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Technical efficiency of sugarcane farming in East Java, Indonesia: A bootstrap data envelopment analysis

  • Agus Santosa EMAIL logo , Endang Siti Rahayu , Joko Sutrisno and Kusnandar Kusnandar
Published/Copyright: November 6, 2024
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Open Agriculture
From the journal Open Agriculture Volume 9 Issue 1

Abstract

Sugarcane is an essential commodity in Indonesia. However, climate change negatively affects the sugarcane production efficiency. This study aims to measure the technical efficiency of sugarcane farming using a bootstrap data envelopment analysis (DEA) approach, compare the efficiency of irrigated and rain-fed sugarcane farming, and determine the factors affecting the technical efficiency of sugarcane farming in East Java, Indonesia. Primary data were collected from 451 sugarcane farmers during the 2020–2021 planting season. The results indicate that the “bias-corrected” technical efficiency scores of the single- and double-bootstrap approach (0.624 and 0.561) were lower than that of conventional DEA (0.714). The efficiency score of irrigated sugarcane farms (0.593) was higher than that of rain-fed farms (0.529). Moreover, the farmers’ age, household size, dependency, farming experience, training, subsidies, crop diversification, and access to irrigation impacted sugarcane farming’s technical efficiency. Improvement of 1% in training, irrigation access, and subsidies increased the technical efficiency by 0.034, 0.032, and 0.030, respectively. This strategy is expected to enhance the productivity and technical efficiency and reduce the poverty in rural households in East Java.

Keywords: sugarcane production; technical efficiency; DEA; bootstrap; farmers

Abbreviations

DEA

data envelopment analysis

PSFC

People’s Sugarcane Farmer Cooperative

1 Introduction

Sugarcane (Saccharum officinarum L.) is among Indonesia’s strategic commodities [1]. Sugar and sugarcane production are essential to Indonesia’s rural economy because they increase the agricultural income and provide employment opportunities [2]. East Java is the nation’s principal sugarcane production center [3]. Nevertheless, domestic sugarcane production has fluctuated over time, trending downward over the last decade [4]. Previous studies have reported significant challenges faced by sugarcane development in Indonesia, including low productivity and quality, agricultural land and factory inefficiencies, and climate variability [2,5]. Furthermore, climate change in Indonesia, as indicated by low rainfall and high temperatures, causes sugarcane production to decline [4,6].

Irrigation is an effective means to overcome the adverse effects of climate change on sugarcane production [6,7]. Additionally, irrigation increases productivity because sugarcane is a highly water-intensive crop; thus, irrigation is critical in meeting water requirements during periods of water deficit [8,9]. However, sugarcane is primarily cultivated under rain-fed conditions and contributes to more than 60% of the sugar production [1]. Rain-fed sugarcane farming – vulnerable to climate change – has the lowest productivity compared to furrow- and drip-irrigated sugarcane farming [10].

Low productivity indicates inefficient sugarcane farming. Inefficiency in the agricultural context can be defined as the extent to which farmers use more inputs to produce a specific output level than the resources used by farmers using best practices [11]. Production efficiency analysis is a suitable method because it helps farmers mitigate the depletion of available natural resources, understand the efficient use of inputs, improve farm productivity, reduce negative environmental impacts, and achieve sustainable agriculture [12,13].

Measuring the inefficiency of sugarcane farming is essential owing to its impact on productivity and sustainability [14]. The inefficiency analysis conducted in this study uses bootstrapped data envelopment analysis (DEA). Notably, DEA is widely used to assess the operational and environmental performances of numerous entities in the public sector. However, conventional DEA has limitations, and therefore a bootstrap approach is needed to address this problem [15,16,17].

Critical research gaps were identified through an extensive review of the existing literature on sugarcane farming efficiency. First, thus far, no significant difference has been found between the technical efficiency of sugarcane farming in drylands and wetlands [18,19]. Second, the association between irrigation and sugarcane farming’s technical efficiency has not been explored [20,21,22]. Furthermore, research on sugarcane farming’s efficiency, especially in East Java, remains limited. Therefore, this study aims to fill these gaps by measuring sugarcane farming’s technical efficiency using a double-bootstrap DEA approach, comparing the technical efficiency of sugarcane farming between irrigated and rain-fed lands and determining the factors influencing sugarcane farming’s technical efficiency in East Java, Indonesia.

2 Materials and methods

2.1 Study sites

This study was conducted in East Java Province – Indonesia’s sugarcane production center – where the sugarcane plantation sites managed by farmers are equivalent to 38% of Indonesia’s total sugarcane land and whose sugar production is equivalent to 41% of Indonesia’s total sugar production [23]. The primary data, collected through interviews, were derived from a sample of sugarcane farming households in three districts (Malang, Kediri, and Ngawi) in East Java from May to August 2022.

The study sample was selected using a stratified sampling method for each survey area (Figure 1). Specifically, two subdistricts were selected from each site based on the sugarcane land area and farmer populations. Four villages and 25 people from each village were selected from the subdistricts using a random sampling method. The survey was administered in 600 households, but only 490 (82%) were willing to be surveyed. As the DEA model is prone to data outliers, we excluded samples exposed to outliers. Therefore, our analysis focused on 451 households.

Figure 1 Study site. Source: Indonesian Geospatial Map Geographic Information System and Remote Sensing processed with QGIS software (downloaded from https://www.indonesia-geospasial.com/).
Figure 1

Study site. Source: Indonesian Geospatial Map Geographic Information System and Remote Sensing processed with QGIS software (downloaded from https://www.indonesia-geospasial.com/).

  1. Consent: Respondents have given consent to participate voluntarily and understand the purpose of the study.

2.2 Data analysis

2.2.1 DEA

This study examines sugarcane farms’ technical efficiency using DEA. The problem is to evaluate the efficiency of sugarcane farming that produces y ij from y i products using various inputs in amount x kj . As the output ( y ) and input ( x ) are heterogeneous, the definitions of weights u r and v i are used to aggregate them. Here, y rj is the amount of output r produced by the farm j (e.g., tons of sugarcane per hectare), while x ij is the amount of input i used by the farmer j (e.g., the amount of fertilizer or labor); hence, the technical efficiency index ( θ j ) can be written as follows:

(1) θ j = ∑ r u r y rj ∑ i v i x ij .

The weights u r and v i must be defined based on all the observations of sugarcane farming ( L ). These weights are defined using the overall observations of sugarcane agar farming; θ j can be maximized but is still less than one. Therefore, the values of θ j , u r , and v i are found with the constraints θ l ≤ 1 , l = 1 , … , L . In this manner, sugarcane farming j is “enveloped” by all other farmers in the sample. Weights in DEA are necessary to combine various inputs (such as labor, fertilizer) and outputs (such as sugarcane yield per hectare) into a consistent measure of efficiency. These weights help in assessing how efficiently a decision-making unit (DMU) uses resources to produce outputs [24].

The input-oriented DEA model was selected based on orientation because farmers discovered that it was more straightforward to control input than output variables [25]. Input-oriented DEA focuses on reducing the inputs used by the DMU without lowering the level of output produced. We used the Variable Returns to Scale (VRS) assumption because we hypothesized robust disposable output and input [26].

The VRS model contains additional variables to account for scale variations ( u 0 ); thus, the efficiency analysis becomes more detailed [27]. Equation (1) changes as follows:

(2) θ j = ∑ r u r y rj − u 0 ∑ i v i x ij ,

where θ j is the technical efficiency value (TE), which ranges from 0 to 1, y r is the vector for the output amount of sugarcane, x i is the vector for the number of inputs; Y is the output of the production quantity; X is the production input; and λ is the weighting vector. The VRS constraint is written with ∑ i = 1 n λ i = 1 . Thus, the equation for the DEA input orientation and VRS assumption for estimating sugarcane farming’s technical efficiency is written as follows:

min θ j

θ j , λ

subject to

− y r + Y λ ≥ 0

θ j x i − X λ ≥ 0

∑ i = 1 n λ i = 1

λ ≥ 0 .

The equation compares the technical efficiency of sugarcane farming i with that of all other farms in the sample. In this context, X represents the matrix of all input quantities used by the farms, while Y represents the matrix of all output quantities produced by the farms. The additional variable u 0 accounts for the variations in scale under the VRS assumption, allowing for a more detailed efficiency analysis.

2.2.2 Bootstrapping DEA

The subsequent stage involves elucidating the sources of sugarcane farming efficiency, whereby socioeconomic and farm characteristics are associated with technical efficiency. Numerous previous studies have employed the Tobit method to examine the relationships between explanatory variables and efficiency scores [25,28,29]. The Tobit model is a regression model designed to estimate relationships involving censored dependent variables, wherein the values of the dependent variable are observed only within a specific range (e.g., zero and one in the context of DEA efficiency scores) [26]. This model is appropriate when efficiency scores are truncated between zero and one, as highlighted by Greene (2000). However, the Tobit method was criticized by Simar and Wilson [17] because it needed to describe the consistency of the data-generating processes and problems related to invalid inferences owing to serial correlations. They proposed an alternative double-bootstrap procedure that allows valid inferences while simultaneously generating standard errors and confidence intervals for efficiency estimates. Long [30] argued that bias correction in the double-bootstrap DEA approach would help create effective policies for improving agricultural efficiency.

Therefore, we employed a double-bootstrap truncated regression, wherein the bias-corrected efficiency scores were regressed on a set of explanatory variables as follows [17]:

(3) δ = z i β + ε i ,

where δ represents bias-corrected technical efficiency scores, z i represents explanatory variables (socioeconomic characteristics), β represents a vector of parameters, and ε i represents the error term.

The initial efficiency can be downward biased in the DEA owing to sampling variations and a finite sample size. This bias occurs because the sample may not include actual production limits, leading to an underestimation of the potential output. The double-bootstrap procedure following Simar and Wilson (2000) was used to correct for bias. This method involves the following steps: First, the initial efficiency ( θ ˆ i ) was calculated using DEA. Second, multiple bootstrap samples were generated by resampling with replacements from the original data. Third, the efficiency score ( θ ˆ i * ) for each bootstrap sample was computed. Fourth, bias was estimated by comparing the average bootstrap efficiency and initial efficiency scores. Finally, the initial efficiency scores were adjusted by subtracting the estimated bias, precipitating bias-corrected efficiency scores ( δ i ).

The explanatory variables used in this model to determine sugarcane farming technical efficiency were farmers’ age [31,32,33], education [25,31,32,33,34], gender [31,32,35], household size [28,33,34], dependency [36,37], farming experience [25,34], training [34,35], farmer-based organization (FBO) participation [25,33], access to credit [14,34,35], subsidies [28,32,38], distance to the sugar mill [14,39], ratoon [40,41], crop diversification [42,43], and access to irrigation [20,44]. These variables were selected because they – according to previous studies – influence farm productivity and efficiency.

Plant diversification was measured using the Herfindahl–Hirschman index (HHI). This index is suitable for measuring crop diversification because it is the ratio of the sum of squares of the area of each type of plant to the total area of plants [45,46]. The HHI is expressed as follows:

(4) HHI = ∑ J N A j 2 ,

where Aj represents the acreage proportion of the jth crop to the total cropped crop. When the diversification level increases, the HHI decreases. An HHI equal to one indicates specialization, and an HHI equal to zero implies diversification.

3 Results and discussion

3.1 Characteristics of sugarcane farming in East Java

Table 1 presents the socioeconomic characteristics and production function variables of the sample of sugarcane farmers disaggregated by access to irrigation and pooled samples. Previously, differences in input, output, and socioeconomic characteristics were examined using t-tests. The average sugarcane production was 174,237 kg per 1.9 ha per year (equivalent to 90.032 kg per ha per year), with rain-fed farms producing more than irrigated farms. Sugarcane farmers controlled 19290.88 m2 of land or the equivalent of 1.9 ha, and rain-fed farmers (23985.15 m2) had significantly more land than irrigated farmers (14533.74 m2).

Table 1

Summary statistics of model variables

Variable Mean
Irrigated farms (N = 224) Rain-fed farms (N = 227) Pooled sample (N = 451)
Production output
Sugarcane (kg) 164656.2 183691.2 174,237
Production input
Land (m2) 14533.74 23985.15 19290.88
Seed (kg) 16152.21 23067.48 19632.85
ZA fertilizer (kg) 1290.434 1848.975 1571.562
PO fertilizer (kg) 785.782 1142.891 965.524
Herbicide (L) 7.125 8.024 7.577
Fuel (L) 144.843 124.494 134.601
Labor (person-days) 401.644 566.748 484.745
Socioeconomic and farm characteristics
Age (years) 48.759 52.211 50.497
Education (years) 9.826 8.678 9.248
Gender (1 = man, 0 = woman) 0.951 0.912 0.931
Household size (number of family members) 3.862 3.502 3.681
Dependency (ratio between dependent and working-age members) 1.846 1.511 1.677
Farming experience (years) 21.103 18.819 19.953
Training (participation in last few years) 2.812 2.172 2.490
FBO participation (years) 13.871 13.026 13.446
Access to credit (1 = yes, 0 = no) 0.326 0.414 0.370
Subsidies (1 = yes, 0 = no) 0.670 0.480 0.574
Distance from the farm to the sugar mill (meters) 2220.629 4112.115 3172.663
Ratoon (1 = plant cane; 2–8 = ratoon cane) 3.339 3.256 3.297
Crop diversification (HHI) 0.834 0.802 0.818
Access to irrigation (1 = yes; 0 = no) — — 0.497

Rain-fed farms (23067.48 kg) utilized more sugarcane seeds than irrigated farms (16152.21 kg), with an overall average of 19632.85 kg. Farmers used an average of 1571.56 kg of ZA fertilizer; in this regard, rain-fed farms (1848.975 kg) used substantially more amount of ZA fertilizer than irrigated farms (1290.434 kg). Rain-fed farms used 1,142,891 kg of PO fertilizer, which was significantly higher than that used by irrigated farms (785.782 kg).

The average herbicide usage was 7.577 L, with rain-fed farms (8.024 L) using more herbicides than irrigated farms (7.125 L). Rain-fed and irrigated farms used 124.494 and 144.843 L of fuel, respectively. Finally, farmers employed 484.745 person-days of labor on average, with rain-fed farms employing (566.748 person-days) more labor than irrigated farms (401.644 person-days).

In this study, the farmers’ access to irrigation is noteworthy. Farmers obtained access to water through preexisting traditional irrigation channels (Figure 2a) and irrigation channels created by the government (Figure 2b). The water sources included springs in the Malang area, reservoirs, and groundwater installations through the Groundwater Development Project (GDP). The government built the GDP, but the operational funds were collectively charged to each farmer. However, agricultural land that does not pass through irrigation canals only receives water during the rainy season.

Figure 2 (a) Traditional irrigation canal used by farmers in sugarcane fields. (b) Irrigation canal constructed by the government to support water access in sugarcane fields. Source: Researcher documentation.
Figure 2

(a) Traditional irrigation canal used by farmers in sugarcane fields. (b) Irrigation canal constructed by the government to support water access in sugarcane fields. Source: Researcher documentation.

Regarding socioeconomic characteristics, 49.7% of the farmers had access to public irrigation. Generally, sugarcane farmers who practiced irrigated farming were younger and had significantly higher education, household size, dependency ratio, training frequency, farming experience, experience in FBOs, and access to input subsidies than those who practiced rain-fed farming. Nevertheless, the former have a significantly shorter distance from the farm to sugar mills and greater access to credit than the latter.

3.2 Estimation of sugarcane farming’s technical efficiency

Table 2 presents the efficiency scores for irrigated and rain-fed sugarcane farming based on the conventional, single, and double-bootstrap approaches. Previously, difference tests were used to compare the estimates. The average technical efficiency scores for irrigated sugarcane farms without changing the output level were 0.749, 0.651, and 0.593, respectively, indicating that on average irrigated sugarcane-farming households can reduce their input by 25.1, 34.9, and 40.7%, respectively.

Table 2

Efficiency scores according to irrigated and rain-fed sugarcane farms

Description Mean technical efficiency
Irrigated farms (N = 224) Rain-fed farms (N = 227) Pooled sample (N = 451)
Conventional technical efficiency (TE) 0.749 0.680 0.714
TE single-bootstrap 0.651 0.597 0.624
TE double-bootstrap 0.593 0.529 0.561

Rain-fed sugarcane farming produced values of 0.680, 0.597, and 0.529 for the three approaches, respectively, indicating that on average rain-fed sugarcane farming households can reduce their input by 32.0, 40.3, and 47.1%, respectively. As irrigated sugarcane farms’ efficiency score was closer to 1 and higher than that of rain-fed farms, irrigated sugarcane farmers can be considered more efficient in using inputs than their competitors.

The efficiency scores based on the conventional DEA were significantly higher than those based on the single- and double-bootstrap approaches. The bias-corrected principle applied in the single- and double-bootstrap approaches allowed the three samples of sugarcane farmers to save more input. Long et al.’s [47] and Olson and Vu’s [48] studies support this finding. The efficiency scores based on the single-bootstrap DEA approach were significantly higher than those based on the double-bootstrap approach. A low efficiency score was caused by the double-bootstrap method, which uses input, output, and information on socioeconomic characteristics [47].

3.3 Determinants of sugarcane farming’s technical efficiency

Table 3 summarizes the determinants of technical efficiency. First, the age of the household head affected the technical efficiency of irrigated sugarcane and the pooled samples at 1 and 5% significance levels. This finding indicates that young household heads were more efficient in using inputs than their older counterparts possibly because older farmers refuse to adopt better agricultural technology. Younger farmers are interested in learning new things and want to apply them to farming operations to achieve higher efficiency. This finding is supported by previous studies [49,50].

Table 3

Determinants of technical efficiency score based on double-bootstrap estimation

Variable Irrigated farms Rain-fed farms Pooled sample
Coefficients Std. error Coefficients Std. error Coefficients Std. error
Age −0.0036435*** 0.000896 0.0004947 0.0005546 −0.0009244* 0.0005592
Education −0.0038257 0.0023872 0.007416*** 0.0015206 −0.0014384 0.0014975
Gender −0.0715186** 0.0321458 0.0177466 0.0182059 −0.0292675 0.0188946
Household size 0.0051285 0.0057068 −0.0015798 0.0046383 0.0111321*** 0.0041577
Dependency 0.0151481*** 0.0055119 −0.0021251 0.004297 0.008202** 0.0040176
Farming experience −0.000694 0.0010034 0.0003549 0.0007402 −0.0014686** 0.0006636
Training 0.0325029*** 0.0069423 0.0095301* 0.0055908 0.0343424*** 0.0047159
FBO participation 0.0083347*** 0.0024861 −0.0038207** 0.0015929 0.0016273 0.0015527
Access to credit 0.0283991* 0.0159266 0.0065764 0.0107698 −0.0104383 0.0099461
Subsidies 0.0270634* 0.015428 0.023552** 0.0103112 0.0298195*** 0.0100102
Distance to mills 0.00000345** 0.0000015 −0.000000164 0.00000087 0.000000257 0.00000073
Ratoon 0.0014423 0.0044033 −0.0133689*** 0.0029722 0.0031433 0.0028975
Diversification −0.0849824** 0.0332403 −0.0412734* 0.0226808 −0.0496517** 0.0223597
Access to irrigation – – – – 0.0320004*** 0.0104806
Observation (N) 224 227 451

***, **, and * denote significance at the 1, 5, and 10% levels, respectively.

Farmer education was positively associated with rain-fed farms’ technical efficiency, suggesting that a 1% increase in the length of rain-fed farmers’ education improves the efficiency by 0.007 (assuming other variables as constant). Farming households with high educational backgrounds can adopt agricultural technological innovations and technical expertise, thereby increasing the productivity and technical efficiency. This finding aligns with that of Chandio et al. [51], who stated that a high educational background increases sugarcane productivity.

Gender detrimentally affected irrigated sugarcane farms’ technical efficiency, suggesting that women household heads use inputs more efficiently than men household heads because they are more careful in managing sugarcane farming at each stage of crop cultivation. This finding aligns with that of Midamba et al. [52], who stated that women-headed households positively affect the technical efficiency of agriculture. These women are widows or have husbands working elsewhere; as their households depend on sugarcane farming, they are more prudent than men. Notably, Suwandari et al. [53] found that female workers can increase sugarcane production.

Household size positively affected the technical efficiency of pooled sugarcane farming. Family size is associated with the availability of cheap labor and reduced production costs. In an interview study, most farmers reported a labor shortage, stating that several family members migrated to the village when the government implemented a large-scale social restriction policy to prevent the spread of COVID-19 [54]. The household head invited family members who were migrants and could work (aged 15–65 years) to cultivate sugarcane. They were willing to work, even though the wages offered were low. Thus, production could run on time according to the sugarcane milling schedule at a sugar factory, thereby reducing production costs. This situation explains why household size increases the technical efficiency. This finding is supported by Francis et al. [50].

The dependency ratio is positively related to irrigated and pooled sugarcane farming’s technical efficiency at 1 and 5% significance levels. The logical reason is that the household head coerces family members who are not actively working to help at every stage of sugarcane production to overcome the labor shortage during the COVID-19 pandemic and, thereby, reduce production costs. Similar results were observed in Fitria et al.’s [54] study, wherein a heavier economic burden borne by a household encouraged family workers to work hard and effectively utilize production resources.

This study found that farming experience negatively influenced sugarcane farms’ technical efficiency, indicating that sugarcane farmers with more extended cultivation experience in traditional practices can be an obstacle to improve the yield performance and efficiency. Furthermore, farmers with more experience tend to be older, which, as already mentioned, negatively affects the technical efficiency. This finding is supported by Ogundari [46].

The training variables were positively related to the irrigated and pooled sugarcane farms’ technical efficiency. In East Java, the People’s Sugarcane Farmer Cooperative (PSFC) and Agricultural Extension Center organized training programs for sugarcane farmers. These programs aimed to develop farmers’ analytical, critical, and creative thinking skills and help them make better decisions in agricultural production [55]. Farmers who attended training more frequently understood how to increase their technical efficiency. This result aligns with those of Effendy et al. [35] and Dessale [55].

The main requirement for a farmer to become a PSFC member was 5 years of farming experience. Sugarcane farmers with extended participation in PSFC had more access to capital and knowledge for allocating efficient inputs. FBO participation was positively related to irrigated farms’ technical efficiency at 1% significance level; this finding aligns with that of Olagunju et al. [56]. By contrast, FBO participation among rain-fed farmers negatively influences the technical efficiency. According to Anang et al. [33], this condition can occur if the FBO is ineffective in promoting their members’ production efficiency.

Credit access was positively related to the technical efficiency of irrigated sugarcane farming at a 10% significance level. A 1% increase in access to credit enhanced the efficiency by 0.028, indicating that farmers who can obtain credit quickly can allocate their funds to purchase quality inputs to increase their sugarcane farming’s productivity and technical efficiency. This finding aligns with that of Chandio et al. [51], who argued that access to credit increases sugarcane production and productivity.

Furthermore, input subsidies positively affected sugarcane technical efficiency, indicating that sugarcane farmers can save costs and optimize inputs to increase the quantity and technical efficiency of sugarcane production. A 1% improvement in input subsidies can enhance sugarcane farms’ efficiency by 0.030 at a 1% significance level. This result aligns with that of Minviel and Latruffe [57], who inferred that subsidies might help farmers overcome the financial constraints that hinder the efficient restructuring of their agricultural production, thereby increasing the technical efficiency with farm production capacity through replacement or net investments in advanced technology.

The distance from the farm site to the sugar mill positively affected irrigated farms’ technical efficiency. Even though farther distances created more transportation costs, the farthest land rentals were cheaper; thus, farmers could still reduce production costs and increase the overall efficiency. Moreover, the land conditions were ideal for sugarcane cultivation. This finding aligns with Linnenluecke et al.’s [9] finding that sugarcane production growth increases if the current agricultural location – despite being relatively far from the sugar mill – has suitable climatic conditions.

Farmers in East Java use plant or ratoon canes to produce sugarcane seeds. Based on field observations, most sugarcane farmers in East Java selected ratoon canes for sugarcane seeds. Nevertheless, the number of ratoons was negatively correlated to rain-fed farms’ technical efficiency at a 1% significance level, indicating that young ratoons, including plant canes, increase sugarcane farming’s technical efficiency. A previous study concluded that sugarcane exhibits high productivity at the first to fourth ratoon ages, whereas ratoon-age canes over four years old exhibit low productivity [41].

This study found that crop diversification had a negative relationship with technical efficiency. In Indonesia, crop diversification can reduce the technical efficiency [42,43]. However, farmers continue to adopt a crop diversification system because according to Ogundari [46] this system provides agronomic benefits, such as increasing soil fertility, reducing disease and pest attacks on crops, and lowering soil erosion. Karimov [58] inferred that crop diversification increases the income, and funds can be invested to purchase high-quality inputs to increase crop productivity. Additionally, crop diversification is a measure for securing income, reducing prices, and reducing technical risks. Farmers can easily adjust to market fluctuations and environmental conditions, thus reducing uncertainty and risk in sugarcane production [59,60].

Finally, farmers who gained access to irrigation enhanced technical efficiency at 1% significance level, indicating that irrigation channels on rain-fed land are needed because – as demonstrated by this study – a 1% increase in irrigation access increases sugarcane farms’ technical efficiency by 0.032. This finding is supported by Rahmawati et al. [61]. The government must actively build irrigation canals and facilitate water management to ensure that all land receives evenly distributed water [2].

The sustainability of sugarcane farming can be improved by developing adequate irrigation infrastructure. This study’s results indicate that irrigation sugarcane farming is better than non-irrigation sugarcane farming. Nevertheless, adequate irrigation with adequate water management must be considered to prevent water wastage [62]. Additionally, proper training can help farmers adopt more efficient and sustainable sugarcane farming technologies and practices. Appropriate training can improve farmers’ analytical skills [63]. Ultimately, this would increase sugarcane farming’s productivity and sustainability. Crop diversification is necessary to increase soil fertility, reduce pest and disease attack rates, and reduce soil erosion; however, it also reduces the technical efficiency.

4 Conclusions

The conclusions drawn from this study are as follows: first, the “bias-corrected” efficiency scores in single- and double-bootstraps were lower than those in the conventional DEA model. Conventional DEA models do not typically account for statistical disruptions or sampling variability. By contrast, bootstrapping can adjust biases such that efficiency estimates are more accurate and result in lower but more reliable efficiency scores. The results indicate that the technical efficiency improvement potential suggested by this study’s two bootstrapping methods was more significant than that of conventional DEA. Second, the efficiency score of irrigated sugarcane farms was higher than that of rain-fed farms, indicating that the former were more efficient at allocating inputs than the latter. Moreover, the farmers’ age, household size, dependency, farming experience, training, subsidies, crop diversification, and access to irrigation influenced their sugarcane farming’s technical efficiency. Furthermore, we recommend the government to increase the quality and quantity of training programs, encourage farmers to join the programs, build new irrigation channels, especially on rain-fed land and ensure effective water management to minimize water wastage, and improve subsidies for farming inputs. Overall, it is hoped that interventions in the sugarcane sector improve the technical efficiency and income while reducing rural poverty in East Java.

Acknowledgements

The authors would like to thank the Head of the Agriculture Office of Ngawi, Kediri, and Malang District of East Java Province for their assistance in organizing the survey. In addition, we would like to thank the Head of the Agricultural Extension Center of Karanganyar, Pitu, Ngadiluwih, Kandat, Gondang Legi, and Pagelaran subdistricts for their guidance during the implementation of research in the field. Furthermore, we would like to express our gratitude to the anonymous reviewers for their comments and suggestions on improving this paper.

  1. Funding information: This research and its findings were published without any external funding sources.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results and approved the final version of the manuscript. AS conceptualized and designed the study, performed the literature search, and prepared the manuscript. ESR supervised the research, elaborated the intellectual content, and performed the literature and manuscript review. JS elaborated the intellectual content, performed the literature search, manuscript review, manuscript revision, administration, grammarly check, Turnitin check, and is the guarantor. KK elaborated the intellectual content and edited and reviewed the manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Consent: Respondents have given consent to participate voluntarily and understand the purpose of the study.

  5. Data availability statement: The datasets generated and analyzed during this study are available from the corresponding author upon reasonable request.

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Received: 2024-01-28
Revised: 2024-07-22
Accepted: 2024-10-07
Published Online: 2024-11-06

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  144. Evolution of climate-smart agriculture research: A science mapping exploration and network analysis
  145. Short Communications
  146. Music enrichment improves the behavior and leukocyte profile of dairy cattle
  147. Effect of pruning height and organic fertilization on the morphological and productive characteristics of Moringa oleifera Lam. in the Peruvian dry tropics
  148. Corrigendum
  149. Corrigendum to “Bioinformatics investigation of the effect of volatile and non-volatile compounds of rhizobacteria in inhibiting late embryogenesis abundant protein that induces drought tolerance”
  150. Corrigendum to “Composition and quality of winter annual agrestal and ruderal herbages of two different land-use types”
  151. Special issue: Smart Agriculture System for Sustainable Development: Methods and Practices
  152. Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy
  153. FruitVision: A deep learning based automatic fruit grading system
  154. Energy harvesting and ANFIS modeling of a PVDF/GO-ZNO piezoelectric nanogenerator on a UAV
  155. Effects of stress hormones on digestibility and performance in cattle: A review
  156. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part II
  157. Assessment of omega-3 and omega-6 fatty acid profiles and ratio of omega-6/omega-3 of white eggs produced by laying hens fed diets enriched with omega-3 rich vegetable oil
  158. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part II
  159. Special Issue on FCEM – International Web Conference on Food Choice & Eating Motivation: Message from the editor
  160. Fruit and vegetable consumption: Study involving Portuguese and French consumers
  161. Knowledge about consumption of milk: Study involving consumers from two European Countries – France and Portugal
https://doi.org/10.1515/opag-2022-0378
Keywords for this article
sugarcane production; technical efficiency; DEA; bootstrap; farmers
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Articles in the same Issue

  1. Regular Articles
  2. Supplementation of P-solubilizing purple nonsulfur bacteria, Rhodopseudomonas palustris improved soil fertility, P nutrient, growth, and yield of Cucumis melo L.
  3. Yield gap variation in rice cultivation in Indonesia
  4. Effects of co-inoculation of indole-3-acetic acid- and ammonia-producing bacteria on plant growth and nutrition, soil elements, and the relationships of soil microbiomes with soil physicochemical parameters
  5. Impact of mulching and planting time on spring-wheat (Triticum aestivum) growth: A combined field experiment and empirical modeling approach
  6. Morphological diversity, correlation studies, and multiple-traits selection for yield and yield components of local cowpea varieties
  7. Participatory on-farm evaluation of new orange-fleshed sweetpotato varieties in Southern Ethiopia
  8. Yield performance and stability analysis of three cultivars of Gayo Arabica coffee across six different environments
  9. Biology of Spodoptera frugiperda (Lepidoptera: Noctuidae) on different types of plants feeds: Potency as a pest on various agricultural plants
  10. Antidiabetic activity of methanolic extract of Hibiscus sabdariffa Linn. fruit in alloxan-induced Swiss albino diabetic mice
  11. Bioinformatics investigation of the effect of volatile and non-volatile compounds of rhizobacteria in inhibiting late embryogenesis abundant protein that induces drought tolerance
  12. Nicotinamide as a biostimulant improves soybean growth and yield
  13. Farmer’s willingness to accept the sustainable zoning-based organic farming development plan: A lesson from Sleman District, Indonesia
  14. Uncovering hidden determinants of millennial farmers’ intentions in running conservation agriculture: An application of the Norm Activation Model
  15. Mediating role of leadership and group capital between human capital component and sustainability of horticultural agribusiness institutions in Indonesia
  16. Biochar technology to increase cassava crop productivity: A study of sustainable agriculture on degraded land
  17. Effect of struvite on the growth of green beans on Mars and Moon regolith simulants
  18. UrbanAgriKG: A knowledge graph on urban agriculture and its embeddings
  19. Provision of loans and credit by cocoa buyers under non-price competition: Cocoa beans market in Ghana
  20. Effectiveness of micro-dosing of lime on selected chemical properties of soil in Banja District, North West, Ethiopia
  21. Effect of weather, nitrogen fertilizer, and biostimulators on the root size and yield components of Hordeum vulgare
  22. Effects of selected biostimulants on qualitative and quantitative parameters of nine cultivars of the genus Capsicum spp.
  23. Growth, yield, and secondary metabolite responses of three shallot cultivars at different watering intervals
  24. Design of drainage channel for effective use of land on fully mechanized sugarcane plantations: A case study at Bone Sugarcane Plantation
  25. Technical feasibility and economic benefit of combined shallot seedlings techniques in Indonesia
  26. Control of Meloidogyne javanica in banana by endophytic bacteria
  27. Comparison of important quality components of red-flesh kiwifruit (Actinidia chinensis) in different locations
  28. Efficiency of rice farming in flood-prone areas of East Java, Indonesia
  29. Comparative analysis of alpine agritourism in Trentino, Tyrol, and South Tyrol: Regional variations and prospects
  30. Detection of Fusarium spp. infection in potato (Solanum tuberosum L.) during postharvest storage through visible–near-infrared and shortwave–near-infrared reflectance spectroscopy
  31. Forage yield, seed, and forage qualitative traits evaluation by determining the optimal forage harvesting stage in dual-purpose cultivation in safflower varieties (Carthamus tinctorius L.)
  32. The influence of tourism on the development of urban space: Comparison in Hanoi, Danang, and Ho Chi Minh City
  33. Optimum intra-row spacing and clove size for the economical production of garlic (Allium sativum L.) in Northwestern Highlands of Ethiopia
  34. The role of organic rice farm income on farmer household welfare: Evidence from Yogyakarta, Indonesia
  35. Exploring innovative food in a developing country: Edible insects as a sustainable option
  36. Genotype by environment interaction and performance stability of common bean (Phaseolus vulgaris L.) cultivars grown in Dawuro zone, Southwestern Ethiopia
  37. Factors influencing green, environmentally-friendly consumer behaviour
  38. Factors affecting coffee farmers’ access to financial institutions: The case of Bandung Regency, Indonesia
  39. Morphological and yield trait-based evaluation and selection of chili (Capsicum annuum L.) genotypes suitable for both summer and winter seasons
  40. Sustainability analysis and decision-making strategy for swamp buffalo (Bubalus bubalis carabauesis) conservation in Jambi Province, Indonesia
  41. Understanding factors affecting rice purchasing decisions in Indonesia: Does rice brand matter?
  42. An implementation of an extended theory of planned behavior to investigate consumer behavior on hygiene sanitation-certified livestock food products
  43. Information technology adoption in Indonesia’s small-scale dairy farms
  44. Draft genome of a biological control agent against Bipolaris sorokiniana, the causal phytopathogen of spot blotch in wheat (Triticum turgidum L. subsp. durum): Bacillus inaquosorum TSO22
  45. Assessment of the recurrent mutagenesis efficacy of sesame crosses followed by isolation and evaluation of promising genetic resources for use in future breeding programs
  46. Fostering cocoa industry resilience: A collaborative approach to managing farm gate price fluctuations in West Sulawesi, Indonesia
  47. Field investigation of component failures for selected farm machinery used in small rice farming operations
  48. Near-infrared technology in agriculture: Rapid, simultaneous, and non-destructive determination of inner quality parameters on intact coffee beans
  49. The synergistic application of sucrose and various LED light exposures to enhance the in vitro growth of Stevia rebaudiana (Bertoni)
  50. Weather index-based agricultural insurance for flower farmers: Willingness to pay, sales, and profitability perspectives
  51. Meta-analysis of dietary Bacillus spp. on serum biochemical and antioxidant status and egg quality of laying hens
  52. Biochemical characterization of trypsin from Indonesian skipjack tuna (Katsuwonus pelamis) viscera
  53. Determination of C-factor for conventional cultivation and soil conservation technique used in hop gardens
  54. Empowering farmers: Unveiling the economic impacts of contract farming on red chilli farmers’ income in Magelang District, Indonesia
  55. Evaluating salt tolerance in fodder crops: A field experiment in the dry land
  56. Labor productivity of lowland rice (Oryza sativa L.) farmers in Central Java Province, Indonesia
  57. Cropping systems and production assessment in southern Myanmar: Informing strategic interventions
  58. The effect of biostimulants and red mud on the growth and yield of shallots in post-unlicensed gold mining soil
  59. Effects of dietary Adansonia digitata L. (baobab) seed meal on growth performance and carcass characteristics of broiler chickens: A systematic review and meta-analysis
  60. Analysis and structural characterization of the vid-pisco market
  61. Pseudomonas fluorescens SP007s enhances defense responses against the soybean bacterial pustule caused by Xanthomonas axonopodis pv. glycines
  62. A brief investigation on the prospective of co-composted biochar as a fertilizer for Zucchini plants cultivated in arid sandy soil
  63. Supply chain efficiency of red chilies in the production center of Sleman Indonesia based on performance measurement system
  64. Investment development path for developed economies: Is agriculture different?
  65. Power relations among actors in laying hen business in Indonesia: A MACTOR analysis
  66. High-throughput digital imaging and detection of morpho-physiological traits in tomato plants under drought
  67. Converting compression ignition engine to dual-fuel (diesel + CNG) engine and experimentally investigating its performance and emissions
  68. Structuration, risk management, and institutional dynamics in resolving palm oil conflicts
  69. Spacing strategies for enhancing drought resilience and yield in maize agriculture
  70. Composition and quality of winter annual agrestal and ruderal herbages of two different land-use types
  71. Investigating Spodoptera spp. diversity, percentage of attack, and control strategies in the West Java, Indonesia, corn cultivation
  72. Yield stability of biofertilizer treatments to soybean in the rainy season based on the GGE biplot
  73. Evaluating agricultural yield and economic implications of varied irrigation depths on maize yield in semi-arid environments, at Birfarm, Upper Blue Nile, Ethiopia
  74. Chemometrics for mapping the spatial nitrate distribution on the leaf lamina of fenugreek grown under varying nitrogenous fertilizer doses
  75. Pomegranate peel ethanolic extract: A promising natural antioxidant, antimicrobial agent, and novel approach to mitigate rancidity in used edible oils
  76. Transformative learning and engagement with organic farming: Lessons learned from Indonesia
  77. Tourism in rural areas as a broader concept: Some insights from the Portuguese reality
  78. Assessment enhancing drought tolerance in henna (Lawsonia inermis L.) ecotypes through sodium nitroprusside foliar application
  79. Edible insects: A survey about perceptions regarding possible beneficial health effects and safety concerns among adult citizens from Portugal and Romania
  80. Phenological stages analysis in peach trees using electronic nose
  81. Harvest date and salicylic acid impact on peanut (Arachis hypogaea L.) properties under different humidity conditions
  82. Hibiscus sabdariffa L. petal biomass: A green source of nanoparticles of multifarious potential
  83. Use of different vegetation indices for the evaluation of the kinetics of the cherry tomato (Solanum lycopersicum var. cerasiforme) growth based on multispectral images by UAV
  84. First evidence of microplastic pollution in mangrove sediments and its ingestion by coral reef fish: Case study in Biawak Island, Indonesia
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  87. Avocado peel by-product in cattle diets and supplementation with oregano oil and effects on production, carcass, and meat quality
  88. Optimizing inorganic blended fertilizer application for the maximum grain yield and profitability of bread wheat and food barley in Dawuro Zone, Southwest Ethiopia
  89. The acceptance of social media as a channel of communication and livestock information for sheep farmers
  90. Adaptation of rice farmers to aging in Thailand
  91. Combined use of improved maize hybrids and nitrogen application increases grain yield of maize, under natural Striga hermonthica infestation
  92. From aquatic to terrestrial: An examination of plant diversity and ecological shifts
  93. Statistical modelling of a tractor tractive performance during ploughing operation on a tropical Alfisol
  94. Participation in artisanal diamond mining and food security: A case study of Kasai Oriental in DR Congo
  95. Assessment and multi-scenario simulation of ecosystem service values in Southwest China’s mountainous and hilly region
  96. Analysis of agricultural emissions and economic growth in Europe in search of ecological balance
  97. Bacillus thuringiensis strains with high insecticidal activity against insect larvae of the orders Coleoptera and Lepidoptera
  98. Technical efficiency of sugarcane farming in East Java, Indonesia: A bootstrap data envelopment analysis
  99. Comparison between mycobiota diversity and fungi and mycotoxin contamination of maize and wheat
  100. Evaluation of cultivation technology package and corn variety based on agronomy characters and leaf green indices
  101. Exploring the association between the consumption of beverages, fast foods, sweets, fats, and oils and the risk of gastric and pancreatic cancers: Findings from case–control study
  102. Phytochemical composition and insecticidal activity of Acokanthera oblongifolia (Hochst.) Benth & Hook.f. ex B.D.Jacks. extract on life span and biological aspects of Spodoptera littoralis (Biosd.)
  103. Land use management solutions in response to climate change: Case study in the central coastal areas of Vietnam
  104. Evaluation of coffee pulp as a feed ingredient for ruminants: A meta-analysis
  105. Interannual variations of normalized difference vegetation index and potential evapotranspiration and their relationship in the Baghdad area
  106. Harnessing synthetic microbial communities with nitrogen-fixing activity to promote rice growth
  107. Agronomic and economic benefits of rice–sweetpotato rotation in lowland rice cropping systems in Uganda
  108. Response of potato tuber as an effect of the N-fertilizer and paclobutrazol application in medium altitude
  109. Bridging the gap: The role of geographic proximity in enhancing seed sustainability in Bandung District
  110. Evaluation of Abrams curve in agricultural sector using the NARDL approach
  111. Challenges and opportunities for young farmers in the implementation of the Rural Development Program 2014–2020 of the Republic of Croatia
  112. Yield stability of ten common bean (Phaseolus vulgaris L.) genotypes at different sowing dates in Lubumbashi, South-East of DR Congo
  113. Effects of encapsulation and combining probiotics with different nitrate forms on methane emission and in vitro rumen fermentation characteristics
  114. Phytochemical analysis of Bienertia sinuspersici extract and its antioxidant and antimicrobial activities
  115. Evaluation of relative drought tolerance of grapevines by leaf fluorescence parameters
  116. Yield assessment of new streak-resistant topcross maize hybrids in Benin
  117. Improvement of cocoa powder properties through ultrasonic- and microwave-assisted alkalization
  118. Potential of ecoenzymes made from nutmeg (Myristica fragrans) leaf and pulp waste as bioinsecticides for Periplaneta americana
  119. Analysis of farm performance to realize the sustainability of organic cabbage vegetable farming in Getasan Semarang, Indonesia
  120. Revealing the influences of organic amendment-derived dissolved organic matter on growth and nutrient accumulation in lettuce seedlings (Lactuca sativa L.)
  121. Identification of viruses infecting sweetpotato (Ipomoea batatas Lam.) in Benin
  122. Assessing the soil physical and chemical properties of long-term pomelo orchard based on tree growth
  123. Investigating access and use of digital tools for agriculture among rural farmers: A case study of Nkomazi Municipality, South Africa
  124. Does sex influence the impact of dietary vitD3 and UVB light on performance parameters and welfare indicators of broilers?
  125. Design of intelligent sprayer control for an autonomous farming drone using a multiclass support vector machine
  126. Deciphering salt-responsive NB-ARC genes in rice transcriptomic data: A bioinformatics approach with gene expression validation
  127. Review Articles
  128. Impact of nematode infestation in livestock production and the role of natural feed additives – A review
  129. Role of dietary fats in reproductive, health, and nutritional benefits in farm animals: A review
  130. Climate change and adaptive strategies on viticulture (Vitis spp.)
  131. The false tiger of almond, Monosteira unicostata (Hemiptera: Tingidae): Biology, ecology, and control methods
  132. A systematic review on potential analogy of phytobiomass and soil carbon evaluation methods: Ethiopia insights
  133. A review of storage temperature and relative humidity effects on shelf life and quality of mango (Mangifera indica L.) fruit and implications for nutrition insecurity in Ethiopia
  134. Green extraction of nutmeg (Myristica fragrans) phytochemicals: Prospective strategies and roadblocks
  135. Potential influence of nitrogen fertilizer rates on yield and yield components of carrot (Dacus carota L.) in Ethiopia: Systematic review
  136. Corn silk: A promising source of antimicrobial compounds for health and wellness
  137. State and contours of research on roselle (Hibiscus sabdariffa L.) in Africa
  138. The potential of phosphorus-solubilizing purple nonsulfur bacteria in agriculture: Present and future perspectives
  139. Minor millets: Processing techniques and their nutritional and health benefits
  140. Meta-analysis of reproductive performance of improved dairy cattle under Ethiopian environmental conditions
  141. Review on enhancing the efficiency of fertilizer utilization: Strategies for optimal nutrient management
  142. The nutritional, phytochemical composition, and utilisation of different parts of maize: A comparative analysis
  143. Motivations for farmers’ participation in agri-environmental scheme in the EU, literature review
  144. Evolution of climate-smart agriculture research: A science mapping exploration and network analysis
  145. Short Communications
  146. Music enrichment improves the behavior and leukocyte profile of dairy cattle
  147. Effect of pruning height and organic fertilization on the morphological and productive characteristics of Moringa oleifera Lam. in the Peruvian dry tropics
  148. Corrigendum
  149. Corrigendum to “Bioinformatics investigation of the effect of volatile and non-volatile compounds of rhizobacteria in inhibiting late embryogenesis abundant protein that induces drought tolerance”
  150. Corrigendum to “Composition and quality of winter annual agrestal and ruderal herbages of two different land-use types”
  151. Special issue: Smart Agriculture System for Sustainable Development: Methods and Practices
  152. Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy
  153. FruitVision: A deep learning based automatic fruit grading system
  154. Energy harvesting and ANFIS modeling of a PVDF/GO-ZNO piezoelectric nanogenerator on a UAV
  155. Effects of stress hormones on digestibility and performance in cattle: A review
  156. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part II
  157. Assessment of omega-3 and omega-6 fatty acid profiles and ratio of omega-6/omega-3 of white eggs produced by laying hens fed diets enriched with omega-3 rich vegetable oil
  158. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part II
  159. Special Issue on FCEM – International Web Conference on Food Choice & Eating Motivation: Message from the editor
  160. Fruit and vegetable consumption: Study involving Portuguese and French consumers
  161. Knowledge about consumption of milk: Study involving consumers from two European Countries – France and Portugal
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