiη1+εi,where x and y are the exogenous (ξ) and endogenous (η) latent variable indicators, λx and λy are the loading factors, δ and ε are the residual/measurement errors or noise.Meanwhile, the average variance extracted (AVE) value was calculated using the following equation [61–65]:(4)AVE=∑λ2i∑λ2i+∑ivarεi,where λi2  is the loading factor, var  is the variance, and εi is the error variance.The loading factor of an indicator with the highest value is the strongest or most important measure in reflecting the latent variable. In this study, the loading factor value is >0.70 for selecting best land characteristics, but values ranging from 0.50 to 0.60 can still be tolerated with at-statistic >1.96 or a small p value of 0.05 [37, 66]. Meanwhile, the AVE value used was more than 0.50, showing that the convergent validity on the latent variable has been reached.The discriminant validity test used the cross-loading value and the Fornell–Larker criterion to test discriminantly valid indicators in explaining or reflecting latent variables. When the correlation between the latent variables and the core measurement of each indicator is high, the latent variable can predict the indicator better and is considered valid. The discriminant validity is measured by the square root of the average variance extracted, which will be compared with the correlation value between variables. The value calculated based on the square root of AVE must be higher than the correlation between constructs [61]. The equation is expressed as follows [61, 63–65, 67]:(5)SquarerootofAVE=∑λ2i∑λ2i+∑ivarεi,where λi2 is the loading factor, var is the variance, and εi is the error variance.Furthermore, the composite reliability and Cronbach’s alpha were used to test the reliability value between indicators of the latent variables. They are considered good and accepted when the value is >0.70 and has a minimum value of 0.60 [37]. The composite reliability value is calculated using the following equation [62, 65, 68, 69]:(6)ρc=∑λi2∑λi2+∑ivarεi,where λi is the loading factor, var is the variance, and εi is the error variance.Meanwhile, the value of Cronbach’s Alpha is calculated according to the following equation [65, 70]:(7)α=∑p≠p′corXpq.Xp′qpq+∑p≠p′corXpq.Xp′qpqpq−1,where Pq is the number of indicators or manifest variables and q is the indicator block.For step 2, the structural model testing (inner model) was carried out after the relationship model was built in line with the observed data and the overall suitability, namely, goodness of fit. The structural equation (inner model) is as follows [59, 60, 62]:(8)Ηj=γjξ1+γjξ2+…γjξn+ςj,where ηj is the endogenous variable vector (dependent), γjξ1+γjξ2+…γjξn is the exogenous latent variable vector, and ςj is the residual vector (error).Meanwhile, the determinant coefficient and goodness of fit (Q2) were calculated using the following equation [62, 64, 70]:(9)Q2Predictiverelevance=1–1–R121–R22…1−Rp2,where R12,R22,…Rp2=R square of endogenous variables in the equation model [68].The quantity ofQ2 has a value with a range of 0 < Q2 < 1, the closer the value to 1, the better the model [37]. It is also equivalent to the coefficient of total determination in path analysis. Furthermore, the effect and significance were tested based on the estimated value of the path coefficient and the critical point value (t-statistics or p value) at = 0.05. The relationship model between variables was measured by testing the direct correlation coefficient between variables. The results of testing the relationship between X and Y variables were indicated by the correlation coefficient, as well as t-statistics, and are also presented in the path diagram.


### 2.5. Class Assignment

To determine the class-required data for optimum results, class limits were calculated from the percentage of optimum results. After knowing the highest and lowest yields, the values were connected with the range of land characteristics values. The land suitability class and yield used referred to FAO [71], namely, class S1 (very suitable) when the values reach 80–100%, S2 was moderately suitable 60–80%, S3 marginally suitable 40–60%, and N not suitable <40% of the optimum capacity.The optimum yield was determined using the boundary line method. This method is carried out by drawing a boundary line on the graph of the relationship between yield and land characteristics to obtain optimum results. In the boundary line method according to Widiatmaka et al. [72], each land characteristic is plotted on the X-axis, while hybrid maize yields are plotted on the Y-axis. Bhat and Sujatha [42] stated that the preparation of the hybrid maize yield boundary line includes the preparation of a scatter diagram between the X and the Y variable, division of the X-axis into several classes of intervals, determination of the highest data points in each class interval, preparation of boundary lines based on the highest data points from each class interval, and drawing a line parallel to the X-axis according to the percentage of the result class.Furthermore, with the Microsoft Excel application tools, the boundary between classes S1 to S2, S2 to S3, and S3 toN were determined by the Data menu ⟶ What-if-Analysis ⟶ Goal Seek ⟶ Set the cell at the location containing the regression equation ⟶ to value fill with the result limit values S1 to S2, S2 to S3, and S3 to N ⟶ By changing cell ⟶ the location where the value of the characteristics of the land will be sought ⟶ Ok. On location “By changing cell,” the number being searched will appear, and at the location, “set cell” will be equal to the limit value of the result.


## 2.1. Study Area

The study area extends from 0°28′5.6″–0″57′30.02″ N to 122°8′34.25″–122°43′10.41″E (Figure1) on a scale of 1 : 40,000, which is located in the agricultural land of Boalemo Regency, Gorontalo Province, Indonesia. The maximum air temperature was 28.19°C and the minimum was 26.79°C with an average of 28.01°C. Meanwhile, the maximum rainfall was 1,849 mm and the minimum was 1,246 mm with an average of 1,478 mm [45]. The wet and dry seasons last for 3 months and 5 months, respectively. The soil mapping carried out by Ritung et al. [46] at a scale of 1 : 50,000 become the initial reference for determining 35 soil units, where each unit has information on land characteristics, namely, effective depth, drainage, texture, pH, cation exchange capacity, base saturation, landform, parent material, relief, and land unit area. Meanwhile, the new soil unit is 1 : 40,000 in scale, and there has been a change in the agricultural land use existing. This indicated that the slope class of 8–15% or hilly is more dominant in the study area with a percentage of 29.77% and slopes >40% or mountainous which is only 2.67%. Furthermore, the dry land is dominant with a value of 59.86% and a little shrub which was only 9.21%, while the dominant Typic Haplustalfs of soil subgroup classification was 22.47%; then, the Fluventic Haplustepts was 21.31% and very little Vertic Haplustepts of soil subgroup classification was 0.04% only (Figure 1).Figure 1
Study area.


## 2.2. Dataset Collection for Land Quality and Land Characteristics

The quality and characteristics of the land in this study refer to Wahyunto et al. [47], consisting of 10 land qualities and 24 characteristics. The set of temperature land quality is determined from the characteristics of the annual average air temperature, while the land quality water availability is determined from the characteristics of annual rainfall, wet months, dry months, and the length of the growth period (LGP). Land quality oxygen availability is determined from soil drainage characteristics, rooting conditions are determined from the soil texture, coarse material, and soil effective depth, and nutrient retention is identified from the pH value, C-organic, cation exchange capacity (CEC), and base saturation. Furthermore, the available nutrient is determined from the characteristics of total N, P, and exchangeable K, the sodicity is obtained from the exchangeable sodium percentage (ESP), while erosion hazard is determined from slopes and soil erosion. The quality of the flood-hazard land is determined by identifying the height and the duration of the inundation, while preparation is carried out from the characteristics of outcrops and surface rocks. The selection of this set of land qualities and characteristics is based on the availability of data and their impact on maize production [26].Data on average annual air temperature and rainfall for 10 years (2010–2021) were collected from different climate stations, namely, the Bandungrejo with 0°41′ N–122°38′ E, the elevation 40 m asl, while Harapan has 0°42′ N–122°29′ E and an elevation of 37 m asl. It also includes Lakeya Rain Post with 0°42.82′ N–122°32.07′ E, 32 m asl, Mohiyolo has 0°46.41′ N–122°26.41′ E and an elevation of 39 m asl, Saritani 0°46.45′ N–122°20.40′ E, with 26 m asl, Tangkobu 0°37.25′ N–122°36.36′ E, 25 m asl, Bubaa 0°31.36′ N–122°33.39′ E, 16 m asl, Wonggahu 0°38′ N–122°33′ E, 35 m asl, and Sambati Rain Post with 0°31.184′ N–122°27.074′ E, and an elevation of 9 m asl managed by BWS II Sulawesi. Furthermore, these data determined wet months (>200 mm) and dry months (<100 mm), which refers to the Oldeman and Darmiyati criteria [48]. The land water balance was determined using the Thornwaite and LGP methods based on the number of surplus and deficit rainy days [49].Based on the previous soil unit [46], then these soil units were detailed again by adding 32 of soil units to be surveyed and observed, making up to 67 soil units in the area as shown in the legend Figure 1. Soil characteristics such as drainage, coarse material, effective soil depth, slope, inundation height and duration, rock outcrops, and surface rocks were determined by conducting soil profile descriptions and direct observation on 67 pedons referring to the description guidelines in the field [50]. Meanwhile, soil erosion was determined by the USLE method [51]. Other soil characteristics were further analyzed in the soil laboratory using samples from each pedon.Soil samples were dried for 3 days and sieved through a 2-mesh sieve. The method of soil physics laboratory analysis was carried out according to the procedures by Kurnia et al. [52]. Based on this procedure, soil texture was analyzed in terms of sand, clay, and silt fractions using the pipette method, while soil moisture storage was evaluated using the gravimetric method that can be applied in water balance analysis. The method of soil chemistry laboratory analysis was carried out according to the procedures by Eviyati and Sulaiman [53]. The soil pH was determined with a pH meter in a 1 : 2.5 soil and water solution, while the organic C content was assessed using the Walkley and Black method. The total N was assessed using the Kjeldahl method, while the available P content was measured using the Olsen method. The basic cations and CEC were extracted with 1N NH4OAc pH 7.0 (ammonium acetate) on a dry sample of 105°C. The base saturation was determined by calculating the percentage of basic cations number with CEC, and ESP was evaluated using the percentage ratio of sodium to CEC [54, 55]. Subsequently, the data from the chemical analysis were averaged to a depth of 0–30 cm using the weighted averaging technique. The framework of this study is presented in Figure 2.Figure 2
Research framework.


## 2.3. Dataset Collection for Hybrid Maize Yield

The areas currently planted with hybrid maize were identified and blocks with a size of 2.5 m × 2.5 m were made in each map unit (Figure1). Maize plants in each block passed through standardized management according to farmers’ technology. After harvesting, weighting was carried out to obtain hybrid maize yield data from the results of tiles on each land unit. Subsequently, the results were calculated using the following formula [56]:(1)Yt=HxA6.25m2.Meanwhile, productivity is calculated using the following formula [56]:(2)Ytha−1=Hx1.64x56.73100,where Y is the hybrid maize yield, H is the tile yield (kg), A is the maize area 1 per hectare (ha), and 1.64 and 56.73 are the constants.


## 2.4. Selection of Land Quality and Land Characteristics

The quality and characteristics of the land in the suitability criteria are used as presented in Tables1 and 2, which show brief statistics. Generally, most data are relatively homogenous, except for coarse material, available P, slopes, soil erosion, height and inundation, as well as rock outcrops and surface rocks. The selection used the partial least squares of the structural equation model (PLS-SEM) refers to Hair et al. [38] with tools SmartPLS, where land quality and characteristics were selected as the latent and manifest variables, respectively. The analysis in PLS-SEM has 2 main steps, namely, the measurement model (outer model) and the structural model test (inner model).Table 1
Latent variables and indicators used in this study.
Latent variablesIndicatorsData sourcesNotationLand qualityNotationLand characteristicsX1Temperature (t)X1.1Temperature[45]X2Water availability (wa)X2.1Rainfall[45]X2.2Wet monthRainfall >200 mmX2.3Dry monthRainfall <100 mmX2.4Long growth period (LGP)Water balance (Thornwaite method), soil moisture storage (gravimetric method), water surplus, and deficit daysX3Oxygen availability (oa)X3.1DrainageSoil survey and land observationX4Rooting condition (rc)X4.1TexturePipet methodX4.1.1Sand fractionX4.1.2Silt fractionX4.1.3ClayX4.2Coarse materialSoil survey and land observationX4.3Effective depthX5Nutrient retention (nr)X5.1pH, H2OpH meter (1 : 2.5)X5.2pH, KClX5.3Organic CWalkley and Black methodX5.4Cation exchange capacity (CEC)1N NH4OAc, pH 7.0, extractedX5.5Base saturationCalculationX6Nutrient availability (na)X6.1TotalNKjeldahl methodX6.2P availabilityOlsen methodX6.3K exchangeable1N NH4OAc, pH 7.0, extractedX7Sodicity (xn)X7.1Exchangeable sodium percentage (ESP)CalculationX8Erosion hazard (eh)X8.1SlopesSoil survey and land observationX8.2Soil erosionX9Flooding hazard (fh)X9.1Inundation heightSoil survey and land observationX9.2Inundation periodX10Land preparation (lp)X10.1Rock outcropsSoil survey and land observationX10.2Surface rockYHybrid maize yieldY.1Hybrid maize yieldTile box methodsTable 2
Brief statistics of land quality and characteristics.
Latent variables/indicatorsUnitnMinMedianMeanMaxSDX1 (temperature)X1.1 (temperature)°C6726.7927.8028.0128.190.63X2 (water availability)X2.1 (rainfall)Mm671,246.001,533.421,478.001,849.00232.69X2.2 (wet month)Month670.001.031.003.000.85X2.3 (dry month)Month672.003.394.005.001.06X2.4 (LGP)Day67211.00246.00214.00304.0044.54X3 (oxygen availability)X3.1 (drainage)Class670.003.764.006.001.82X4 (rooting conditions)X4.1 (texture)Class671.002.212.005.000.99X4.1.1 (sand fraction)%675.0041.5843.0081.3318.51X4.1.2 (silt fraction)%677.3327.3124.5051.5011.54X4.1.3 (clay)%6711.3331.9030.0056.3312.72X4.2 (coarse material)%675.0017.2710.0070.0016.58X4.3 (effective depth)Cm6710.0074.5574.00160.0036.40X5 (nutrient retention)X5.1 (pH, H2O)675.005.925.907.150.52X5.2 (pH, KCl)674.355.245.176.600.56X5.3 (organic C)%670.410.850.772.350.39X5.4 (CEC)Cmol(+)/kg678.9424.8922.4359.5711.41X5.5 (base saturation)%6745.0356.2252.8581.899.76X6 (nutrient availability)X6.1 (totalN)%670.040.090.080.270.04X6.2 (P availability)Mg/kg670.738.623.7758.6712.61X6.3 (K exchangeable)Cmol(+)/kg670.070.390.241.920.42X7 (sodicity)X7.1 (ESP)%670.767.066.2024.175.62X8 (erosion hazard)X8.1 (slopes)%671.009.586.0025.007.29X8.2 (soil erosion)Tons/ha/year673.66334.51110.271772.43439.08X9 (flooding hazard)X9.1 (inundation height)Cm670.007.580.0050.0017.10X 9.2 (inundation period)Day670.000.640.005.001.52X10 (land preparation)X10.1 (rock outcrops)%670.006.640.0045.0011.56X10.2 (surface rock)%670.006.580.0045.0011.59Y (hybrid maize yield)Ton/ha672.854.954.688.071.15n, the number of the land unit; min, minimum; max, maximum; SD, standard deviation; LGP, long growth period; CEC, cation exchange capacity; ESP, exchangeable sodium percentage.Step 1 consists of validity and reliability tests, wherein the validity test is conducted with convergent and discriminant validity. The convergent validity is in form of outer loadings (loading factor) and average variance extracted (AVE), while discriminant validity is in form of cross-loading and the Fornell–Larcker criterion. Meanwhile, the reliability test uses composite reliability and Cronbach’s alpha.Convergent validity was observed from the magnitude of the outer loading and the AVE value of each indicator on the latent variable. The validity was calculated according to the following equation [57–60]:(3)xi=λxiξ1+δi,yi=λyiη1+εi,where x and y are the exogenous (ξ) and endogenous (η) latent variable indicators, λx and λy are the loading factors, δ and ε are the residual/measurement errors or noise.Meanwhile, the average variance extracted (AVE) value was calculated using the following equation [61–65]:(4)AVE=∑λ2i∑λ2i+∑ivarεi,where λi2  is the loading factor, var  is the variance, and εi is the error variance.The loading factor of an indicator with the highest value is the strongest or most important measure in reflecting the latent variable. In this study, the loading factor value is >0.70 for selecting best land characteristics, but values ranging from 0.50 to 0.60 can still be tolerated with at-statistic >1.96 or a small p value of 0.05 [37, 66]. Meanwhile, the AVE value used was more than 0.50, showing that the convergent validity on the latent variable has been reached.The discriminant validity test used the cross-loading value and the Fornell–Larker criterion to test discriminantly valid indicators in explaining or reflecting latent variables. When the correlation between the latent variables and the core measurement of each indicator is high, the latent variable can predict the indicator better and is considered valid. The discriminant validity is measured by the square root of the average variance extracted, which will be compared with the correlation value between variables. The value calculated based on the square root of AVE must be higher than the correlation between constructs [61]. The equation is expressed as follows [61, 63–65, 67]:(5)SquarerootofAVE=∑λ2i∑λ2i+∑ivarεi,where λi2 is the loading factor, var is the variance, and εi is the error variance.Furthermore, the composite reliability and Cronbach’s alpha were used to test the reliability value between indicators of the latent variables. They are considered good and accepted when the value is >0.70 and has a minimum value of 0.60 [37]. The composite reliability value is calculated using the following equation [62, 65, 68, 69]:(6)ρc=∑λi2∑λi2+∑ivarεi,where λi is the loading factor, var is the variance, and εi is the error variance.Meanwhile, the value of Cronbach’s Alpha is calculated according to the following equation [65, 70]:(7)α=∑p≠p′corXpq.Xp′qpq+∑p≠p′corXpq.Xp′qpqpq−1,where Pq is the number of indicators or manifest variables and q is the indicator block.For step 2, the structural model testing (inner model) was carried out after the relationship model was built in line with the observed data and the overall suitability, namely, goodness of fit. The structural equation (inner model) is as follows [59, 60, 62]:(8)Ηj=γjξ1+γjξ2+…γjξn+ςj,where ηj is the endogenous variable vector (dependent), γjξ1+γjξ2+…γjξn is the exogenous latent variable vector, and ςj is the residual vector (error).Meanwhile, the determinant coefficient and goodness of fit (Q2) were calculated using the following equation [62, 64, 70]:(9)Q2Predictiverelevance=1–1–R121–R22…1−Rp2,where R12,R22,…Rp2=R square of endogenous variables in the equation model [68].The quantity ofQ2 has a value with a range of 0 < Q2 < 1, the closer the value to 1, the better the model [37]. It is also equivalent to the coefficient of total determination in path analysis. Furthermore, the effect and significance were tested based on the estimated value of the path coefficient and the critical point value (t-statistics or p value) at = 0.05. The relationship model between variables was measured by testing the direct correlation coefficient between variables. The results of testing the relationship between X and Y variables were indicated by the correlation coefficient, as well as t-statistics, and are also presented in the path diagram.


## 2.5. Class Assignment

To determine the class-required data for optimum results, class limits were calculated from the percentage of optimum results. After knowing the highest and lowest yields, the values were connected with the range of land characteristics values. The land suitability class and yield used referred to FAO [71], namely, class S1 (very suitable) when the values reach 80–100%, S2 was moderately suitable 60–80%, S3 marginally suitable 40–60%, and N not suitable <40% of the optimum capacity.The optimum yield was determined using the boundary line method. This method is carried out by drawing a boundary line on the graph of the relationship between yield and land characteristics to obtain optimum results. In the boundary line method according to Widiatmaka et al. [72], each land characteristic is plotted on the X-axis, while hybrid maize yields are plotted on the Y-axis. Bhat and Sujatha [42] stated that the preparation of the hybrid maize yield boundary line includes the preparation of a scatter diagram between the X and the Y variable, division of the X-axis into several classes of intervals, determination of the highest data points in each class interval, preparation of boundary lines based on the highest data points from each class interval, and drawing a line parallel to the X-axis according to the percentage of the result class.Furthermore, with the Microsoft Excel application tools, the boundary between classes S1 to S2, S2 to S3, and S3 toN were determined by the Data menu ⟶ What-if-Analysis ⟶ Goal Seek ⟶ Set the cell at the location containing the regression equation ⟶ to value fill with the result limit values S1 to S2, S2 to S3, and S3 to N ⟶ By changing cell ⟶ the location where the value of the characteristics of the land will be sought ⟶ Ok. On location “By changing cell,” the number being searched will appear, and at the location, “set cell” will be equal to the limit value of the result.


## 3. Results and Discussion

### 3.1. Land Quality and Characteristics Controlling Hybrid Maize Yield

#### 3.1.1. Validity Test Result

Table3 shows the loading factor of the variables, where most indicators were more than the critical limit of 0.70 with a 95% confidence level (P>1.960). Therefore, these variables are highly recommended and the indicators are considered convergently valid. In the soil texture indicator for the latent variable of root conditions as well as the cation exchange capacity (CEC) and base saturation (BS) indicators for nutrient retention, the loading factor was below the tolerance value of 0.50 at the 95% confidence level (P<1.960); hence, it was not used. This implies that the indicators have not been established or explained properly because the standard value of the loading factor must be greater than or equal to 0.50 [37, 38, 66].Table 3
Outer loading (loading factor) and the average variance extracted from study variables.
Indicators (land characteristics)Latent variables (land quality)Loading factorst statStatusAVEX1.1 (temperature)⟶X1 (temperature)1.000∗∗11.192Valid1.000X2.1 (rainfall)⟶X2 (water availability)0.8380.085Valid0.906X2.2 (wet month)⟶0.9890.999ValidX2.3 (dry month)⟶0.8500.428ValidX2.4 (LGP)⟶0.993∗1.431ValidX3.1 (drainage)⟶X3 (oxygen availability)1.0000.000Valid1.000X4.1 (texture)⟶X4 (rooting condition)0.0130.066Invalid0.573X4.2 (coarse material)⟶0.9211.086ValidX4.3 (effective depth)⟶−0.8991.047ValidX5.1 (pH, H2O)⟶X5 (nutrient retention)0.6470.857Valid0.360 (invalid)X5.2 (pH, KCl)⟶0.570∗∗1.973ValidX5.3 (organic C)⟶0.831∗∗3.135ValidX5.4 (CEC)⟶0.436∗1.381InvalidX5.5 (base saturation)⟶0.3650.845InvalidX6.1 (totalN)⟶X6 (nutrient availability)0.760∗∗3.226Valid0.585X6.2 (P availability)⟶0.587∗1.385ValidX6.3 (K exchangeable)⟶0.897∗∗6.907ValidX7.1 (ESP)⟶X7 (sodicity)1.0000.000Valid1.000X8.1 (slopes)⟶X8 (erosion hazard)0.954∗∗21.438Valid0.932X8.2 (soil erosion)⟶0.941∗∗18.308ValidX9.1 (inundation height)⟶X9 (flooding hazard)0.984∗∗4.213Valid0.984X9.2 (inundation period)⟶0.985∗∗3.918ValidX10.1 (rock outcrops)⟶X10 (land preparation)0.998∗∗189.133Valid0.995X10.2 (surface rock)⟶0.998∗∗320.273ValidAVE, average variance extracted; LGP, long growth period; CEC, cation exchange capacity; ESP, exchangeable sodium percentage.The average variance extracted (AVE) value of almost all variables was greater than 0.50; therefore, it was considered convergently valid [61, 62]. The AVE value of the available nutrient variable was not valid due to the smaller value of the loading factor for the CEC and BS indicators of 0.50, leading to the removal of both indicators. A similar result was discovered in the root condition variable, although the AVE value was greater than 0.50, while the soil texture indicator was not used because the loading factor value is only 0.013.The measurement of the Fornell–Larcker criterion and cross-loading was used as the basis for assessing the discriminant validity of the model. The calculation results on the Fornell–Larcker criterion in Table4 show that the average of the tested variables has a higher square root of AVE than the correlation value; hence, the latent variable was considered discriminantly valid. The square root value of the AVE must be greater than its correlation value with other constructs to meet the discriminant validity requirements [69, 73]. The average loading factor value for the latent variable indicator was above that of others as shown in Table 5.Table 4
Fornell–Larker criterion test.
X1X2X3X4X5X6X7X8X9X10YX11.000X20.9400.952X30.0590.1491.000X40.0820.030−0.1620.757X5−0.360−0.239−0.103−0.3680.600X6−0.0690.0210.012−0.5180.6940.765X70.3820.4290.0840.228−0.030−0.2171.000X80.019−0.082−0.5010.285−0.317−0.370−0.0090.966X9−0.104−0.0330.237−0.2040.0730.0900.202−0.2500.992X100.1980.093−0.2230.873−0.303−0.5380.3620.304−0.1260.998Y0.0180.1520.169−0.5780.3870.456−0.016−0.5170.164−0.5681.000X1, temperature; X2, water availability; X3, oxygen availability; X4, rooting condition; X5, nutrient retention; X6, nutrient availability; X7, sodicity; X8, erosion hazard; X9, flooding hazard; X10, land preparation;Y, maize hybrid yield.Table 5
Cross-loading of latent variables to indicators.
X1, temperature; X2, water availability; X3, oxygen availability; X4, rooting condition; X5, nutrient retention; X6, nutrient availability; X7, sodicity; X8, erosion hazard; X9, flooding hazard; X10, land preparation; Y, hybrid maize yield; X1.1, temperature; X2.1, rainfall; X2.2, the wet month; X2.3, the dry month; X2.3, long growth period; X3.1, drainage; X4.1, texture; X4.2, coarse material; X4.3, effective depth; X5.1, organic C; X5.2, cation exchanges capacity; X5.3, base saturation; X6.1, totalN; X6.2, P availability; X6.3, K exchangeable; X7.1, the exchange sodium percentage; X8.1, slopes; X8.2, soil erosion; X9.1, inundation height; X9.2, inundation period; X10.1, rock outcrops; X10.2, surface rock; Y.1, hybrid maize yield. The yellow color shows the loading factor value for the indicators of the latent variables above the loading factor values for other latent variable indicators (>0.5), while the red color indicates the opposite (<0.5).


#### 3.1.2. Reliability Test Result

The variables are considered reliable because composite reliability and Cronbach’s alpha coefficient on average were more than 0.7 [61, 74], as shown in Table 6. However, certain indicators still had values less than 0.6, namely, soil texture, but the indicators used are reliable and adequate in forming the latent variables.Table 6
Composite reliability and Cronbach’s alpha test.
Indicators (land characteristics)Cronbach’s alphaComposite reliabilityX1.1 (temperature)1.0001.000X2.1 (rainfall)0.9750.965X2.2 (wet month)X2.3 (dry month)X2.4 (long growth periods)X3.1 (drainage)1.0001.000X4.1 (texture)0.002nor−1.055norX4.2 (coarse material)X4.3 (effective depth)X5.1 (pH, H2O)0.7180.628X5.2 (pH, KCl)X5.3 (organic C)X5.4 (cation exchange capacity)X5.5 (base saturation)X6.1 (totalN)0.8050.681X6.2 (P availability)X6.3 (K exchangeable)X7.1 (exchangeable sodium percentage)1.0001.000X8.1 (slopes)0.9650.928X8.2 (soil erosion)X9.1 (inundation height)0.9920.984X9.2 (inundation period)X10.1 (rock outcrops)0.9980.995X10.2 (surface rock)nor, not reliable.The highest composite reliability and Cronbach’s alpha coefficients were obtained for the variables of temperature, oxygen availability, and toxicity of 1 for the variables to be very reliable. The lowest coefficient was obtained on the root condition variable; therefore, the variable was not reliable. According to Bagozzi and Yi [75] and Hair et al. [74], variables are considered good and accepted when the value is >0.70.The remaining variables are water availability, nutrient retention, available nutrients, erosion hazard, and land preparation variables. The coefficient of composite reliability and Cronbach’s alpha >0.6; therefore, the variable is considered reliable. The minimum value of composite reliability and Cronbach’s alpha coefficients was 0.60 [61, 74, 75].


#### 3.1.3. Structural Model Test (Inner Models)

Land characteristics that have a significant correlation with hybrid maize yields show a high level of contribution to land quality in influencing hybrid maize yields as indicated in Figure3. The figure shows a structural model of the relationship between indicator variables, namely, 24 land characteristics, rectangular yellow, and latent variables, including 10 land qualities maize yield and oval blue. It also shows a model for the relationship between latent variables such as land qualities and maize yield, as well as loading figures. The factor for each indicator and path coefficient for land qualities has a direct effect on the value of maize yields.Figure 3
Path coefficient of land quality on hybrid maize yield.For example, X8 (erosion hazard) significantly determines the hybrid maize yield with a path coefficient of −0.392. The negative sign indicates that the erosion hazard is negatively related to maize yield, where the higher the erosion hazard, the lower the maize yield. Furthermore, nutrient retention (X5) contributes to the diversity of hybrid maize yields with a path coefficient of 0.252. A positive sign indicates that nutrient retention is positively related to maize yield, where the higher the value of nutrient retention, followed by the maize yield.The results of this path analysis indicated that the land quality that can be a predictor of maize yield diversity were oxygen availability (X3), nutrient retention (X5), nutrient availability (X6), erosion hazard (X8), and land preparation (X 10). Figure2 indicates that only 8 of the 24 indicators explain latent variance at the 5% real test level. The 8 indicators were coarse material and effective soil depth as an indicator of rooting condition, organic carbon content as nutrient retention, exchangeable K as nutrient availability, soil erosion and slope as erosion hazard, as well as rock outcrop and surface rock as an indicator of land preparation. It was also indicated that the drainage loading factor was unable to explain the diversity of oxygen availability. Therefore, oxygen availability cannot be used as a land quality because there are no indicators that can represent it. Only the land qualities of nutrient retention (X5), nutrient availability (x6), erosion hazard (x8), and land preparation (X10) were used next.The indicators of land characteristics for effective depth, organic C, totalN, and exchangeable K have a fairly strong positive relationship and a very significant effect on hybrid maize yields. In this relationship, an increase in these parameters by 1% will be followed by a rise in hybrid maize yields of 39% to 57.7%. According to Wirosoedarmo et al. [4], effective depth affects root growth and development, making plants grow and develop properly. Moreover, the levels of organic C, total N, and CEC are influenced by soil organic matter [76], while potassium plays a role in the growth and development of maize [77].Indicators of rough soil characteristics, slopes, soil erosion, as well as surface and rock outcrops, have a strong negative relationship with a very significant effect on hybrid maize yields. In this relationship, 1% decrease in coarse material, slope, soil erosion, as well as surface, and rock outcrop is followed by an increase in hybrid maize yields by 39–57.7%.


### 3.2. Optimum Hybrid Maize Yield by the Land Quality and Land Characteristics

Table7 shows the mathematical equations for each land characteristic and also the optimum hybrid maize yield for the land equation. Figure 4 shows the diagram of the relationship between land characteristics as an independent variable (X) and maize yield as an independent variable Y. Model fitting indicates that the quadratic equation is sufficient to describe the condition of data distribution.Table 7
The optimum hybrid maize yield by the land quality and land characteristics.
Land quality/land characteristicsOptimum yield (ton/ha)Yield equationR2Rooting condition (rc)Coarse material8.17Y = 0.0025900x2 − 0.2568578x + 11.90935760.96Effective depth8.46Y = −0.0008354x2 + 0.29100569x − 1.39574960.97Nutrient retention (nr)Organic carbon8.46Y = −25.492979x2 + 47.9575089X − 8.98950670.97Nutrient availability (na)TotalN8.54Y = −305.5574654X2 + 155.8690907X − 2.74396401.00K exchangeable5.58Y = −10.6697409X2 + 18.5239943X + 2.31792890.95Erosion hazard (eh)Slopes8.54Y = 0.0183X2 − 0.9559X + 14.8060.92Soil erosion8.17y = 0.0000184X2 − 0.0198647X + 9.05375690.89Land preparation (lp)Rock outcrops7.41Y = 0.0057496X2 − 0.3845867X + 8.62697850.92Surface rock7.41Y = 0.0057496X2 − 0.3945867X + 8.62697850.92Figure 4
Scatter diagram relationship among maize yield and land characteristics.The optimum of hybrid maize yield ranged from 5.58 to 8.54 ton/ha, where the highest yield was obtained from totalN and slopes of 8.54 ton/ha with an R2 value of 100% and 92%. Sutardjo et al. [78] showed that hybrid maize yields ranged from 7.43 to 9.2 ton/ha. This indicated that the optimum yield achieved is still within the range of hybrid maize yields that have been previously reported. Nitrogen is directly involved in the formation of amino acids, proteins, nucleic acids, enzymes, nucleoproteins, and alkaloids, which are needed in the plant growth process [79, 80]. An extremely high amount of N causes excessive vegetative growth, thereby making plants fall easily and increasing susceptibility to disease [81]. Meanwhile, the lack of N nutrients can limit cell division, enlargement [82], and the formation of chlorophyll, leading to stunted growth as well as yellowing of leaves [81].The lowest optimum yield was obtained from exchangeableK, which was only 5.58 ton/ha with an R2 value of 95%. This was presumably because the K content in the soil is very low, thereby affecting the hybrid maize yield. Potassium (K) is required by plants for physiological functions, including carbohydrate metabolism, enzyme activity, osmotic regulation, efficient water use, N uptake, protein synthesis, and assimilate translocation [83–86]. It also plays a role in improving the quality of crop yields [87, 83, 88].The optimum hybrid maize yield of the remaining is relatively diverse. This includes effective depth and organic carbon, which were both 8.46 ton/ha with anR2 value of 97%. Furthermore, coarse material and soil erosion were 8.17 ton/ha with an R2 value of 96% and 89%, while rock outcrops and surface rock were 7.41 ton/ha with an R2 value of 92%. The absence of coarse material >2 mm in diameter indicated that plant roots can grow freely on the surface or deeper parts of the soil [89] because the deeper the roots of the maize, the greater the maize yield [90, 91]. The addition of organic matter will increase maize yield [92–94] and organic C content [95] because soil organic matter is a strong positive predictor of yield [96]. Kane et al. [96] also stated that the addition of more organic matter will improve water retention, thereby reducing maize yield losses due to drought. The slope has a significant effect on soil degradation [97]. According to a previous study, erosion and maize yield are negatively correlated; hence, increased erosion will reduce maize productivity [98]. Soil erosion on flat land is slower surface runoff [99]. It was also reported that surface rocks and outcrops are the limiting factors in the suitability of maize plantations [100]. Therefore, a high percentage of rock outcrops will complicate land cultivation and plant root growth.


### 3.3. Land Suitability Criteria for Hybrid Maize Crops

Table8 shows the yield limit for each class from the calculation of the optimum yield, where the class range for each land characteristic is derived. Based on the optimum yield of the highest hybrid maize, there were 2 indicators, namely, the total N content and the slope. The total N indicator with a very suitable class (S1) was achieved when the value in the soil was greater than 0.11%, while in the moderately suitable class (S2), it was achieved when the total N in the soil ranges from 0.08 to 0.10%. In the marginally appropriate class (S3), the total N indicator was achieved when the total N in the soil ranges from 0.06 to 0.07%, while the not suitable class (N) was achieved when the content was less than 0.06%. On the slope indicator with class S1, it was obtained when the slope class ranges from 0 to 7.70%%, while class S2 was achieved when the slope class ranges from 7.71 to 11.84%. Furthermore, in classes S3 and N, it was obtained when the slope class ranged from 11.85 to 18.25% and greater than 18.25%, respectively.Table 8
Yield limits of hybrid maize and values obtained in each land suitability class interval by land quality and characteristics.
Land quality/land characteristicsYield limits (ton/ha)Value of land suitability criterion obtainedS1 − S2 (80% ×Yoptim)S2 − S3 (60% ×Yoptim)S3 −N(40% × Yoptim)S1S2S3NRooting condition (rc)Coarse material (%)8.176.054.040–13.5113.51–27.4827.48–52.41>52.41Effective depth (cm)8.466.374.29≥69.6649.36–69.6533.29–49.35<33.29Nutrient retention (nr)Organic carbon (%)8.466.374.29≥0.610.52–0.600.34–0.51<0.34Nutrient availability (na)TotalN (%)8.546.434.33≥0.110.08–0.100.06–0.07<0.06K exchangeable (cmol(+)/kg)5.584.422.98≥0.250.14–0.240.05–0.13<0.05Erosion hazard (eh)Slopes (%)8.546.434.330–7.707.71–11.8411.85–18.25>18.25Soil erosion (ton/ha/year)8.176.054.04≤55.3255.32–195.29195.30–605.57>605.57Land preparation (lp)Rock outcrops (%)7.415.693.970–4.464.47–13.1013.11–31.89>31.89Surface rock (%)7.415.693.970–4.464.47–13.1013.11–31.89>31.89S1, very suitable; S2, moderately suitable; S3, marginally suitable;N, not suitable.Based on the optimum yield of the lowest hybrid maize, only the exchangeableK was the indicator. In classes S1 and S2, it was achieved when the exchangeable K content in the soil was greater than 0.25 cmol(+)/kg and ranges from 0.14 to 0.24 cmol(+)/kg, respectively. Furthermore, class S3 was achieved when the exchangeable K content in the soil ranges from 0.05 to 0.13 cmol(+)/kg, while in class N, it was obtained when the exchangeable K content in the soil was less than 0.05 cmol(+)/kg. The remaining variables and indicators were relatively varied according to the optimum yield of hybrid maize and the range of land suitability classes obtained as presented in Table 8.Based on the relationship between the quality and characteristics of the selected land with optimum results, the criteria for hybrid maize land suitability were obtained as shown in Table8. These criteria described the actual state of achieving optimum, moderate, and minimum yields of hybrid maize in the field with values of 80%, 60%, and 40%, respectively. According to Sukarman et al. [101], the parameters used in the land suitability assessment must describe the actual conditions. This is due to the significant positive correlation between maize yield and land suitability class [102].The land suitability criteria for the new hybrid maize are fewer and have referred to the optimum yield. Meanwhile, the land quality and characteristics in this new criterion are less because it only consists of root conditions with characteristics of coarse material and effective depth, nutrient retention with organic C, and nutrient availability with totalN and K exchangeable. It also consists of erosion hazards with characteristics of slopes and soil erosion, as well as land preparation with surface rocks and rock outcrops only. The land qualities selected and maize yields consistent with the land potential are the basis for developing suitability criteria. This will reduce the land characteristics and make the evaluation process faster, cheaper, and easier with accurate results [26]. Some characteristics and land quality criteria were not made because they did not significantly affect the yield of hybrid maize. The number and distribution of the data were still limited, and the diversity of values was small or not measurable in the field [72].Compared to Wahyunto et al. [47], the new land suitability criteria for hybrid maize plants in Table 9 are more realistic in value with the conditions in the field and is based on the achievement of optimum yields. The current land suitability criteria are still general and not specific to maize yields [26], although the agronomic and yield potential of each maize variety differ, based on the diversity of characteristics and land quality in the field. There are still limitations on the use of these results for the development of hybrid maize in the Boalemo Regency because the setting is only based on land characteristics and optimum yields in this regency. Therefore, further investigation to expand the scope of the research area nationally with more diverse and contrasting land characteristic values is recommended to determine the effect on hybrid maize production.Table 9
Comparison of new and general land suitability criteria with land quality and characteristics.
Land quality/land characteristicsNew land suitability criterion of hybrid maizeLand suitability criterion of general maize [47]S1S2S3NS1S2S3NRooting condition (rc)Coarse material (%)0–13.5113.51–27.4827.48–52.41>52.41<1515–3535–55>55Effective depth (cm)≥69.6649.36–69.6533.29–49.35<33.29>6060–4040–25<25Nutrient retention (nr)Organic carbon (%)≥0.610.52–0.600.34–0.51<0.34>1.200.8–1.2<0.8—Nutrient availability (na)TotalN (%)≥0.110.08–0.100.06–0.07<0.06MoLoVLo—K exchangeable (cmol(+)/kg)≥0.250.14–0.240.05–0.13<0.05Mo-HiLoVLo—Erosion hazard (eh)Slopes (%)0–7.707.71–11.8411.85–18.25>18.25<88–1515–25>25Soil erosion (ton/ha/year)≤55.3255.32–195.29195.30–605.57>605.57—VLiLi-MoHe-VHeLand preparation (lp)Rock outcrops (%)0–4.464.47–13.1013.11–31.89>31.89<55–1515–40>40Surface rock (%)0–4.464.47–13.1013.11–31.89>31.89<55–1515–40>40S1, very suitable; S2, moderately suitable; S3, marginally suitable;N, not suitable; Hi, high; M, moderate; Lo, low; VLo, very low; He, heavy; Li, light; VLi, very light; VHe, very heavy.


## 3.1. Land Quality and Characteristics Controlling Hybrid Maize Yield

### 3.1.1. Validity Test Result

Table3 shows the loading factor of the variables, where most indicators were more than the critical limit of 0.70 with a 95% confidence level (P>1.960). Therefore, these variables are highly recommended and the indicators are considered convergently valid. In the soil texture indicator for the latent variable of root conditions as well as the cation exchange capacity (CEC) and base saturation (BS) indicators for nutrient retention, the loading factor was below the tolerance value of 0.50 at the 95% confidence level (P<1.960); hence, it was not used. This implies that the indicators have not been established or explained properly because the standard value of the loading factor must be greater than or equal to 0.50 [37, 38, 66].Table 3
Outer loading (loading factor) and the average variance extracted from study variables.
Indicators (land characteristics)Latent variables (land quality)Loading factorst statStatusAVEX1.1 (temperature)⟶X1 (temperature)1.000∗∗11.192Valid1.000X2.1 (rainfall)⟶X2 (water availability)0.8380.085Valid0.906X2.2 (wet month)⟶0.9890.999ValidX2.3 (dry month)⟶0.8500.428ValidX2.4 (LGP)⟶0.993∗1.431ValidX3.1 (drainage)⟶X3 (oxygen availability)1.0000.000Valid1.000X4.1 (texture)⟶X4 (rooting condition)0.0130.066Invalid0.573X4.2 (coarse material)⟶0.9211.086ValidX4.3 (effective depth)⟶−0.8991.047ValidX5.1 (pH, H2O)⟶X5 (nutrient retention)0.6470.857Valid0.360 (invalid)X5.2 (pH, KCl)⟶0.570∗∗1.973ValidX5.3 (organic C)⟶0.831∗∗3.135ValidX5.4 (CEC)⟶0.436∗1.381InvalidX5.5 (base saturation)⟶0.3650.845InvalidX6.1 (totalN)⟶X6 (nutrient availability)0.760∗∗3.226Valid0.585X6.2 (P availability)⟶0.587∗1.385ValidX6.3 (K exchangeable)⟶0.897∗∗6.907ValidX7.1 (ESP)⟶X7 (sodicity)1.0000.000Valid1.000X8.1 (slopes)⟶X8 (erosion hazard)0.954∗∗21.438Valid0.932X8.2 (soil erosion)⟶0.941∗∗18.308ValidX9.1 (inundation height)⟶X9 (flooding hazard)0.984∗∗4.213Valid0.984X9.2 (inundation period)⟶0.985∗∗3.918ValidX10.1 (rock outcrops)⟶X10 (land preparation)0.998∗∗189.133Valid0.995X10.2 (surface rock)⟶0.998∗∗320.273ValidAVE, average variance extracted; LGP, long growth period; CEC, cation exchange capacity; ESP, exchangeable sodium percentage.The average variance extracted (AVE) value of almost all variables was greater than 0.50; therefore, it was considered convergently valid [61, 62]. The AVE value of the available nutrient variable was not valid due to the smaller value of the loading factor for the CEC and BS indicators of 0.50, leading to the removal of both indicators. A similar result was discovered in the root condition variable, although the AVE value was greater than 0.50, while the soil texture indicator was not used because the loading factor value is only 0.013.The measurement of the Fornell–Larcker criterion and cross-loading was used as the basis for assessing the discriminant validity of the model. The calculation results on the Fornell–Larcker criterion in Table4 show that the average of the tested variables has a higher square root of AVE than the correlation value; hence, the latent variable was considered discriminantly valid. The square root value of the AVE must be greater than its correlation value with other constructs to meet the discriminant validity requirements [69, 73]. The average loading factor value for the latent variable indicator was above that of others as shown in Table 5.Table 4
Fornell–Larker criterion test.
X1X2X3X4X5X6X7X8X9X10YX11.000X20.9400.952X30.0590.1491.000X40.0820.030−0.1620.757X5−0.360−0.239−0.103−0.3680.600X6−0.0690.0210.012−0.5180.6940.765X70.3820.4290.0840.228−0.030−0.2171.000X80.019−0.082−0.5010.285−0.317−0.370−0.0090.966X9−0.104−0.0330.237−0.2040.0730.0900.202−0.2500.992X100.1980.093−0.2230.873−0.303−0.5380.3620.304−0.1260.998Y0.0180.1520.169−0.5780.3870.456−0.016−0.5170.164−0.5681.000X1, temperature; X2, water availability; X3, oxygen availability; X4, rooting condition; X5, nutrient retention; X6, nutrient availability; X7, sodicity; X8, erosion hazard; X9, flooding hazard; X10, land preparation;Y, maize hybrid yield.Table 5
Cross-loading of latent variables to indicators.
X1, temperature; X2, water availability; X3, oxygen availability; X4, rooting condition; X5, nutrient retention; X6, nutrient availability; X7, sodicity; X8, erosion hazard; X9, flooding hazard; X10, land preparation; Y, hybrid maize yield; X1.1, temperature; X2.1, rainfall; X2.2, the wet month; X2.3, the dry month; X2.3, long growth period; X3.1, drainage; X4.1, texture; X4.2, coarse material; X4.3, effective depth; X5.1, organic C; X5.2, cation exchanges capacity; X5.3, base saturation; X6.1, totalN; X6.2, P availability; X6.3, K exchangeable; X7.1, the exchange sodium percentage; X8.1, slopes; X8.2, soil erosion; X9.1, inundation height; X9.2, inundation period; X10.1, rock outcrops; X10.2, surface rock; Y.1, hybrid maize yield. The yellow color shows the loading factor value for the indicators of the latent variables above the loading factor values for other latent variable indicators (>0.5), while the red color indicates the opposite (<0.5).


### 3.1.2. Reliability Test Result

The variables are considered reliable because composite reliability and Cronbach’s alpha coefficient on average were more than 0.7 [61, 74], as shown in Table 6. However, certain indicators still had values less than 0.6, namely, soil texture, but the indicators used are reliable and adequate in forming the latent variables.Table 6
Composite reliability and Cronbach’s alpha test.
Indicators (land characteristics)Cronbach’s alphaComposite reliabilityX1.1 (temperature)1.0001.000X2.1 (rainfall)0.9750.965X2.2 (wet month)X2.3 (dry month)X2.4 (long growth periods)X3.1 (drainage)1.0001.000X4.1 (texture)0.002nor−1.055norX4.2 (coarse material)X4.3 (effective depth)X5.1 (pH, H2O)0.7180.628X5.2 (pH, KCl)X5.3 (organic C)X5.4 (cation exchange capacity)X5.5 (base saturation)X6.1 (totalN)0.8050.681X6.2 (P availability)X6.3 (K exchangeable)X7.1 (exchangeable sodium percentage)1.0001.000X8.1 (slopes)0.9650.928X8.2 (soil erosion)X9.1 (inundation height)0.9920.984X9.2 (inundation period)X10.1 (rock outcrops)0.9980.995X10.2 (surface rock)nor, not reliable.The highest composite reliability and Cronbach’s alpha coefficients were obtained for the variables of temperature, oxygen availability, and toxicity of 1 for the variables to be very reliable. The lowest coefficient was obtained on the root condition variable; therefore, the variable was not reliable. According to Bagozzi and Yi [75] and Hair et al. [74], variables are considered good and accepted when the value is >0.70.The remaining variables are water availability, nutrient retention, available nutrients, erosion hazard, and land preparation variables. The coefficient of composite reliability and Cronbach’s alpha >0.6; therefore, the variable is considered reliable. The minimum value of composite reliability and Cronbach’s alpha coefficients was 0.60 [61, 74, 75].


### 3.1.3. Structural Model Test (Inner Models)

Land characteristics that have a significant correlation with hybrid maize yields show a high level of contribution to land quality in influencing hybrid maize yields as indicated in Figure3. The figure shows a structural model of the relationship between indicator variables, namely, 24 land characteristics, rectangular yellow, and latent variables, including 10 land qualities maize yield and oval blue. It also shows a model for the relationship between latent variables such as land qualities and maize yield, as well as loading figures. The factor for each indicator and path coefficient for land qualities has a direct effect on the value of maize yields.Figure 3
Path coefficient of land quality on hybrid maize yield.For example, X8 (erosion hazard) significantly determines the hybrid maize yield with a path coefficient of −0.392. The negative sign indicates that the erosion hazard is negatively related to maize yield, where the higher the erosion hazard, the lower the maize yield. Furthermore, nutrient retention (X5) contributes to the diversity of hybrid maize yields with a path coefficient of 0.252. A positive sign indicates that nutrient retention is positively related to maize yield, where the higher the value of nutrient retention, followed by the maize yield.The results of this path analysis indicated that the land quality that can be a predictor of maize yield diversity were oxygen availability (X3), nutrient retention (X5), nutrient availability (X6), erosion hazard (X8), and land preparation (X 10). Figure2 indicates that only 8 of the 24 indicators explain latent variance at the 5% real test level. The 8 indicators were coarse material and effective soil depth as an indicator of rooting condition, organic carbon content as nutrient retention, exchangeable K as nutrient availability, soil erosion and slope as erosion hazard, as well as rock outcrop and surface rock as an indicator of land preparation. It was also indicated that the drainage loading factor was unable to explain the diversity of oxygen availability. Therefore, oxygen availability cannot be used as a land quality because there are no indicators that can represent it. Only the land qualities of nutrient retention (X5), nutrient availability (x6), erosion hazard (x8), and land preparation (X10) were used next.The indicators of land characteristics for effective depth, organic C, totalN, and exchangeable K have a fairly strong positive relationship and a very significant effect on hybrid maize yields. In this relationship, an increase in these parameters by 1% will be followed by a rise in hybrid maize yields of 39% to 57.7%. According to Wirosoedarmo et al. [4], effective depth affects root growth and development, making plants grow and develop properly. Moreover, the levels of organic C, total N, and CEC are influenced by soil organic matter [76], while potassium plays a role in the growth and development of maize [77].Indicators of rough soil characteristics, slopes, soil erosion, as well as surface and rock outcrops, have a strong negative relationship with a very significant effect on hybrid maize yields. In this relationship, 1% decrease in coarse material, slope, soil erosion, as well as surface, and rock outcrop is followed by an increase in hybrid maize yields by 39–57.7%.


## 3.1.1. Validity Test Result

Table3 shows the loading factor of the variables, where most indicators were more than the critical limit of 0.70 with a 95% confidence level (P>1.960). Therefore, these variables are highly recommended and the indicators are considered convergently valid. In the soil texture indicator for the latent variable of root conditions as well as the cation exchange capacity (CEC) and base saturation (BS) indicators for nutrient retention, the loading factor was below the tolerance value of 0.50 at the 95% confidence level (P<1.960); hence, it was not used. This implies that the indicators have not been established or explained properly because the standard value of the loading factor must be greater than or equal to 0.50 [37, 38, 66].Table 3
Outer loading (loading factor) and the average variance extracted from study variables.
Indicators (land characteristics)Latent variables (land quality)Loading factorst statStatusAVEX1.1 (temperature)⟶X1 (temperature)1.000∗∗11.192Valid1.000X2.1 (rainfall)⟶X2 (water availability)0.8380.085Valid0.906X2.2 (wet month)⟶0.9890.999ValidX2.3 (dry month)⟶0.8500.428ValidX2.4 (LGP)⟶0.993∗1.431ValidX3.1 (drainage)⟶X3 (oxygen availability)1.0000.000Valid1.000X4.1 (texture)⟶X4 (rooting condition)0.0130.066Invalid0.573X4.2 (coarse material)⟶0.9211.086ValidX4.3 (effective depth)⟶−0.8991.047ValidX5.1 (pH, H2O)⟶X5 (nutrient retention)0.6470.857Valid0.360 (invalid)X5.2 (pH, KCl)⟶0.570∗∗1.973ValidX5.3 (organic C)⟶0.831∗∗3.135ValidX5.4 (CEC)⟶0.436∗1.381InvalidX5.5 (base saturation)⟶0.3650.845InvalidX6.1 (totalN)⟶X6 (nutrient availability)0.760∗∗3.226Valid0.585X6.2 (P availability)⟶0.587∗1.385ValidX6.3 (K exchangeable)⟶0.897∗∗6.907ValidX7.1 (ESP)⟶X7 (sodicity)1.0000.000Valid1.000X8.1 (slopes)⟶X8 (erosion hazard)0.954∗∗21.438Valid0.932X8.2 (soil erosion)⟶0.941∗∗18.308ValidX9.1 (inundation height)⟶X9 (flooding hazard)0.984∗∗4.213Valid0.984X9.2 (inundation period)⟶0.985∗∗3.918ValidX10.1 (rock outcrops)⟶X10 (land preparation)0.998∗∗189.133Valid0.995X10.2 (surface rock)⟶0.998∗∗320.273ValidAVE, average variance extracted; LGP, long growth period; CEC, cation exchange capacity; ESP, exchangeable sodium percentage.The average variance extracted (AVE) value of almost all variables was greater than 0.50; therefore, it was considered convergently valid [61, 62]. The AVE value of the available nutrient variable was not valid due to the smaller value of the loading factor for the CEC and BS indicators of 0.50, leading to the removal of both indicators. A similar result was discovered in the root condition variable, although the AVE value was greater than 0.50, while the soil texture indicator was not used because the loading factor value is only 0.013.The measurement of the Fornell–Larcker criterion and cross-loading was used as the basis for assessing the discriminant validity of the model. The calculation results on the Fornell–Larcker criterion in Table4 show that the average of the tested variables has a higher square root of AVE than the correlation value; hence, the latent variable was considered discriminantly valid. The square root value of the AVE must be greater than its correlation value with other constructs to meet the discriminant validity requirements [69, 73]. The average loading factor value for the latent variable indicator was above that of others as shown in Table 5.Table 4
Fornell–Larker criterion test.
X1X2X3X4X5X6X7X8X9X10YX11.000X20.9400.952X30.0590.1491.000X40.0820.030−0.1620.757X5−0.360−0.239−0.103−0.3680.600X6−0.0690.0210.012−0.5180.6940.765X70.3820.4290.0840.228−0.030−0.2171.000X80.019−0.082−0.5010.285−0.317−0.370−0.0090.966X9−0.104−0.0330.237−0.2040.0730.0900.202−0.2500.992X100.1980.093−0.2230.873−0.303−0.5380.3620.304−0.1260.998Y0.0180.1520.169−0.5780.3870.456−0.016−0.5170.164−0.5681.000X1, temperature; X2, water availability; X3, oxygen availability; X4, rooting condition; X5, nutrient retention; X6, nutrient availability; X7, sodicity; X8, erosion hazard; X9, flooding hazard; X10, land preparation;Y, maize hybrid yield.Table 5
Cross-loading of latent variables to indicators.
X1, temperature; X2, water availability; X3, oxygen availability; X4, rooting condition; X5, nutrient retention; X6, nutrient availability; X7, sodicity; X8, erosion hazard; X9, flooding hazard; X10, land preparation; Y, hybrid maize yield; X1.1, temperature; X2.1, rainfall; X2.2, the wet month; X2.3, the dry month; X2.3, long growth period; X3.1, drainage; X4.1, texture; X4.2, coarse material; X4.3, effective depth; X5.1, organic C; X5.2, cation exchanges capacity; X5.3, base saturation; X6.1, totalN; X6.2, P availability; X6.3, K exchangeable; X7.1, the exchange sodium percentage; X8.1, slopes; X8.2, soil erosion; X9.1, inundation height; X9.2, inundation period; X10.1, rock outcrops; X10.2, surface rock; Y.1, hybrid maize yield. The yellow color shows the loading factor value for the indicators of the latent variables above the loading factor values for other latent variable indicators (>0.5), while the red color indicates the opposite (<0.5).


## 3.1.2. Reliability Test Result

The variables are considered reliable because composite reliability and Cronbach’s alpha coefficient on average were more than 0.7 [61, 74], as shown in Table 6. However, certain indicators still had values less than 0.6, namely, soil texture, but the indicators used are reliable and adequate in forming the latent variables.Table 6
Composite reliability and Cronbach’s alpha test.
Indicators (land characteristics)Cronbach’s alphaComposite reliabilityX1.1 (temperature)1.0001.000X2.1 (rainfall)0.9750.965X2.2 (wet month)X2.3 (dry month)X2.4 (long growth periods)X3.1 (drainage)1.0001.000X4.1 (texture)0.002nor−1.055norX4.2 (coarse material)X4.3 (effective depth)X5.1 (pH, H2O)0.7180.628X5.2 (pH, KCl)X5.3 (organic C)X5.4 (cation exchange capacity)X5.5 (base saturation)X6.1 (totalN)0.8050.681X6.2 (P availability)X6.3 (K exchangeable)X7.1 (exchangeable sodium percentage)1.0001.000X8.1 (slopes)0.9650.928X8.2 (soil erosion)X9.1 (inundation height)0.9920.984X9.2 (inundation period)X10.1 (rock outcrops)0.9980.995X10.2 (surface rock)nor, not reliable.The highest composite reliability and Cronbach’s alpha coefficients were obtained for the variables of temperature, oxygen availability, and toxicity of 1 for the variables to be very reliable. The lowest coefficient was obtained on the root condition variable; therefore, the variable was not reliable. According to Bagozzi and Yi [75] and Hair et al. [74], variables are considered good and accepted when the value is >0.70.The remaining variables are water availability, nutrient retention, available nutrients, erosion hazard, and land preparation variables. The coefficient of composite reliability and Cronbach’s alpha >0.6; therefore, the variable is considered reliable. The minimum value of composite reliability and Cronbach’s alpha coefficients was 0.60 [61, 74, 75].


## 3.1.3. Structural Model Test (Inner Models)

Land characteristics that have a significant correlation with hybrid maize yields show a high level of contribution to land quality in influencing hybrid maize yields as indicated in Figure3. The figure shows a structural model of the relationship between indicator variables, namely, 24 land characteristics, rectangular yellow, and latent variables, including 10 land qualities maize yield and oval blue. It also shows a model for the relationship between latent variables such as land qualities and maize yield, as well as loading figures. The factor for each indicator and path coefficient for land qualities has a direct effect on the value of maize yields.Figure 3
Path coefficient of land quality on hybrid maize yield.For example, X8 (erosion hazard) significantly determines the hybrid maize yield with a path coefficient of −0.392. The negative sign indicates that the erosion hazard is negatively related to maize yield, where the higher the erosion hazard, the lower the maize yield. Furthermore, nutrient retention (X5) contributes to the diversity of hybrid maize yields with a path coefficient of 0.252. A positive sign indicates that nutrient retention is positively related to maize yield, where the higher the value of nutrient retention, followed by the maize yield.The results of this path analysis indicated that the land quality that can be a predictor of maize yield diversity were oxygen availability (X3), nutrient retention (X5), nutrient availability (X6), erosion hazard (X8), and land preparation (X 10). Figure2 indicates that only 8 of the 24 indicators explain latent variance at the 5% real test level. The 8 indicators were coarse material and effective soil depth as an indicator of rooting condition, organic carbon content as nutrient retention, exchangeable K as nutrient availability, soil erosion and slope as erosion hazard, as well as rock outcrop and surface rock as an indicator of land preparation. It was also indicated that the drainage loading factor was unable to explain the diversity of oxygen availability. Therefore, oxygen availability cannot be used as a land quality because there are no indicators that can represent it. Only the land qualities of nutrient retention (X5), nutrient availability (x6), erosion hazard (x8), and land preparation (X10) were used next.The indicators of land characteristics for effective depth, organic C, totalN, and exchangeable K have a fairly strong positive relationship and a very significant effect on hybrid maize yields. In this relationship, an increase in these parameters by 1% will be followed by a rise in hybrid maize yields of 39% to 57.7%. According to Wirosoedarmo et al. [4], effective depth affects root growth and development, making plants grow and develop properly. Moreover, the levels of organic C, total N, and CEC are influenced by soil organic matter [76], while potassium plays a role in the growth and development of maize [77].Indicators of rough soil characteristics, slopes, soil erosion, as well as surface and rock outcrops, have a strong negative relationship with a very significant effect on hybrid maize yields. In this relationship, 1% decrease in coarse material, slope, soil erosion, as well as surface, and rock outcrop is followed by an increase in hybrid maize yields by 39–57.7%.


## 3.2. Optimum Hybrid Maize Yield by the Land Quality and Land Characteristics

Table7 shows the mathematical equations for each land characteristic and also the optimum hybrid maize yield for the land equation. Figure 4 shows the diagram of the relationship between land characteristics as an independent variable (X) and maize yield as an independent variable Y. Model fitting indicates that the quadratic equation is sufficient to describe the condition of data distribution.Table 7
The optimum hybrid maize yield by the land quality and land characteristics.
Land quality/land characteristicsOptimum yield (ton/ha)Yield equationR2Rooting condition (rc)Coarse material8.17Y = 0.0025900x2 − 0.2568578x + 11.90935760.96Effective depth8.46Y = −0.0008354x2 + 0.29100569x − 1.39574960.97Nutrient retention (nr)Organic carbon8.46Y = −25.492979x2 + 47.9575089X − 8.98950670.97Nutrient availability (na)TotalN8.54Y = −305.5574654X2 + 155.8690907X − 2.74396401.00K exchangeable5.58Y = −10.6697409X2 + 18.5239943X + 2.31792890.95Erosion hazard (eh)Slopes8.54Y = 0.0183X2 − 0.9559X + 14.8060.92Soil erosion8.17y = 0.0000184X2 − 0.0198647X + 9.05375690.89Land preparation (lp)Rock outcrops7.41Y = 0.0057496X2 − 0.3845867X + 8.62697850.92Surface rock7.41Y = 0.0057496X2 − 0.3945867X + 8.62697850.92Figure 4
Scatter diagram relationship among maize yield and land characteristics.The optimum of hybrid maize yield ranged from 5.58 to 8.54 ton/ha, where the highest yield was obtained from totalN and slopes of 8.54 ton/ha with an R2 value of 100% and 92%. Sutardjo et al. [78] showed that hybrid maize yields ranged from 7.43 to 9.2 ton/ha. This indicated that the optimum yield achieved is still within the range of hybrid maize yields that have been previously reported. Nitrogen is directly involved in the formation of amino acids, proteins, nucleic acids, enzymes, nucleoproteins, and alkaloids, which are needed in the plant growth process [79, 80]. An extremely high amount of N causes excessive vegetative growth, thereby making plants fall easily and increasing susceptibility to disease [81]. Meanwhile, the lack of N nutrients can limit cell division, enlargement [82], and the formation of chlorophyll, leading to stunted growth as well as yellowing of leaves [81].The lowest optimum yield was obtained from exchangeableK, which was only 5.58 ton/ha with an R2 value of 95%. This was presumably because the K content in the soil is very low, thereby affecting the hybrid maize yield. Potassium (K) is required by plants for physiological functions, including carbohydrate metabolism, enzyme activity, osmotic regulation, efficient water use, N uptake, protein synthesis, and assimilate translocation [83–86]. It also plays a role in improving the quality of crop yields [87, 83, 88].The optimum hybrid maize yield of the remaining is relatively diverse. This includes effective depth and organic carbon, which were both 8.46 ton/ha with anR2 value of 97%. Furthermore, coarse material and soil erosion were 8.17 ton/ha with an R2 value of 96% and 89%, while rock outcrops and surface rock were 7.41 ton/ha with an R2 value of 92%. The absence of coarse material >2 mm in diameter indicated that plant roots can grow freely on the surface or deeper parts of the soil [89] because the deeper the roots of the maize, the greater the maize yield [90, 91]. The addition of organic matter will increase maize yield [92–94] and organic C content [95] because soil organic matter is a strong positive predictor of yield [96]. Kane et al. [96] also stated that the addition of more organic matter will improve water retention, thereby reducing maize yield losses due to drought. The slope has a significant effect on soil degradation [97]. According to a previous study, erosion and maize yield are negatively correlated; hence, increased erosion will reduce maize productivity [98]. Soil erosion on flat land is slower surface runoff [99]. It was also reported that surface rocks and outcrops are the limiting factors in the suitability of maize plantations [100]. Therefore, a high percentage of rock outcrops will complicate land cultivation and plant root growth.


## 3.3. Land Suitability Criteria for Hybrid Maize Crops

Table8 shows the yield limit for each class from the calculation of the optimum yield, where the class range for each land characteristic is derived. Based on the optimum yield of the highest hybrid maize, there were 2 indicators, namely, the total N content and the slope. The total N indicator with a very suitable class (S1) was achieved when the value in the soil was greater than 0.11%, while in the moderately suitable class (S2), it was achieved when the total N in the soil ranges from 0.08 to 0.10%. In the marginally appropriate class (S3), the total N indicator was achieved when the total N in the soil ranges from 0.06 to 0.07%, while the not suitable class (N) was achieved when the content was less than 0.06%. On the slope indicator with class S1, it was obtained when the slope class ranges from 0 to 7.70%%, while class S2 was achieved when the slope class ranges from 7.71 to 11.84%. Furthermore, in classes S3 and N, it was obtained when the slope class ranged from 11.85 to 18.25% and greater than 18.25%, respectively.Table 8
Yield limits of hybrid maize and values obtained in each land suitability class interval by land quality and characteristics.
Land quality/land characteristicsYield limits (ton/ha)Value of land suitability criterion obtainedS1 − S2 (80% ×Yoptim)S2 − S3 (60% ×Yoptim)S3 −N(40% × Yoptim)S1S2S3NRooting condition (rc)Coarse material (%)8.176.054.040–13.5113.51–27.4827.48–52.41>52.41Effective depth (cm)8.466.374.29≥69.6649.36–69.6533.29–49.35<33.29Nutrient retention (nr)Organic carbon (%)8.466.374.29≥0.610.52–0.600.34–0.51<0.34Nutrient availability (na)TotalN (%)8.546.434.33≥0.110.08–0.100.06–0.07<0.06K exchangeable (cmol(+)/kg)5.584.422.98≥0.250.14–0.240.05–0.13<0.05Erosion hazard (eh)Slopes (%)8.546.434.330–7.707.71–11.8411.85–18.25>18.25Soil erosion (ton/ha/year)8.176.054.04≤55.3255.32–195.29195.30–605.57>605.57Land preparation (lp)Rock outcrops (%)7.415.693.970–4.464.47–13.1013.11–31.89>31.89Surface rock (%)7.415.693.970–4.464.47–13.1013.11–31.89>31.89S1, very suitable; S2, moderately suitable; S3, marginally suitable;N, not suitable.Based on the optimum yield of the lowest hybrid maize, only the exchangeableK was the indicator. In classes S1 and S2, it was achieved when the exchangeable K content in the soil was greater than 0.25 cmol(+)/kg and ranges from 0.14 to 0.24 cmol(+)/kg, respectively. Furthermore, class S3 was achieved when the exchangeable K content in the soil ranges from 0.05 to 0.13 cmol(+)/kg, while in class N, it was obtained when the exchangeable K content in the soil was less than 0.05 cmol(+)/kg. The remaining variables and indicators were relatively varied according to the optimum yield of hybrid maize and the range of land suitability classes obtained as presented in Table 8.Based on the relationship between the quality and characteristics of the selected land with optimum results, the criteria for hybrid maize land suitability were obtained as shown in Table8. These criteria described the actual state of achieving optimum, moderate, and minimum yields of hybrid maize in the field with values of 80%, 60%, and 40%, respectively. According to Sukarman et al. [101], the parameters used in the land suitability assessment must describe the actual conditions. This is due to the significant positive correlation between maize yield and land suitability class [102].The land suitability criteria for the new hybrid maize are fewer and have referred to the optimum yield. Meanwhile, the land quality and characteristics in this new criterion are less because it only consists of root conditions with characteristics of coarse material and effective depth, nutrient retention with organic C, and nutrient availability with totalN and K exchangeable. It also consists of erosion hazards with characteristics of slopes and soil erosion, as well as land preparation with surface rocks and rock outcrops only. The land qualities selected and maize yields consistent with the land potential are the basis for developing suitability criteria. This will reduce the land characteristics and make the evaluation process faster, cheaper, and easier with accurate results [26]. Some characteristics and land quality criteria were not made because they did not significantly affect the yield of hybrid maize. The number and distribution of the data were still limited, and the diversity of values was small or not measurable in the field [72].Compared to Wahyunto et al. [47], the new land suitability criteria for hybrid maize plants in Table 9 are more realistic in value with the conditions in the field and is based on the achievement of optimum yields. The current land suitability criteria are still general and not specific to maize yields [26], although the agronomic and yield potential of each maize variety differ, based on the diversity of characteristics and land quality in the field. There are still limitations on the use of these results for the development of hybrid maize in the Boalemo Regency because the setting is only based on land characteristics and optimum yields in this regency. Therefore, further investigation to expand the scope of the research area nationally with more diverse and contrasting land characteristic values is recommended to determine the effect on hybrid maize production.Table 9
Comparison of new and general land suitability criteria with land quality and characteristics.
Land quality/land characteristicsNew land suitability criterion of hybrid maizeLand suitability criterion of general maize [47]S1S2S3NS1S2S3NRooting condition (rc)Coarse material (%)0–13.5113.51–27.4827.48–52.41>52.41<1515–3535–55>55Effective depth (cm)≥69.6649.36–69.6533.29–49.35<33.29>6060–4040–25<25Nutrient retention (nr)Organic carbon (%)≥0.610.52–0.600.34–0.51<0.34>1.200.8–1.2<0.8—Nutrient availability (na)TotalN (%)≥0.110.08–0.100.06–0.07<0.06MoLoVLo—K exchangeable (cmol(+)/kg)≥0.250.14–0.240.05–0.13<0.05Mo-HiLoVLo—Erosion hazard (eh)Slopes (%)0–7.707.71–11.8411.85–18.25>18.25<88–1515–25>25Soil erosion (ton/ha/year)≤55.3255.32–195.29195.30–605.57>605.57—VLiLi-MoHe-VHeLand preparation (lp)Rock outcrops (%)0–4.464.47–13.1013.11–31.89>31.89<55–1515–40>40Surface rock (%)0–4.464.47–13.1013.11–31.89>31.89<55–1515–40>40S1, very suitable; S2, moderately suitable; S3, marginally suitable;N, not suitable; Hi, high; M, moderate; Lo, low; VLo, very low; He, heavy; Li, light; VLi, very light; VHe, very heavy.


## 4. Conclusions

Land suitability criteria for the new hybrid maize are determined by land qualities, namely, root conditions, nutrient retention, available nutrients, erosion hazard, as well as land preparation, and land characteristics, including coarse material, effective depth, organic C, totalN, exchangeable K, slopes, soil erosion, rock outcrops, and surface rocks. The highest optimum yield of 8.54 ton/ha was achieved by the total N and slopes for a very suitable class (S1), while the lowest value of 5.58 ton/ha was attained by exchangeable K for class S1. These results showed that the combination of the PLS-SEM and boundary line analysis can be an alternative approach to establishing new land suitability criteria for crops based on optimum yields and selected land quality.



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*Source: 3800877-2023-01-18.xml*