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Technical efficiency changes of rice farming in the favorable irrigated areas of Indonesia

  • S. Sumaryanto , Sri Hery Susilowati , S. Saptana , Bambang Sayaka , Erma Suryani , Adang Agustian , A. Ashari , Helena Juliani Purba EMAIL logo , S. Sumedi , Saktyanu Kristyantoadi Dermoredjo , Tri Bastuti Purwantini , Rangga Ditya Yofa and Sahat Marulitua Pasaribu
Published/Copyright: May 31, 2023
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Open Agriculture
From the journal Open Agriculture Volume 8 Issue 1

Abstract

The main sources of rice production growth are increases in the yield and area harvested. Yield improvement is carried out through intensification, mainly using more inputs and better irrigation, while increasing the harvested area is associated with increasing the cropping intensity. Unfortunately, even in favorable irrigated areas, outcomes of the coupled approach are not always synergistic. This study aims to assess technical efficiency (TE), its changes in direction, and the factors responsible for inefficiency during the last 10 years. The data analyzed were those of rice farming through a panel survey of farmer households in several villages with favorable irrigation. The survey was conducted in 2010, 2016, and 2021. The results showed that the use of higher seed quality and inorganic fertilizers positively affected the yield. The TE level was relatively high but tended to degrade in these 3 years. The farmers’ TE in Java Island was higher than that outside Java. The older the farmer, the more inefficient the farmer was. The number of family members working in rice farming negatively affected efficiency. TE increased as the agricultural contribution to household income increased. On the other hand, the farmers’ educational background did not significantly affect TE. Based on these findings, it is recommended to encourage farmers to adopt higher quality seeds of improved rice varieties. It is also urgent to encourage young farmers to pursue rice farming as their main profession. In the middle and long term, breeding improved rice varieties adapted to climate stress will become a pressing need.

Keywords: rice farming; panel data; rice productivity; technical efficiency; inefficiency factor

1 Introduction

Indonesian’s staple food as the most vital source of carbohydrates is rice. The commodity is the main component of the food supply for national food security along with its relatively significant household consumption expenditure [1,2,3]. Indonesian per capita rice consumption is relatively high, i.e., 104 kg/year in 2019 [4]. Meanwhile, rice farming remains one of the main income sources for Indonesian farm households [5,6].

Rice crop is grown on various agroecosystems, either in lowland, upland, or swampland areas [7]. Most of the rice is produced in lowland areas so it is considered a determining factor for national rice availability. Among the lowland areas, irrigated land contributes the most share of rice production (67.5%) and rainfed lowland (27.5%). Both types of lowland areas (43% of the total area) are found on Java Island [8]. Irrigated land also plays an important role in the national rice production in some countries, such as Thailand [9], the Philippines [10], and Brazil [11].

The role of rice as the staple food of the majority of Indonesian is irreplaceable. On the one hand, Indonesia’s total population is quite large, and thus, sustainable food security through rice production growth should at least meet its consumption demand. On the other hand, production growth is an outcome of those harvested area and yield and the country’s ability to enhance agricultural land, especially for rice, is limited as the irrigation investment gets more expensive. This implies that the rice production growth will rely on enhanced cropping intensity and improved yield in the future. Consequently, rice farming's technical efficiency (TE) is essential. Rice production from irrigated lowlands will contribute more to the national food supply if it has high TE. Some studies showed that rice farming technical efficiencies in Indonesia varied among region, season, and year [12,13,14,15,16].

Referring to the empirical conditions, favorable irrigated areas are the lowland of rice-producing centers with satisfactory water irrigation availability, which encourage farmers to grow paddy at least twice or two growing seasons in 1 year. This is consistent with the fact that rice is the crop most chosen by farmers in the cropping pattern of the lowland areas. Rice is among the selected food crops because (i) rice price is the most stable among those of food commodities in Indonesia and (ii) the irrigation water system in the tertiary lowland blocks is water flow from one plot to another based on gravitation and technically the condition is suitable for paddy wetland cultivation but less suitable for other food commodities.

Most studies on TE were based on cross-sectional data, and it was rarely found any study using the panel data for evaluating the dynamics of TE in Indonesia. One that could be well noted is a study on National Farmers Panel (Patanas) in some villages including those favorable irrigated lowlands conducted by the Indonesian Center for Agricultural Socio-Economic and Policy Studies (ICASEPS) over several decades. The frontier production functions were applied to estimate the TE of the dairy industries in New South Wales and Victoria, Australia [17]. TE was estimated using panel data of dairy farms in New England by employing the stochastic production frontiers (SPFs) [18]. Estimations of a stochastic frontier production (SFP) function model to find the technical efficiencies were carried out using the panel data of the US domestic airline industry. The quarterly data of 12 airlines were used in this study from quarter I in 1970 to quarter II in 1978 [19]. Using the parametric production function and cost minimization hypothesis, it was possible to estimate firm-specific technical inefficiency based on the panel-data framework, which is allowed to vary over time [20]. The novelty of this study is the use of the panel data of rice farming yields in favorable irrigated areas for evaluating its TE in Indonesia. Using the unbalanced panel data, this study aims to (1) assess trends in the rice yield and TE on favorable irrigated rice fields, and (2) identify the technical inefficiency determinants.

2 Methods

2.1 Conceptual framework

The attainable yield is the result of various factors, which are internal or controllable and external or off-farmers’ control. The other influencing factors are the input use intensity and relative prices [21]. Enhancement in economic efficiency and farming sustainability could be carried out through yield improvement and efficient resource allocation.

Technical inefficiency exists basically due to the gap between the actual yield of a firm compared to its potential yield. The firm could not achieve its potential yield because it may cope with best practices or organizational factors. If the firms apply input I 2, it operates at C and its yield is Q 4 on AA’s actual production frontier and it is not technically efficient (Figure 1). To maximize its profit, the firm has to apply an input I 3 operating at D with output Q 3. However, to be technically efficient, the firm has to operate at the FF’s production frontier, i.e., at B with output Q 1. Applying input I 2, the firm’s TE is Q 2/Q 1. Thus, the firm’s technical inefficiency is (Q 1 − Q 2)/Q 1.

Figure 1 TE framework [26].
Figure 1

TE framework [26].

Experienced rice farmers do not always achieve the expected yield and TE. Variation arises when farmers in the same land, ecosystem, and cropping season apply the same technology. Farmers’ training, technical guidance, and credit access are among the actions to be taken [22]. Daily practice of rice farmers in homogenous regions and ecosystems tends to improve TE through maximizing yield efforts [23].

Factors affecting technical inefficiency varied among locations and seasons. Heriqbaldi et al. [24] used the SPF method and found a large inefficiency variation in the 15 provinces of study. The area size, income, and source of finance were the determining variables of TE. The farmers’ participation in field schools, farmers’ groups, crop spacing, seed quality, and cropping season affected significantly the TE. Kea et al. [25] showed that rice production in Cambodia depended on capital value and agricultural machinery adoption. They showed that the overall TE was 78.4%, indicating the possibility to improve TE due to relatively the same input and technology levels.

Applying panel data, it was found that the TE of rice production in Thailand was decreasing [27]. It was found that the TE decreased from the 1987/1988 cropping season to the 2007/2008 cropping season. A study by Alam et al. [28] in Bangladesh, applying an SPF, also indicated that there was a decrease in technical change from 1987 to 2004. Using the data envelopment analysis (DEA) approach, Pradhan [29] estimated that the average rice TE in Odisha, India, was 79.10%, which indicated that the input was overused up to 20.90%. Meanwhile, a study in Malaysia stated that rice farming had not been efficient but there was still potential for improvement with input rationalization [30]. Using the total factor productivity approach, rice farming in Indonesia tended to decrease due to relatively low TE [31].

The producers try their best to produce goods efficiently. In production economics, economic efficiency is realized if both technical and allocative efficiencies are attainable simultaneously. Empirically, the output market structure is oligopolistic resulting in the unpredictability of the farmers’ commodity. Accordingly, most Indonesian rice farmers try to improve only their TE [27,28].

The rice farmers in Indonesia try their best practices to achieve the best yield. However, most of them do not operate at the frontier production function. Some factors affecting this are an inappropriate time of input application, lack of input quantity and quality, weather disturbance, etc. Water supply for irrigating rice crops is a crucial issue as competing water use with the non-agricultural sector is more intense [29,30].

This study explored an econometric approach to estimate rice farming TE, its changes, and factors affecting inefficiency. Using unbalanced panel data, two approaches were employed, namely (i) TE estimation using a time-varying decay model (truncated-normal; random effect), and (ii) the Tobit model to describe factors affecting technical inefficiency.

2.2 Analytical methods

The most popular approaches to calculate efficiency are (1) the non-parametric techniques [32], the DEA based on the linear programming tools; and (2) parametric techniques [33,34], the stochastic frontier analysis (SFA) – SFP – based on econometric tools. DEA does not require a functional form and SFA does. The DEA (deterministic) approach ignores the random effects (noise) but the SFA approach takes it into account. Therefore, in the DEA model, any deviation is considered inefficiency but in SFA noise and inefficiency are considered. Both approaches seem to be useful and their use will depend on the objectives of the analysis [35].

According to Silva and Azurbi [36], the DEA approach has the advantage of considering many inputs and many outputs simultaneously. It also does not require a parametric specification of a functional form to construct the frontier. The major limitations of DEA are that it is difficult, conceptually, to separate the effects of uncontrollable environmental variables and the measurement error from the effect of differences in farm management and the presence of outliers. Coelli and Perelman [37] stated that the main disadvantage of DEA is that when the calculation of shadow prices is desired, only a range of prices can be derived for efficient firms.

One advantage of parametric methods is that they allow the testing of hypotheses such as those relating to the significance of included inputs and/or outputs, returns to scale, and so on [37]. Coelli [38] and Coelli et al. [39] stated that estimating a frontier production function had two advantages compared to that of an average production function. First, the average production function estimate indicates an average technology function achieved by farmers, while the frontier production function estimate is significantly affected by the farmers with the best farming practices revealing their adopted technology. Second, the frontier production function represents the best practice method results in which farming efficiency is measurable. According to Reinhard et al. [40], one of the most important characteristics of econometric models (SFA) is that it allows a specification in the case of panel data and the construction of confidence intervals. The literature review shows that SPF taking error term into the model is relevant to estimate rice farming efficiency.

Random effect modeling was initiated by Pitt and Lee [41]. They proposed a model with distributional assumptions about the error term, v i t ∩ i .d . N ( 0 , σ r 2 ) , which represents noise, and u i t ∩ i .d . N + ( 0 , σ u 2 ) reflects the distribution of the non-negative component, which translates the inefficiency of the model. The estimate method applied was a maximum likelihood. Some years later, Battese and Coelli [17] adopted this formula and developed it by proposing truncated-normal distribution for modeling technical inefficiency components with maximum likelihood as the estimate method. This study employed a maximum likelihood estimator (MLE) to estimate the yield function of panel stochastic frontier time-varying decay (PSF-TVD).

The model of Pitt and Lee [41] was also improved by Schmidt and Sickles [19] using a random effect model by the assumption of a particular distribution for the inefficiency component and regressors variable over time. The random effect model is expressed as follows:

(1) ln Y i = β 0 ⁎ + ∑ n = 1 N β n ln X n i t + v i t − u i ∗ ,

where β 0 ∗ = β 0 − E ( u i ) , u i ∗ = u i − E ( u i ) , and zero mean for u i ∗ and v i . With the introduction of this transformation, the zero mean for the error term, the GLS (generalized least squares) technique, can be applied to estimate the model. The random effect model operates in the same way as the error component (one-way) model described in the literature on panel data. To estimate this model, the GLS technique is used in two steps.

For the time-varying decay model, the log-likelihood function is derived as

(2) ln L = − 1 2 ∑ i = 1 N T i { ln ( 2 π ) + ln ( σ S 2 ) } − 1 2 ∑ i = 1 N ( T i − 1 ) ln ( 1 − γ ) − 1 2 ∑ i = 1 N ln 1 + ∑ i = 1 N η i t 2 − 1 γ − N ln { 1 − Φ ( z ˜ ) } − 1 2 N z ˜ 2 + ∑ i = 1 N ln { 1 − Φ ( − z i ∗ ) } + 1 2 ∑ i = 1 N z i ∗ 2 − 1 2 ∑ i = 1 N ∑ t = 1 T i ε i t 2 ( 1 − γ ) σ S 2 ,

where

(3) σ = ( σ u 2 + σ v 2 ) 1 2 , γ = σ u 2 / σ 2 , ε i t = y i t − x i t β , η i t = exp { − η ( t − T ) } , z ˜ = μ / ( γ σ 2 ) 1 / 2 , Φ ( ) ,

is the cumulative distribution function of the standard normal distribution, and

(4) z i ∗ = μ ( 1 − γ ) − s γ ∑ t = 1 T i η i t ε i t γ ( 1 − γ ) σ S 2 1 + ∑ t = 1 T i η i t 2 − 1 γ 1 / 2 .

Social interaction among individuals in the intra-region in the country is more intense due to their homogeneity than that in the inter-region. Thus, it is common to report standard errors that account for the clustering of units. Typically, the motivation given for clustering adjustments is that unobserved components in outcomes for units within clusters are correlated. However, because correlation may take place across more than one dimension, this motivation makes it difficult to justify why researchers use clustering in some dimensions, such as geographic [42]. The better parameter estimate method is maximum likelihood with clustered-robust standard errors.

Cluster-robust standard errors are now widely used, popularized in part by Reinhard et al. [40] and Bertrand et al. [43], who incorporated the method in Stata. Cameron and Miller [44] and Wooldridge [45] provide surveys, and lengthy expositions are given in Wooldridge [45]. Based on those arguments, the estimate method applied is a likelihood with clustered-robust standard errors, as its parameter estimates are the most efficient and consistent. The cluster approach is islands, i.e., Java and off-Java.

2.3 Empirical model

2.3.1 Estimating TE

TE can be measured using either the total production or yield. The yield function approach requires outputs, and all inputs are measured per hectare. This study applied a yield approach. Production inputs presumably affecting yields are inputs used per hectare consisting of seed, fertilizer, labor, and other expenses. There are also other factors affecting the yield hypothesized. First, water adequacy for rice farming. Second, the variables related to regional characteristics represent some factors such as agricultural development, population density and its implications on socio-cultural aspects and general condition of infrastructure, and farm-level water management.

Some rice farmers used their seed production and some of them adopted certified seed. The viability of certified seed is relatively higher resulting in a lower volume of seed grown for the same area than that of farmers’ seeds. The yield potential of the certified seed is also higher. In addition to the seed volume as one of the independent variables, the seed dummy was also introduced, i.e., (a) certified seed and (b) the farmers’ seed.

Besides seed, fertilizer, and labor, the model also incorporated expenses for herbicides, pesticides, liquid fertilizers, organic fertilizers, and irrigation fees. It was aimed to accommodate farmers’ recall on expense values but not their accurate volume of purchase for respected inputs due to the various brands of those inputs. Few farmers applied organic fertilizer, i.e., less than 3%, and it was incorporated into part of other expenses for simplifying purposes.

All of the sample rice farmers adopted inorganic fertilizers for their rice farming. The types and volumes of inorganic fertilizers varied depending on preference, price, and availability. There were three popular single inorganic fertilizers, i.e., urea, SP36, KCl, and one compound fertilizer, i.e., NPK (15:15:15). The farmers obtained information from the agricultural extension workers that inorganic fertilizers contained three nutrients most required by the rice crop, i.e., nitrogen, phosphorus, and potash. The main sources of nitrogen are urea, ZA, and NPK. Phosphorus comes from SP36 and NPK, while sources of potash are KCl and NPK fertilizers. ZA fertilizer is less popular with farmers. Most farmers well recognize urea and NPK fertilizers for rice farming application. However, only some farmers adopted SP36 and KCl fertilizers. The measurement unit for fertilizers used by the farmers in this study was based on their nutrient content. Assuming that Q Urea , Q ZA , Q SP 36 , Q KCl , and Q NPK represent the volume of urea, ZA, SP36, and NPK applied, and based on the label on its package, the contents of N, P, and K used in the rice farming are as follows:

N = 0.46 × Q Urea + 0.21 × Q ZA + 0.15 × Q NPK ,

P = 0.36 × Q SP 36 + 0.15 × Q NPK ,

(5) K = 0.60 × Q KCl + 0.15 × Q NPK .

The model applied is the stochastic Frontier production function using unbalanced panel data with the time-varying decay model:

(6) ln y i t = ln β 0 ∑ k = 1 6 β k ln x k i t + ∑ l = 1 4 δ l ln D l i t + v i t − u i t ,

where y and x 1 , x 2 , ⋯ x 6 variables in the model above were expressed per hectare per cropping season. The subscript i refers to the farming carried out each cropping season by the corresponding farmer, while t denotes the observed year, i.e., 2010, 2016, and 2021. The description of each variable is as follows:

  1. y = yield (kg/hectare).

  2. x 1 is the seed used, x 2 , x 3 , and x 4 are nutrient contents of nitrogen, phosphorous, and potash in the urea, ZA, SP36, and NPK fertilizers applied (kg/hectare).

  3. x 5 is the labor used (man-days equivalent/hectare).

  4. x 6 is another input outside x 1 , … , x 5 . Formerly, the measurement was in IDR in accordance with the year of survey conducted (2010, 2016, 2021). To get a suitable measurement, the value was deflated by the average price of the output (rice) in the village corresponding to its year and season. So, the new measurement is in kg rice equivalent per hectare.

The farmers could control only some factors affecting the rice yield in rice farming. Those external factors were not easily measured using ratios but they could be categorized and were represented in dummy variables. There are four dummy variables, namely:

D 1 = seed quality; 0 = low (uncertified seed), 1 = high (certified seed); base: 0.

D 2 = cropping season (I = rainy season, II = first dry season, and III = second dry season); base: cropping season I.

D 3 = rainfall regime (RR) and refers to the Walsh and Lawler approach (1981). There are three rainfall regimes in this study, namely RR_2, RR_3, RR_4 base: RR_2.

D 4 = region; 0 = Java Island, 1 = off-Java Island; base: Java Island.

The following are justifications for the dummy variables above. First, the effect of seeds on the yield is not only by their volume applied but also their quality. Theoretically, higher quality will give a higher yield. Second, the rice yield will be higher if the rice crop during certain periods gets sufficient inundation [46]. Water supply for rice farming comes from irrigation and rainfall. Rainfall among seasons and rainfall regimes is different. In the same rainfall regimes, the rainfall in season I > season II > season III. For the same seasons, the rainfall in RR_2 > RR_3 > RR_4. Third, the social economic conditions of rural Java are different from those in off-Java. Those factors were presumably affecting the achieved TE.

Based on SPF, it was possible to estimate the TE for each observation [17,20,47], namely:

(7) TE i t = exp ( − u i t ) .

It was assumed that u i was identically independent distribution (iid) and nonnegative error term normally distributed independently truncated at 0, with a certain average value and constant variance.

The change direction of TE ( η ) could be derived from the following relations:

(8) u i t = η i t u i = { exp [ − η ( t − T ) ] } u i ,

(9) η i t = exp [ − η ( t − T ) ] ,

(10) ln η i t = − η ( t − T ) ,

meanwhile η i t = u i t u i , thus:

(11) η ( t − T ) = − ln u i t u i ,

where η it represents time-varying TE conditions. The estimates give positive, negative, or equal to zero statistically. η values indicate TE changes.

(12) η = statistically η > 0 ⇒ representing increased technical efficiency statistically η < 0 ⇒ representing decreased technical efficiency statistically η = 0 ⇒ representing no technical efficiency change

2.3.2 Estimating TE

Estimating the factors affecting inefficiency was carried out using the Tobit model as follows:

(13) u i t = b 0 + ∑ m = 1 5 b m z m i t + ∑ w = 1 3 d w Dum w i t + ε i t ,

where u i t is the rice farming technical inefficiency of ith farmer’s household in tth year, z i t is the ith farmer’s household age in tth year (year), z 2 t is the ith farmer’s household formal educational level in tth year (year), z 3 t is the total household members working on the farm (person), z 4 i t is the total land holding size per year, included outside land holding for rice farming (hectare), z 5 t is the rice farming income share to farmer’s household income (%), Dum 1 represents the variable dummy of land-holding status (0 = owned, 1 = others), Dum 2 represents the variable dummy of the farmer’s main job (0 = on the farm, 1 = others), and Dum 3 represents the dummy variable of the region (0 = Java Island, 1 = off-Java Island).

2.4 Data

The primary data analyzed were panel data collected from the farmer’s household survey. The survey was carried out in 2010, 2016, and 2021 in seven villages and three rainfall regimes with their respective agroecosystems of favorable irrigated rice fields distributed in seven districts of five provinces in Indonesia (Table 1).

Table 1

Number of respondents by study location in 2010, 2016, and 2021

Island and province District* Subdistrict Village Respondents
2010 2016 2021
Java island
West Java Karawang (RR_3) Kutawaluya Sindangsari 27 19 19
Indramayu (RR_3) Lelea Tugu 28 21 20
Central Java Sragen (RR_3) Karang Malang Mojorejo 25 17 15
East Java Jember (RR_4) Jombang Padomasan 32 22 21
Off-Java island
North Sumatera Serdang Bedagai (RR_2) Perbaungan Lidah Tanah 19 13 13
South Sulawesi Sidrap (RR_3) Watang Pulu Carawali 29 23 20
Luwu (RR_2) Lamasi Salujambu 27 22 19
Total 187 137 127

*RR – rainfall regime.

The total sample farmers in 2010 were 187 households. Due to attrition, the samples in 2016 and 2021 were each of 137 and 128 households. Thus, the primary data analyzed were unbalanced panel data. It showed that the cross-sectional unit had unequal time-series observations. STATA application was employed for data processing and analysis in this article. STATA is able to regress unbalanced panel data to balanced panel data [48].

3 Results and discussion

As shown in Table 2, rice seed varieties adopted by the sample farmer in the study location were as follows. Most rice farmers, i.e., 444 out of 451 farm household samples, adopted the high-yielding varieties (HYVs). However, most of the sample farmers (73%) grew uncertified rice seeds, namely, they grew the seeds that they produced. The certified rice seed adoption rate was around 27%, mainly found in Java Island, i.e., Sindangsari, Tugu, Mojorejo, and Padomasan villages. Among the most popular HYVs were Inpari32, Mekongga, and Ciherang. The farmers sowed seeds several days before land tillage. Seed transplantation was carried out in about 2–3 weeks after sowing. HYVs were harvested at 95–100 days, on average, after planting.

Table 2

Sample farmers’ distribution by high-yielding rice varieties adopted in each study village: 2010, 2016, and 2021

No. Island/village Uncertified seed Certified seed Total Rice HYVs
N % N %
1 Java island
1.1 Tugu 20 29.9 47 70.1 67 Inpari32, Mekongga
1.2 Sindangsari 28 43.1 37 56.9 65 Inpari32
1.3 Padomasan 62 86.1 10 13.9 72 Inpari32, Mekongga
1.4 Mojorejo 42 73.7 15 26.3 57 Mekongga, Inpari32
2 Off-Java island
2.1 Carawali 70 97.2 2 2.8 72 Inpari32, Mekongga
2.2 Lidah Tanah 40 88.9 5 11.1 45 Inpari32
2.3 Salujambu 64 97.0 2 3.0 66 Ciherang, Inpari32
Total 326 73.4 118 26.6 444 Inpari32, Mekongga, Ciherang

The rice yield trend was not linear. The yield in 2010 was lower than that in 2016 (t = −3.3365), and the yield in 2016 was higher than that in 2021 (t = 3.6307). Inter-seasonal yield was found to be highest in the first growing season (rainy season) and lowest in the third growing season (second dry season). Inputs applied for the same growing seasons were relatively more intensive. Descriptive outputs and inputs applied per hectare and per growing season are depicted in the Appendix.

3.1 Stochastic frontier estimates

The stochastic frontier production function estimate results are shown in Table 3. Referring to a Cobb–Douglass model, the coefficients of each estimated parameter indicated its respective elasticity [49].

Table 3

Stochastic frontier production function estimate

Dependent variable: Yield Coefficient Robust Std. Err. P > |z|
Frontier
x 1 = seed (kg/ha) −0.017 0.016 0.286
x 2 = nitrogen (kg/ha) 0.093 0.047 0.046
x 3 = phosphorus (kg/ha) 0.051 0.007 0.000
x 4 = potash (kg/ha) 0.064 0.008 0.000
x 5 = labor (man-days eq./ha) 0.006 0.028 0.832
x 6 = other input (IDR standardized by average village output price) 0.014 0.023 0.560
Dummy variables
(a) Quality of seed 0.106 0.013 0.000
(b) Cropping season:
 II (first dry season) −0.034 0.033 0.310
 III (second dry season) −0.101 0.005 0.000
(c) Rainfall regime (RR)
 RR_3 0.009 0.011 0.411
 RR_4 −0.036 0.004 0.000
(d) Island −0.050 0.001 0.000
_cons 7.839 0.161 0.000
/lnsigma2 3.225 0.075 0.000
/ilgtgamma 6.002 0.056 0.000
/mu −187.899 10.191 0.000
/eta −0.338 0.023 0.000
sigma2 25.143 1.893
Gamma 0.998 0.000
sigma_u2 25.081 1.889
sigma_v2 0.062 0.005

Time-varying decay model (truncated-normal), group variable: unbal_farmer, time variable: year and season, Log pseudolikelihood = −133.2150, number of obs. = 1,012, number of groups = 507, Prob > chi2 = 0.0000, Wald chi2(2) = 1.07 × 109 (SE adjusted for two clusters in Island).

In general, most rice yield function estimates were significantly different, i.e., 9 out of 10 variables in the model. The signs of the coefficients met the general economic theory. Those variables were application rates of seeds, fertilizer nutrients of N, P, K, and pesticides, as well as the dummies of seed quality, cropping season, rainfall regime, and islands. Detailed descriptions of the nine variables are provided in the following sections.

3.1.1 Application of quality seed

The findings showed that the more seed volume the farmers applied, the yield was lowered significantly. It was in accordance with the results in the Nigeria case [50]. Conversely, the quality rice seed adoption improved the rice yield significantly. Technical irrigated lowland was quite suitable for quality seed application such that it positively affected the rice yield. On the other hand, low-quality seed application at a higher rate decreased the rice yield.

Based on estimation, high-quality seed application significantly increased the rice yield by 10.8% compared to the application of low-quality seed at a 99% significant level, ceteris paribus. This finding was in accordance with the research results of Theingi and Thanda [51] and Houngue and Nonvide [52] indicating that the quality seed adoption elasticity on the rice yield was 0.191 while its elasticity of low-quality seed, i.e., local seed, was only 0.072. The study of Jimi et al. [53] revealed that high TE was achieved by modern hybrid rice varieties compared to the traditional variety. Other studies showed that corn farming TE in Ghana was affected among others by improved variety adoption enhancement [52,53]. The findings pointed out that the quality seed supply and education for farmers to adopt quality rice seeds are strategic policies to be implemented.

3.1.2 Application of N, P, and K nutrients

The application of three main fertilizer nutrients had coefficients of 0.103, 0.067, and 0.074 with their respective significant levels of 99%. If the farmers increased the N nutrient application usually originating from urea, ZA, and the NPK fertilizer by 1%, the rice yield would significantly increase by 0.103% (ceteris paribus). It was also true for the application of P and K nutrients. This finding revealed that the technically irrigated lowland was still responsive to inorganic applications, especially those containing N nutrients. The result of this study was consistent with that of Kusnadi et al. [54] that analyzed rice production factors in some rice-producing centers in Indonesia. Thus, fertilizer supply, especially those containing N nutrients on time and appropriate volume in accordance with the rice crop growth demand is the right strategy for rice yield improvement.

Khan et al. [55] found that the application of inorganic fertilizers, namely urea and diammonium phosphate, in Pakistan significantly improved rice yield. In Myanmar, inorganic fertilizers also significantly affected smallholding rice farms [51]. Furthermore, Hendrani et al. [56] used SFA and the generalized linear model and found that rice farming applied the balanced combination of organic and inorganic fertilizers in West Java Province, Indonesia, resulting in 9% higher TE than the conventional method.

3.1.3 Other inputs

These inputs significantly (95%) affected the rice yield by 0.026. This implied that an increase of these inputs by 1% would increase the rice yield by 0.03% (ceteris paribus). In this context, most of the other inputs were pesticides besides herbicides, irrigation fees, and taxes. All of these inputs were valued in thousand rupiahs or IDR000.

The farmers usually applied pesticides after pests and/or disease attacks, and not anticipatively. Thus, the pesticide dosage applied by farmers depended on the pest attack intensity. The irrigated lowland farmers had good technical knowledge of dealing with the crop’s pests and diseases even though the pesticide effect was lower compared to those of other inputs. This finding was in accordance with other research findings that revealed pesticide volumes applied by the farmers did not improve the rice yield significantly [55,56,57,58]. However, this finding was different from those found in previous studies [7,23,50,59,60,61], which showed a significant positive impact of the applied pesticide on the rice yield.

3.1.4 Cropping season

Most farmers (67%) grew rice twice a year in the wet and first dry seasons. Some farmers (37%) grew this crop three times a year, namely the wet, first, and second dry seasons. Rice yields in the wet and first dry seasons were not significantly different. However, the rice yield in the second dry season was lower than those in the two previous seasons. This finding was in line with the study on irrigated rice in the Philippines where the rice yield in the wet season was higher than that in the dry season [10].

3.1.5 Rainfall regime

Referring to Walsh and Lawler [62], the rainfall regime was classified into seven categories, i.e., (i) very equitable, (ii) equitable but with a definite wetter season, (iii) rather seasonal with a short drier season, (iv) seasonal, (v) markedly seasonal with a long drier season, (vi) mostly rain in 3 months or less, and (vii) extreme, almost all rain in 1–2 months.

The estimated results showed that the expected rice yield in the rainfall regime (iii) is not significantly different from the regime (ii). On the other hand, the rice yield in the rainfall regime (iv) is different from that of the regime (ii). This confirmed the field phenomenon that the rainfall effect, mainly in the second dry season, was significant in the favorable irrigated areas.

3.1.6 Region

The Java Island area is only 6.75% of total Indonesia, but the rice harvested area on this island is 52.46% of this country. According to BPS, the rice yield in Java Island was on average, 6% higher than the national yield [63]. The lack of irrigation access, less intensive fertilizer application, and lower application of qualified seed impacted the rice yield outside Java to be relatively lower. This implies that improving the rice yield in off-Java could be carried out by improving irrigation access and more intensified fertilizer application. On the other hand, in the favorable irrigated area, the estimated results showed that the yield discrepancy between Java and off-Java was only 3.2%.

3.1.7 Gamma coefficient

Another important coefficient to be discussed in Table 3 is the gamma coefficient, which indicates the proportion of sigma u 2 with sigma v 2 (sigma u 2 + sigma v 2). Given the gamma coefficient of 0.997, it indicated that almost all rice yield variations were due to TE factors. This finding implied that rice farming technical skill enhancement and farmers’ managerial capacity improvement are the dominant factors to boost the rice yield in favorable irrigated areas of Indonesia. It was one of the very intensive agricultural extension impacts implemented in these areas. Other studies also indicated that agricultural extension enabled farmers to improve their technical skills in applying improved technology [7,62].

3.2 TE rating and its dynamics

Table 4 depicts the mean and coefficient variation of the actual yield, frontier yield, and TE rating in 2010, 2016, and 2021. Based on this finding and referring to the TE standard of 70% [39], it was concluded that the TE of rice farming in favorable irrigated areas in Indonesia, in general, was efficient (more than 87%).

Table 4

Rice farming TE in 2010, 2016, and 2021

Mean Coef. Var
Year: 2010 ( n = 419)
Actual yield (ton/ha) 5.669 0.250
Predicted (frontier) yield (ton/ha) 6.123 0.090
TE 0.881 0.075
Year: 2016 ( n = 302)
Actual yield (ton/ha) 6.057 0.267
Predicted (frontier) yield (ton/ha) 6.437 0.082
TE 0.878 0.083
Year: 2021 ( n = 291)
Actual yield (ton/ha) 5.583 0.279
Predicted (frontier) yield (ton/ha) 6.354 0.090
TE 0.871 0.103

During the periods of 2010–2011 and 2016–2021, there were little significant changes in the TE significantly, as depicted in Table 3, namely eta = −0.217 with a robust standard error of 0.008. A decrease in rice farming TE in lowland areas was also observed in other Asian countries even with larger magnitudes. For example, in Thailand, rice farming TE decreased by 17.76% between 1987/1988 and 2007/2008 [27]. It was also found that rice farming TE in Bangladesh decreased by 10.84% from 1987 to 2000 and by 18.92% between 2000 and 2004 [28].

The TE change over time is not always along with the output level change per area unit (productivity). Figure 2 shows that even though rice farming TE decreased between 2010 and 2016, its yield increased by 6% (MT-2) and 10.44% (MT-3). The decreased TE is a decreased yield of the input bundle increase. In other words, applied production inputs tended to expand between 2010 and 2016 in which the output increased ratio to increased input or diminishing marginal productivity of the input bundle. The increased input applied revealed a higher intensification level indicated by per hectare input use improvement, especially the fertilizer. More intensified practice dealt with other factors such as agriculture infrastructure, lowered soil fertility, and climate factors. This implies the productivity leveling off. Thus, educating farmers on production inputs such as utilizing more organic fertilizers followed by decreasing inorganic fertilizers could be an effort to improve the rice yield.

Figure 2 Mean of yield and TE by season and year.
Figure 2

Mean of yield and TE by season and year.

The distribution of farmers by the TE group is shown in Table 5. If the TE of 0.7 was used as the lower limit of high TE, the rice farmers classified with high TE in 2010, 2016, and 2021 were 90, 88, and 87%, respectively

Table 5

Distribution of TE levels by groups in 2010, 2016, and 2021 (%)

TE group 2010 2016 2021
TE ≤ 0.50 0.72 0.99 2.41
0.51 < TE ≤ 0.60 1.67 1.66 2.75
0.61 < TE ≤ 0.70 7.40 9.27 7.56
0.71 < TE ≤ 0.80 40.10 39.40 39.86
TE > 0.80 50.12 48.68 47.42

3.3 Factors affecting technical inefficiency

There were five out of eight variables affecting rice farming technical inefficiency significantly for the observed period (Table 6). The five variables that were significant at the 95% level were the household head age, the total family labor working on farms, the total land holding size, the agricultural income share, and the main jobs of household heads. The variables that were not significant were the educational level, land ownership status, and regional dummy of household heads. The following are descriptions of both significant and insignificant variables.

Table 6

Parameter estimate of factors affecting inefficiency ( u i t )

Coefficient Std. Err. P > |z|
z 1 t Age of the household head 0.0011 0.0004 0.0030
z 1 t Education of the household head −0.0012 0.0010 0.2060
z 1 t Number of household members working on the farm 0.0081 0.0017 0.0000
z 1 t Total land holding size 0.0114 0.0029 0.0000
z 1 t Share of the farm income −0.0219 0.0090 0.0140
Dummy variables:
Dum 1 Main job of the household head (0 = agriculture, 1 = non) 0.0228 0.0105 0.0300
Dum 1 Tenurial status (0 = owned, 1 = non-owned) 0.0100 0.0078 0.1980
Dum 1 Region (0 = Java, 1 = off-Java) 0.0049 0.0099 0.6210
_cons 0.0490 0.0230 0.0330
/sigma_u 0.0581 0.0037 0.0000
/sigma_e 0.0405 0.0018 0.0000
Rho 0.6732 0.0361

Log-likelihood = 643.83667.

3.3.1 Household heads’ age

The household head age parameter was positive, indicating that the older the household head the higher the technical inefficiency. Rice farming carried out by younger farmers would be more efficient as they were stronger physically and more capable. Older farmers are usually more conservative in adopting new technology. The other studies were in accordance with this study [23,27,50,60,63–65].

3.3.2 Total household members working on the farm

This variable was significantly positive at a 95% level on rice farming technical inefficiency. The more household members working on the farm, the more inefficient rice farming was. This finding was in accordance with other studies stating that more total household members working on the farm indicated more inefficient farming compared to those with fewer household members [39,52,66]. It was possible to take place if the household members involved in rice farming were less skilled. Rice farming would be more efficient if hired labor was employed rather than family labor. Hired labor played more role in increasing efficiency as most of the production process depends on them. Previous studies found that more farmer household members increased TE due to increased labor and substituted hired labor [10,27,28,67,68].

Total households working on farms tended to increase by more than twofold during the three periods of the panel, i.e., on average from 1.43 persons per household in 2016 to 3.89 persons per household in 2021. The drastic increase in farmer’s household members working on farms was one of the impacts of the COVID-19 pandemic. Some household members who previously worked in urban areas went back to their villages due to labor layoff and worked on farms with fewer skills.

3.3.3 Total land holding size

The total land holding size was the outcome of the area size with cropping intensity. In 1 year, the average land areas for rice farming in Java Island and off-Java were 0.93 and 1.19 ha, respectively. The average total land holding areas in Java Island and off-Java were 1.22 and 1.12 ha, respectively.

The total land holding significantly and positively affected the rice farming technical inefficiency. This indicated that the more a farmer’s total land-holding area, the more technically inefficient the rice farming would be. The total land holding size in this study consisted of lowland, perennial-tree planted land, dryland, and home yard. Thus, the findings of the study were different from the other studies. Some studies found that the farmers with more land-holding sizes were more technically efficient [27,28,69,70].

3.3.4 Share of farm income

The farmer family’s income shares from farming negatively affected the technical inefficiency of rice farming at a 95% level for the 2010, 2016, and 2021 cropping seasons. This revealed that the higher the farming income ratio, the rice farming would be more technically efficient. Other studies also revealed that the higher farming income ratio revealed the farmer household’s participation in managing rice farming to be more efficient [10,27].

The average sample household’s income farming share in 3 years was 69.8% but it tended to decrease, i.e., 76% in 2010, 74% in 2016, and 54% in 2021. This decreased ratio of farmers’ household income was in line with those of household heads’ main jobs of farming that tended to decline, namely 95, 89, and 91% in 2010, 2016, and 2021, respectively. The increased ratio of household heads with farming main jobs in 2021 was affected by the COVID-19 pandemic ratio.

3.3.5 Main jobs of the household heads

This dummy significantly and positively affected technical inefficiency. This revealed that the household heads with non-farming main jobs decreased the rice farming TE. Another study also showed that increasing non-farming job opportunities by 10% would increase farming technical inefficiency by 0.3% [28]. This result was in accordance with the study of Balcombe et al. [71] that non-farming main jobs would distract the farmers’ focus on farming activities.

4 Conclusions

This research found the TE estimate of rice farming using the panel data in the favorable areas of irrigated lowlands. During the last decade, the rice farming TE in the favorable irrigated areas in Indonesia decreased significantly even though its magnitude was relatively small. This direction change was not linear, i.e., during the first 5 years it tended to increase and tended to decrease during the next five years. In general, the rice farming TE in the region, which was more advanced economically, namely in rural areas of Java Island, was higher.

The main foothold of rice yield improvement is quality rice seed adoption, namely certified seed of improved variety. The yield response to chemical fertilizer was significantly positive but its magnitude was small.

Higher TE was achieved by the relatively younger farmers and those with more income share from agriculture. The farmers who employed more family labor tended to be more inefficient technically. On the other hand, farmers’ formal educational background did not significantly affect TE.

Based on the research results, there are some policy recommendations. First, enhancing the adoption of certified rice seeds of improved variety. In the middle and long term, it is urgent to conduct a research and development program for improving rice varieties adaptive to climate change stress. Second, a conducive incentive policy on encouraging young farmers to rely on rice farming as their main job. It is suggested that the research be continued on larger areas including those of non-favorable irrigated areas.

Acknowledgments

The authors would like to thank the Director of the Indonesian Center for Agricultural Socio-Economic and Policy Studies (ICASEPS), Ministry of Agriculture, for supporting and financing this research. We also thank the enumerators who have helped to collect data during the research.

  1. Funding information: The study was financed with sources of Indonesian Center for Agricultural Socio-Economic and Policy Studies (ICASEPS) and Indonesian Ministry of Agriculture.

  2. Conflict of interest: The authors state no conflict of interest.

  3. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Appendix

Descriptive statistics of yields and per hectare inputs used in rice farming by cropping season and year.

Year 2010Year 2016Year 2021
ObsMeanStd. Dev.ObsMeanStd. Dev.ObsMeanStd. Dev.
Cropping season I
Y1875996.791488.001376210.151735.941265851.281747.01
x118745.0017.3413744.1618.5112643.7917.90
x2187138.1934.36137145.3533.93126140.5943.70
x318746.7013.2113755.7014.7112650.7713.82
x418740.7419.8813744.6718.3412646.6821.11
x5187226.01192.07137270.90244.85126228.54237.24
x6187176.85104.14137241.17105.77126231.66102.02
Cropping season II
Y1755459.161176.341306038.391489.821125315.671449.84
x117545.6217.4113044.6818.9011245.2618.15
x2175138.5134.11130142.2531.78112144.8544.05
x317545.9112.9813055.8414.5311251.0013.05
x417538.9919.2913044.7817.9611245.2519.72
x5175229.23201.37130258.78230.08112223.93235.08
x6175175.41107.09130219.7996.34112221.4896.62
Cropping season III
Y575239.161647.82355522.401515.89535510.691171.80
x15744.7216.223540.9012.235341.8812.03
x257144.8437.1935158.6227.2553153.3836.38
x35749.6014.173564.0513.845355.1813.49
x45740.7616.053554.0413.705352.3419.61
x557261.50220.7435287.17258.9153300.21281.35
x657140.47108.9835274.1595.7653217.8893.52

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Received: 2023-02-20
Revised: 2023-05-08
Accepted: 2023-05-09
Published Online: 2023-05-31

© 2023 the author(s), published by De Gruyter

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

Articles in the same Issue

  1. Regular Articles
  2. The impact of COVID-19 pandemic on business risks and potato commercial model
  3. Effects of potato (Solanum tuberosum L.)–Mucuna pruriens intercropping pattern on the agronomic performances of potato and the soil physicochemical properties of the western highlands of Cameroon
  4. Machine learning-based prediction of total phenolic and flavonoid in horticultural products
  5. Revamping agricultural sector and its implications on output and employment generation: Evidence from Nigeria
  6. Does product certification matter? A review of mechanism to influence customer loyalty in the poultry feed industry
  7. Farmer regeneration and knowledge co-creation in the sustainability of coconut agribusiness in Gorontalo, Indonesia
  8. Lablab purpureus: Analysis of landraces cultivation and distribution, farming systems, and some climatic trends in production areas in Tanzania
  9. The effects of carrot (Daucus carota L.) waste juice on the performances of native chicken in North Sulawesi, Indonesia
  10. Properties of potassium dihydrogen phosphate and its effects on plants and soil
  11. Factors influencing the role and performance of independent agricultural extension workers in supporting agricultural extension
  12. The fate of probiotic species applied in intensive grow-out ponds in rearing water and intestinal tracts of white shrimp, Litopenaeus vannamei
  13. Yield stability and agronomic performances of provitamin A maize (Zea mays L.) genotypes in South-East of DR Congo
  14. Diallel analysis of length and shape of rice using Hayman and Griffing method
  15. Physicochemical and microbiological characteristics of various stem bark extracts of Hopea beccariana Burck potential as natural preservatives of coconut sap
  16. Correlation between descriptive and group type traits in the system of cow’s linear classification of Ukrainian Brown dairy breed
  17. Meta-analysis of the influence of the substitution of maize with cassava on performance indices of broiler chickens
  18. Bacteriocin-like inhibitory substance (BLIS) produced by Enterococcus faecium MA115 and its potential use as a seafood biopreservative
  19. Meta-analysis of the benefits of dietary Saccharomyces cerevisiae intervention on milk yield and component characteristics in lactating small ruminants
  20. Growth promotion potential of Bacillus spp. isolates on two tomato (Solanum lycopersicum L.) varieties in the West region of Cameroon
  21. Prioritizing IoT adoption strategies in millennial farming: An analytical network process approach
  22. Soil fertility and pomelo yield influenced by soil conservation practices
  23. Soil macrofauna under laying hens’ grazed fields in two different agroecosystems in Portugal
  24. Factors affecting household carbohydrate food consumption in Central Java: Before and during the COVID-19 pandemic
  25. Properties of paper coated with Prunus serotina (Ehrh.) extract formulation
  26. Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks
  27. Molecular and phenotypic markers for pyramiding multiple traits in rice
  28. Natural product nanofibers derived from Trichoderma hamatum K01 to control citrus anthracnose caused by Colletotrichum gloeosporioides
  29. Role of actors in promoting sustainable peatland management in Kubu Raya Regency, West Kalimantan, Indonesia
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  33. Economic performance of smallholder soya bean production in Kwara State, Nigeria
  34. Indonesian rice farmers’ perceptions of different sources of information and their effect on farmer capability
  35. Feed preference, body condition scoring, and growth performance of Dohne Merino ram fed varying levels of fossil shell flour
  36. Assessing the determinant factors of risk strategy adoption to mitigate various risks: An experience from smallholder rubber farmers in West Kalimantan Province, Indonesia
  37. Analysis of trade potential and factors influencing chili export in Indonesia
  38. Grade-C kenaf fiber (poor quality) as an alternative material for textile crafts
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  40. Palm oil cluster resilience to enhance indigenous welfare by innovative ability to address land conflicts: Evidence of disaster hierarchy
  41. Factors determining cassava farmers’ accessibility to loan sources: Evidence from Lampung, Indonesia
  42. Tailoring business models for small-medium food enterprises in Eastern Africa can drive the commercialization and utilization of vitamin A rich orange-fleshed sweet potato puree
  43. Revitalizing sub-optimal drylands: Exploring the role of biofertilizers
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  45. Design and fabrication of a fish feed mixing cum pelleting machine for small-medium scale aquaculture industry
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  48. How intergenerational farmers negotiate their identity in the era of Agriculture 4.0: A multiple-case study in Indonesia
  49. Responses of broiler chickens to incremental levels of water deprivation: Growth performance, carcass characteristics, and relative organ weights
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  51. Effect of short-term grazing exclusion on herbage species composition, dry matter productivity, and chemical composition of subtropical grasslands
  52. Analysis of beef market integration between consumer and producer regions in Indonesia
  53. Analysing the sustainability of swamp buffalo (Bubalus bubalis carabauesis) farming as a protein source and germplasm
  54. Toxicity of Calophyllum soulattri, Piper aduncum, Sesamum indicum and their potential mixture for control Spodoptera frugiperda
  55. Consumption profile of organic fruits and vegetables by a Portuguese consumer’s sample
  56. Phenotypic characterisation of indigenous chicken in the central zone of Tanzania
  57. Diversity and structure of bacterial communities in saline and non-saline rice fields in Cilacap Regency, Indonesia
  58. Isolation and screening of lactic acid bacteria producing anti-Edwardsiella from the gastrointestinal tract of wild catfish (Clarias gariepinus) for probiotic candidates
  59. Effects of land use and slope position on selected soil physicochemical properties in Tekorsh Sub-Watershed, East Gojjam Zone, Ethiopia
  60. Design of smart farming communication and web interface using MQTT and Node.js
  61. Assessment of bread wheat (Triticum aestivum L.) seed quality accessed through different seed sources in northwest Ethiopia
  62. Estimation of water consumption and productivity for wheat using remote sensing and SEBAL model: A case study from central clay plain Ecosystem in Sudan
  63. Agronomic performance, seed chemical composition, and bioactive components of selected Indonesian soybean genotypes (Glycine max [L.] Merr.)
  64. The role of halal requirements, health-environmental factors, and domestic interest in food miles of apple fruit
  65. Subsidized fertilizer management in the rice production centers of South Sulawesi, Indonesia: Bridging the gap between policy and practice
  66. Factors affecting consumers’ loyalty and purchase decisions on honey products: An emerging market perspective
  67. Inclusive rice seed business: Performance and sustainability
  68. Design guidelines for sustainable utilization of agricultural appropriate technology: Enhancing human factors and user experience
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  70. Synergic effect of Arbuscular mycorrhizal fungi and potassium fertilizer improves biomass-related characteristics of cocoa seedlings to enhance their drought resilience and field survival
  71. Control measure of sweet potato weevil (Cylas formicarius Fab.) (Coleoptera: Curculionidae) in endemic land of entisol type using mulch and entomopathogenic fungus Beauveria bassiana
  72. In vitro and in silico study for plant growth promotion potential of indigenous Ochrobactrum ciceri and Bacillus australimaris
  73. Effects of repeated replanting on yield, dry matter, starch, and protein content in different potato (Solanum tuberosum L.) genotypes
  74. Review Articles
  75. Nutritional and chemical composition of black velvet tamarind (Dialium guineense Willd) and its influence on animal production: A review
  76. Black pepper (Piper nigrum Lam) as a natural feed additive and source of beneficial nutrients and phytochemicals in chicken nutrition
  77. The long-crowing chickens in Indonesia: A review
  78. A transformative poultry feed system: The impact of insects as an alternative and transformative poultry-based diet in sub-Saharan Africa
  79. Short Communication
  80. Profiling of carbonyl compounds in fresh cabbage with chemometric analysis for the development of freshness assessment method
  81. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part I
  82. Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible–shortwave near-infrared spectroscopy
  83. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part I
  84. Traditional agri-food products and sustainability – A fruitful relationship for the development of rural areas in Portugal
  85. Consumers’ attitudes toward refrigerated ready-to-eat meat and dairy foods
  86. Breakfast habits and knowledge: Study involving participants from Brazil and Portugal
  87. Food determinants and motivation factors impact on consumer behavior in Lebanon
  88. Comparison of three wine routes’ realities in Central Portugal
  89. Special Issue on Agriculture, Climate Change, Information Technology, Food and Animal (ACIFAS 2020)
  90. Environmentally friendly bioameliorant to increase soil fertility and rice (Oryza sativa) production
  91. Enhancing the ability of rice to adapt and grow under saline stress using selected halotolerant rhizobacterial nitrogen fixer
https://doi.org/10.1515/opag-2022-0207
Keywords for this article
rice farming; panel data; rice productivity; technical efficiency; inefficiency factor
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. The impact of COVID-19 pandemic on business risks and potato commercial model
  3. Effects of potato (Solanum tuberosum L.)–Mucuna pruriens intercropping pattern on the agronomic performances of potato and the soil physicochemical properties of the western highlands of Cameroon
  4. Machine learning-based prediction of total phenolic and flavonoid in horticultural products
  5. Revamping agricultural sector and its implications on output and employment generation: Evidence from Nigeria
  6. Does product certification matter? A review of mechanism to influence customer loyalty in the poultry feed industry
  7. Farmer regeneration and knowledge co-creation in the sustainability of coconut agribusiness in Gorontalo, Indonesia
  8. Lablab purpureus: Analysis of landraces cultivation and distribution, farming systems, and some climatic trends in production areas in Tanzania
  9. The effects of carrot (Daucus carota L.) waste juice on the performances of native chicken in North Sulawesi, Indonesia
  10. Properties of potassium dihydrogen phosphate and its effects on plants and soil
  11. Factors influencing the role and performance of independent agricultural extension workers in supporting agricultural extension
  12. The fate of probiotic species applied in intensive grow-out ponds in rearing water and intestinal tracts of white shrimp, Litopenaeus vannamei
  13. Yield stability and agronomic performances of provitamin A maize (Zea mays L.) genotypes in South-East of DR Congo
  14. Diallel analysis of length and shape of rice using Hayman and Griffing method
  15. Physicochemical and microbiological characteristics of various stem bark extracts of Hopea beccariana Burck potential as natural preservatives of coconut sap
  16. Correlation between descriptive and group type traits in the system of cow’s linear classification of Ukrainian Brown dairy breed
  17. Meta-analysis of the influence of the substitution of maize with cassava on performance indices of broiler chickens
  18. Bacteriocin-like inhibitory substance (BLIS) produced by Enterococcus faecium MA115 and its potential use as a seafood biopreservative
  19. Meta-analysis of the benefits of dietary Saccharomyces cerevisiae intervention on milk yield and component characteristics in lactating small ruminants
  20. Growth promotion potential of Bacillus spp. isolates on two tomato (Solanum lycopersicum L.) varieties in the West region of Cameroon
  21. Prioritizing IoT adoption strategies in millennial farming: An analytical network process approach
  22. Soil fertility and pomelo yield influenced by soil conservation practices
  23. Soil macrofauna under laying hens’ grazed fields in two different agroecosystems in Portugal
  24. Factors affecting household carbohydrate food consumption in Central Java: Before and during the COVID-19 pandemic
  25. Properties of paper coated with Prunus serotina (Ehrh.) extract formulation
  26. Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks
  27. Molecular and phenotypic markers for pyramiding multiple traits in rice
  28. Natural product nanofibers derived from Trichoderma hamatum K01 to control citrus anthracnose caused by Colletotrichum gloeosporioides
  29. Role of actors in promoting sustainable peatland management in Kubu Raya Regency, West Kalimantan, Indonesia
  30. Small-scale coffee farmers’ perception of climate-adapted attributes in participatory coffee breeding: A case study of Gayo Highland, Aceh, Indonesia
  31. Optimization of extraction using surface response methodology and quantification of cannabinoids in female inflorescences of marijuana (Cannabis sativa L.) at three altitudinal floors of Peru
  32. Production factors, technical, and economic efficiency of soybean (Glycine max L. Merr.) farming in Indonesia
  33. Economic performance of smallholder soya bean production in Kwara State, Nigeria
  34. Indonesian rice farmers’ perceptions of different sources of information and their effect on farmer capability
  35. Feed preference, body condition scoring, and growth performance of Dohne Merino ram fed varying levels of fossil shell flour
  36. Assessing the determinant factors of risk strategy adoption to mitigate various risks: An experience from smallholder rubber farmers in West Kalimantan Province, Indonesia
  37. Analysis of trade potential and factors influencing chili export in Indonesia
  38. Grade-C kenaf fiber (poor quality) as an alternative material for textile crafts
  39. Technical efficiency changes of rice farming in the favorable irrigated areas of Indonesia
  40. Palm oil cluster resilience to enhance indigenous welfare by innovative ability to address land conflicts: Evidence of disaster hierarchy
  41. Factors determining cassava farmers’ accessibility to loan sources: Evidence from Lampung, Indonesia
  42. Tailoring business models for small-medium food enterprises in Eastern Africa can drive the commercialization and utilization of vitamin A rich orange-fleshed sweet potato puree
  43. Revitalizing sub-optimal drylands: Exploring the role of biofertilizers
  44. Effects of salt stress on growth of Quercus ilex L. seedlings
  45. Design and fabrication of a fish feed mixing cum pelleting machine for small-medium scale aquaculture industry
  46. Indicators of swamp buffalo business sustainability using partial least squares structural equation modelling
  47. Effect of arbuscular mycorrhizal fungi on early growth, root colonization, and chlorophyll content of North Maluku nutmeg cultivars
  48. How intergenerational farmers negotiate their identity in the era of Agriculture 4.0: A multiple-case study in Indonesia
  49. Responses of broiler chickens to incremental levels of water deprivation: Growth performance, carcass characteristics, and relative organ weights
  50. The improvement of horticultural villages sustainability in Central Java Province, Indonesia
  51. Effect of short-term grazing exclusion on herbage species composition, dry matter productivity, and chemical composition of subtropical grasslands
  52. Analysis of beef market integration between consumer and producer regions in Indonesia
  53. Analysing the sustainability of swamp buffalo (Bubalus bubalis carabauesis) farming as a protein source and germplasm
  54. Toxicity of Calophyllum soulattri, Piper aduncum, Sesamum indicum and their potential mixture for control Spodoptera frugiperda
  55. Consumption profile of organic fruits and vegetables by a Portuguese consumer’s sample
  56. Phenotypic characterisation of indigenous chicken in the central zone of Tanzania
  57. Diversity and structure of bacterial communities in saline and non-saline rice fields in Cilacap Regency, Indonesia
  58. Isolation and screening of lactic acid bacteria producing anti-Edwardsiella from the gastrointestinal tract of wild catfish (Clarias gariepinus) for probiotic candidates
  59. Effects of land use and slope position on selected soil physicochemical properties in Tekorsh Sub-Watershed, East Gojjam Zone, Ethiopia
  60. Design of smart farming communication and web interface using MQTT and Node.js
  61. Assessment of bread wheat (Triticum aestivum L.) seed quality accessed through different seed sources in northwest Ethiopia
  62. Estimation of water consumption and productivity for wheat using remote sensing and SEBAL model: A case study from central clay plain Ecosystem in Sudan
  63. Agronomic performance, seed chemical composition, and bioactive components of selected Indonesian soybean genotypes (Glycine max [L.] Merr.)
  64. The role of halal requirements, health-environmental factors, and domestic interest in food miles of apple fruit
  65. Subsidized fertilizer management in the rice production centers of South Sulawesi, Indonesia: Bridging the gap between policy and practice
  66. Factors affecting consumers’ loyalty and purchase decisions on honey products: An emerging market perspective
  67. Inclusive rice seed business: Performance and sustainability
  68. Design guidelines for sustainable utilization of agricultural appropriate technology: Enhancing human factors and user experience
  69. Effect of integrate water shortage and soil conditioners on water productivity, growth, and yield of Red Globe grapevines grown in sandy soil
  70. Synergic effect of Arbuscular mycorrhizal fungi and potassium fertilizer improves biomass-related characteristics of cocoa seedlings to enhance their drought resilience and field survival
  71. Control measure of sweet potato weevil (Cylas formicarius Fab.) (Coleoptera: Curculionidae) in endemic land of entisol type using mulch and entomopathogenic fungus Beauveria bassiana
  72. In vitro and in silico study for plant growth promotion potential of indigenous Ochrobactrum ciceri and Bacillus australimaris
  73. Effects of repeated replanting on yield, dry matter, starch, and protein content in different potato (Solanum tuberosum L.) genotypes
  74. Review Articles
  75. Nutritional and chemical composition of black velvet tamarind (Dialium guineense Willd) and its influence on animal production: A review
  76. Black pepper (Piper nigrum Lam) as a natural feed additive and source of beneficial nutrients and phytochemicals in chicken nutrition
  77. The long-crowing chickens in Indonesia: A review
  78. A transformative poultry feed system: The impact of insects as an alternative and transformative poultry-based diet in sub-Saharan Africa
  79. Short Communication
  80. Profiling of carbonyl compounds in fresh cabbage with chemometric analysis for the development of freshness assessment method
  81. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part I
  82. Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible–shortwave near-infrared spectroscopy
  83. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part I
  84. Traditional agri-food products and sustainability – A fruitful relationship for the development of rural areas in Portugal
  85. Consumers’ attitudes toward refrigerated ready-to-eat meat and dairy foods
  86. Breakfast habits and knowledge: Study involving participants from Brazil and Portugal
  87. Food determinants and motivation factors impact on consumer behavior in Lebanon
  88. Comparison of three wine routes’ realities in Central Portugal
  89. Special Issue on Agriculture, Climate Change, Information Technology, Food and Animal (ACIFAS 2020)
  90. Environmentally friendly bioameliorant to increase soil fertility and rice (Oryza sativa) production
  91. Enhancing the ability of rice to adapt and grow under saline stress using selected halotolerant rhizobacterial nitrogen fixer
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