The impact of migration on rice farming technical efficiency in Indonesia

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This study investigates the impact of migration on rice farming Technical efficiency in the Rokan Hulu District Indonesia. It as a measure of rice farming performance and a way to raise productivity without an increase in the inputs used.

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 10, Issue 03, March 2019, pp. 1703-1712. Article ID: IJMET_10_03_172 Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3 ISSN Print: 0976-6340 and ISSN Online: 0976-6359 © IAEME Publication Scopus Indexed THE IMPACT OF MIGRATION ON RICE FARMING TECHNICAL EFFICIENCY IN INDONESIA Defidelwina* Ph.D Candidate on Agricultural Science Study Program, Universitas Gadjah Mada, Yogyakarta, Indonesia Agribusiness Study Program, Faculty of Agriculture, University of Pasir Pengaraian, Riau, Indonesia Jamhari, Lestari Rahayu Waluyati and Sri Widodo Agricultural Science Study Program, Faculty of Agriculture, Gadjah Mada University, Yogyakarta, Indonesia *Corresponding Author ABSTRACT This study investigates the impact of migration on rice farming Technical efficiency in the Rokan Hulu District Indonesia. It as a measure of rice farming performance and a way to raise productivity without an increase in the inputs used. We investigate and discuss the technical efficiency of migrants and indigenous farmers in terms of characteristics and socio-economic conditions. We argue that it contributes to the policy-making basis in increasing rural economic growth. Furthermore, existing research focuses on rural-urban migration. But, we rarely found the literature on reverse flows and its impact, especially staple food crops. The primary data were obtained from interviews and analyzed using one step stochastic frontier production function. The study shows that migration has a negative impact on technical efficiency and indigenous farmers are more technically efficient than migrant ones. The rice farmers are expected to increase their average technical efficiency level by 13%. To reach their potential production, the farmers must improve their labor management. Key words: Technical efficiency, stochastic frontier, indigenous farmers, migrant. Cite this Article Defidelwina, Jamhari, Lestari Rahayu Waluyati and Sri Widodo, the Impact of Migration on Rice Farming Technical Efficiency in Indonesia, International Journal of Mechanical Engineering and Technology, 10(3), 2019, pp. 1703-1712. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=3 http://www.iaeme.com/IJMET/index.asp 1703 editor@iaeme.com
Defidelwina, Jamhari, Lestari Rahayu Waluyati and Sri Widodo 1. INTRODUCTION Migration and agriculture are influenced by each other. Migration can occur in two directions, come or out of the agricultural sector. In General, it causes by the pull factors such as opportunities for economic improvement, social encouragement, political environment [1], education, health and job [2]. So, migration policy and agricultural development have become topical issues of interest to many researchers [3]. Whether migration will improve or worsen the conditions of agricultural households and their communities, in the long run, the question that cannot be answered in a short time [4]. The conditions of agricultural households will depend on the sustainability of the farming they manage which is reflected in how farm perform. One measure of farm performance is technical efficiency. Technical efficiency refers to how land to get the maximum output from a bundle of inputs based on certain production technologies[5];[6]. In case, assuming that the input used is the same, farming that is technically inefficient tends to get lower production than to technically efficient ones. This means, technically efficiency can raise farm productivity without having to increase the number of input costs. Technical efficiency decision is very relevant to be used to increase productivity in the limited economic conditions of the community. In addition, measuring the technical efficiency of the farming will provide important information to decision-makers in formulating agricultural policies [7];[8]. Some researchers have examined the effects of migration on technical efficiency. [9] use the characteristics of stochastic frontier on average output and the level of technical efficiency. [10] investigated the impact of international migration on technical efficiency, allocation of resources and income from agricultural production in Albania. [11] investigated the relationship between labor migration and agricultural production through its technical efficiency. [12] uses a two-stage frontier estimation technique to capture the effects of migration on agricultural efficiency. Although the impact of migration on efficiency has been widely studied, in earlier, migrants bear the costs of migration independently. Meanwhile, in Indonesia, one of the government's regional development strategies is a migration program [13], through a balanced relocation of population based on natural and environmental carrying capacity [14]. The Indonesian government regulates and funds the migration process for middle to lower class citizens and grants certain areas of land to support their lives in the destination village. Migrants use this land for farming [15]. This makes migrants easier to work in the agricultural sector. Even though many commodities make it possible for migrants to be planted, rice as a staple food is an option, either as a main or side livelihood, to support their household food security. This plant is also not an annual plant so that within 90-115 days, farmers can harvest and use it to meet their household needs. This study fills in the existing gaps, how the impact of migration on the technical efficiency of rice farming in people who migrate due to government programs. We use the one step stochastic frontier production function to investigate the impact of migration on the technical efficiency of rice farming in this region. This method is more recommended than the two steps because it can avoid estimation bias [16]. 2. METHODS We chose Rokan Hulu District as a research area because of it one of the destinations for migration that has been implemented by the government. We chose Rambah and Rokan IV Koto sub-district as samples representing the regions and collected data through direct interviews with farmers using a structured questionnaire. This survey collects information about the characteristics and socio-economic of 100 farm households. We have chosen respondents purposely according to the research goals. The number of samples taken is proportional to the http://www.iaeme.com/IJMET/index.asp 1704 editor@iaeme.com
The Impact of Migration on Rice Farming Technical Efficiency in Indonesia rice farmers population in each sub-district, 55 and 45 respondents for Rambah and Rokan IV Koto respectively Data were analyzed using the stochastic frontier production function. Initially, the stochastic frontier model was used by [17] and [18], depended on one side error term which is assumed to be identical and independently distributed. Afterward, [19] used the stochastic frontier model to allow for heterogeneous component errors, depending on differences in farmer characteristics. This model has also been used by [20], [21] and [22]. In this study, the Cobb- Douglas model specification was used in the stochastic frontier production function to estimate coefficient: ln Yi   0  1 ln Landi   2 ln Seedsi   3 ln Labori   4 ln Fertilizeri   5 ln Pesticidei   6 DSAi  vi  ui (1) Yi is the actual production of rice (kg) and i mean farm i (i = 1,2,3, ..., 100), β1-β6 is the parameter to be estimated, Land is the cultivated farmland (ha), Seeds is the amount of use of seeds (kg), Labor is the number of labor use (man-day), Fertilizer is the amount of fertilizer use (kg), Pesticide is the amount of pesticide use (l), DSA is dummy of a seed-aid (using seed-aid from government =1, others=0), v is a random error which is assumed to be independent and identically distributed N (0,  v2 ) and u is a non-negative random variable (the impact of inefficiencies related to production). Since the farming process in the local area is still conducted conventionally, DSA is used to capture technological differences between farmers. The use of the Cobb Douglas model in this study was tested by the likelihood ratio test statistic (λ) compared with the value of the chi-square distribution [23] with the degree of freedom that corresponds to the restriction value. Hypothesis testing with a likelihood ratio statistical test is defined as:   2lnLH0  lnLH1  (2) L(H0) and L(H1) are the value of the likelihood of null and alternative hypothesis for the Cobb Douglas model. The estimation results show that the Cobb Douglas model is fitted with data on rice farming in Rokan Hulu District. Model specifications used to identify the level of technical inefficiency of rice farming households defined as: ui   0  1 Agei   2 Educi   3 Expi   4 HS i   5FCi   6 MLi   7 Migrationi   i (3) Here, ui is the effect of technical inefficiency, δ1-δ7 is the coefficient that affects technical inefficiencies, Age is age (years), Educ is education (years), Exp is the experience of farming (years), HS is the household size, FC is the frequency of counseling (times), ML is the dummy of the main livelihood as rice farming (the main livelihood as rice farming = 1, others = 0), Migration is dummy of the migration (Migrant farmers = 1, indigenous farmers = 0) and 𝜖 is an error term. The technical efficiency of each farm is a comparison between actual production and potential production, at the condition of the level of use of farm inputs. If the actual production is located on the frontier, farming is technically efficient. However, when farming is under the frontier, farming is said to be technically inefficient. Technical efficiency can be defined as: Yi TE i  (4) Yi * Here, Yi* is the potential production, while technical inefficiency is TI i  1  TEi  1  exp ui  . The variance parameters of equations (3) and (4) are expressed by  2   u2   v2 and    u2  2 . γ shows the effect of technical inefficiency where values lie http://www.iaeme.com/IJMET/index.asp 1705 editor@iaeme.com
Defidelwina, Jamhari, Lestari Rahayu Waluyati and Sri Widodo between 0 and 1. t-test was used to test the individual significance of the parameters and test the overall parameters, while to test the effects of the inefficiency model was used the likelihood ratio (LR) test. The null hypothesis: there is no effect of technical inefficiency then H 0 :    0  1  ... 7  0 and the coefficient on the model of the effect of inefficiency is nothing H 0 : 1  ... 7  0 . Production functions and technical inefficiency are estimated by Maximum Likelihood Estimation (MLE) using the Frontier 4.1c program. This program will estimate both functions simultaneously in one step [24]. 3. RESULTS AND DISCUSSION 3.1. Characteristics of Rice Farmers Table 1 presents the characteristics of migrants and indigenous farmers of rice farming in the Rokan Hulu District. Overall, there were no significant differences except in the experience of farming and the dummy of the main livelihood as rice farmers. Based on farming experience, the migrant farmers have longer than indigenous ones with a difference of 14.5 years. Experience is a reasonable proxy for farmers' skill levels and different skill levels may have very different levels of technical efficiency. Based on rice farming as the main livelihood, 21% of the indigenous farmers are more in number than the migrant ones. Table 1. Characteristics of migrants and indigenous farmers of rice farming in Rokan Hulu Regency Farmers Characteristics Migrant Indigenous Age (Years) 53.52 46.75 Education (Years) 6.31 7.75 Farming Experience (Years) 26.35 12.17 Household size (People) 3 4.00 Dummy Rice farming as the main livelihood (%) 0.31 0.52 Others (%) 0.69 0.48 3.2. Production Table 2 presents the use of each input per farm and per ha for migrant and indigenous farmers. There is a fairly high difference between rice productivity of migrant farmers and indigenous ones. Overall local farmers use input more intensively than migrant farmers except for land and fertilizers. The land use of migrant farming is 1.84 times higher than local farmers. The average land used of rice farming in the Rokan Hulu District is quite narrow with 0.268 hectares. According to [25], farmers are included in the small-holder group. This is also in line with the research of [26], which states that the agricultural land used is concentrated between 0.1-0.4 hectares per farm household. The use of fertilizers per ha of the migrant farmers is 22% higher than that of the indigenous ones. The average of this fertilizer used in this area (269.30 kg per ha) is lower than the recommendation for fertilizer use for the Rokan Hulu District (375 kg) [27]. Conversely, local farmers are more intensive in using seeds, labor, and pesticides. The seeds in this study were divided into two categories: aid from government and non-aid. The Number of migrant farmers uses seed aid 5.67 times more in number than the indigenous ones. Seeds are the initial determinant of farming productivity. The use of the correct quality and quantity of seeds can be one way to make farming more efficient [28]. The used of seeds will greatly http://www.iaeme.com/IJMET/index.asp 1706 editor@iaeme.com
The Impact of Migration on Rice Farming Technical Efficiency in Indonesia depend on the power of growing, the possibility of being attacked by pests, diseases, and seasons. The use of labor per ha on indigenous farming is 25% higher than that of migrant ones. The average labor use in this area (74.77 man-days per ha) is slightly higher than conventional rice farming results of the study by [29], which were 69.92 man-days per ha per planting season. On pesticide use, indigenous farmers use 4 times more in amount than migrant ones. The high use of pesticides in indigenous farmers is due to the stages of cultivation carried out using more such as cleaning the grass on land preparation, weeding and controlling pests and plant diseases. Table 2. Use of per farm and per ha inputs of rice farming in Rokan Hulu District Migrant Farmers Indigenous Farmers Pool Variable per farm per ha per farm per ha per farm per ha Production (kg) 913.96 2,619.10 646.10 3,328.50 774.67 2,885.85 Land (ha) 0.35 1.00 0.19 1.00 0.27 1.00 Seed (kg) 9.19 26.33 6.85 35.27 7.97 29.69 Labor (man-days) 23.18 66.43 17.20 88.60 20.07 74.77 Fertilizer (kg) 102.42 293.49 44.48 229.15 72.29 269.30 Pesticide (l) 0.64 1.82 1.78 9.18 1.23 4.59 Farmers Farmers Farmers Dummy % % % (people) (people) (people) Seeds aid used (1= Seeds aid used, 0= 17.00 17.00 3.00 3.00 20.00 20.00 Others) 3.3. Estimating the Stochastic Frontier Production Function and Technical Inefficiency The estimation results of the stochastic frontier production function and technical inefficiency are presented in Table 3. The sigma square value (σ2) of 0.28 is positive and significantly different from zero at a significant level of 1%, indicating that the distribution used is in accordance with the assumptions of the existing distribution with a half-normal distribution. The parameter value γ is related to the variance of the effect of technical inefficiency estimated from the stochastic frontier production function. The gamma variant value is 0.91 and is significant at alpha 1%. This shows that 91% of the error term variation is affected by technical inefficiency and only 9% is caused by noise. MLE value is greater than OLS/ordinary least square (13.69 > -3.86). Thus, it is conclusive that the MLE model is good enough to represent the existing conditions in the field. The likelihood ratio test is 35.09 and this is greater than the table value of χ2 [23] at a significant level of 5% with a restriction value of 9 (16.27). This shows that the Cobb Douglas stochastic frontier production function used can explain the existence of technical inefficiency of farmers in the production process. http://www.iaeme.com/IJMET/index.asp 1707 editor@iaeme.com
Defidelwina, Jamhari, Lestari Rahayu Waluyati and Sri Widodo Table 3. Estimated stochastic frontier production function and rice farming inefficiency in Rokan Hulu District Variables Coefficient Standard error t-ratio Production Function Constant 7.31*** 0.51 14.45 Land 0.61*** 0.10 5.85 Seed 0.06 0.06 0.87 Labor 0.03 0.14 0.19 Fertilizer 0.03** 0.01 2.39 Pesticide -0.03 0.03 -1.07 Dummy of Seed Subsidy -0.03 0.06 -0.44 The Function of Technical Inefficiency Constant -2.88* 1.56 -1.84 Age 0.03** 0.01 2.00 Education 0.15** 0.08 2.02 Farming Experience -0.07** 0.03 -2.20 Household size -0.37* 0.19 -1.93 Frequency of counseling 0.49** 0.24 2.02 Rice farming as the main 0.29** 0.19 1.51 livelihood Migration 1.53** 0.59 2.59 Sigma-squared 0.28*** 0.10 2.64 Gamma 0.91*** 0.04 25.22 Log-likelihood function OLS -3.86 Log-likelihood function MLE 13.69 LR test of the one-sided error 35.09 Description: *p
The Impact of Migration on Rice Farming Technical Efficiency in Indonesia the farmers more technically efficient because it will be easier to allocate the needed labor for the farms. This finding is in line with the research of [37]. The frequency of counseling has a positive and significant coefficient at 5% alpha. This shows that it does not have a positive impact on farming. The frequency of counseling per planting season for indigenous farmers is higher (0.90 times) than that of migrant ones (0.88 times). The average frequency of counseling attended is 0.89 times per planting season. In terms of the main livelihood, it is apparent that farmers whose main livelihood as rice farming have a higher inefficiency level than other ones. This is in line with the research by [38]. Migration variables indicate that indigenous farmers have a better level of efficiency than migrant ones. This is in line with the research by [39]. 3.4. Technical Efficiency Value of Rice Farming The value of the technical efficiency of rice farmers in the Rokan Hulu District is presented in Table 4. The average value of efficiency of farmers is 0.87, which indicates that farmers still have the opportunity to increase production by 13%. The value of farmers' technical efficiency has a wide range of 73% (0.97 - 0.24). Thus, farmers may improve their technical efficiency in the range of 13% - 73%. [22] also find this wide range of levels. The migrant farmers have a lower efficiency value of 5% (0.89 - 0.84) than indigenous ones. The results of this study are in line with the research of [3], where non-migrant technical efficiency (0.68) is higher than intercontinental migrants (0.61) and non-migrant technical efficiency is lower than continental migrants (0.78) Table 4. Frequency distribution of technical efficiency of rice farming Migrant Farmers Indigenous Farmers Pool Interval Frequency Percent Frequency Percent Frequency Percent 0.24 - 0.48 1 2.08 1 1.92 2 2.00 0.49 - 0.73 7 14.58 2 3.85 9 9.00 0.74 - 0.99 40 83.33 49 94.23 89 89.00 Total 48 100.00 52 100.00 100 100.00 Minimum 0.24 0.40 0.24 Maximum 0.97 0.96 0.97 Mean 0.84 0.89 0.87 4. CONCLUSION This study examines the impact of migration on the technical efficiency of rice farming for migrant and indigenous farmers. Overall, indigenous farmers are more technically efficient than migrant ones. In addition, the use of inputs by migrant farmers is lower than that of indigenous ones. Farmers' technical efficiency is wide-ranging with 73%, and an average of 0.87. The average, farmers can still increase their technical efficiency by 13%. Using good labor management, farmers are expected to achieve their potential production. ACKNOWLEDGMENTS We wish to thank the Ministry of Research, Technology and Higher Education of the Republic of Indonesia (Kemenristekdikti RI) in collaboration with Education Fund Management Institute (LPDP) for the financial support of our research. http://www.iaeme.com/IJMET/index.asp 1709 editor@iaeme.com
Defidelwina, Jamhari, Lestari Rahayu Waluyati and Sri Widodo REFERENCES [1] M. I. Khan, G. K. Kundu, B. Mallick, M. M. Islam, and M. S. Akter, “Climatic Impacts and Responses of Migratory and Non-Migratory Fishers of the Padma River, Bangladesh,” Soc. Sci., vol. 7, no. 254, pp. 1–19, 2018. [2] K. Petrou, “Before , it wasn’t like generation of continuity and change in rural – urban migration in Vanuatu,” Asian Pacific Migr. J., vol. 26, no. 1, pp. 31–55, 2017. [3] F. S. Wouterse, “Migration and Technical Efficiency in Cereal Production Evidence from Burkina Faso,” 2008. [4] A. Maharjan, S. Bauer, and B. Knerr, “Migration for Labour and Its Impact on Farm Production in Nepal,” Kathmandu, Nepal, 2013. [5] S. I. Orewa and O.B. Izeko, “Farm Household Technical Efficiency: A Study on Rice Producers in Selected Areas of Jamalpur District in Bangladesh,” Int. J. Dev. Sustain., vol. 1, no. 2, pp. 516–526, 2012. [6] B. T. Anang, S. Bäckman, and A. Rezitis, “Production Technology and Technical Efficiency: Irrigated and Rain-Fed Rice Farms In Northern Ghana,” Eurasian Econ. Rev., vol. 7, pp. 95–113, 2017. [7] O. M.A, A. . Salami, and M. U. S., “Profitability, Inputs Elasticities and Resource-Use Efficiency In Small Scale Cowpea Production In Niger State, Nigeria,” J. Agric. Soc. Res., vol. 8, no. 2, 2008. [8] A. Khan, F. A. Huda, and A. Alam, “Farm household technical efficiency: A study on rice producers in selected areas of Jamalpur district in Bangladesh,” Eur. J. Soc. Sci., vol. 14, no. 2, pp. 262–271, 2010. [9] P. Nonthakot and R. Villano, “Migration and Farm Efficiency : Evidence from Northern Thailand,” in the 52nd Australian Agricultural and Resource Economics Society Conference, 2008, no. February, pp. 1–17. [10] J. Miluka, G. Carletto, B. Davis, and A. Zezza, “The Vanishing Farms? The Impact of International Migration on Albanian Family Farming,” J. Dev. Stud., vol. 46, no. 1, pp. 140– 161, 2010. [11] M. T. Mochelelele and A. Winter-Nelson, “Migrant labor and farm technical efficiency in Lesotho,” World Dev., vol. 28, no. 1, pp. 143–153, 2000. [12] J. Sauer, M. Gorton, and S. Davidova, “Migration and Agricultural Efficiency – Empirical Evidence for Kosovo,” in Agricultural & Applied Economics Association’s, 2010, no. July 27-29, pp. 1–13. [13] Junaidi, E. Rustiadi, S. Sutomo, and B. Juanda, “Pengembangan Penyelenggaraan Transmigrasi Di Era Otonomi Daerah: Kajian Khusus Interaksi Permukiman Transmigrasi Dengan Desa Sekitarnya [Development of Transmigration Organizing in the Era of Regional Autonomy: Case Study of the Interaction of Transmigration Settlements with Surrounding Villages],” J. Visi Publik, vol. 9, no. 1, pp. 522–534, 2012. [14] Law of the Republic of Indonesia, Ketransmigrasian [Transmigration], no. 15. Indonesia, 1997, pp. 1–12. [15] Y. Nova, “Dampak transmigrasi terhadap kehidupan sosial masyarakat: Studi Sejarah Masyarakat Timpeh Dharmasraya [Impact of transmigration on the social life of the community: A Historical Study of the Timpeh Dharmasraya Community],” J. Ilmu Sos. Mamangan, vol. 5, no. 1, pp. 23–36, 2016. [16] H. Wang and P. Schmidt, “One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels,” J. Product. Anal., vol. 18, no. 2, pp. 129–144, 2002. [17] D. Aigner, C. A. K. Lovell, and P. Schmidt, “Formulation And estimation Of Stochastic Frontier Production Function Models,” J. Econom., vol. 6, no. 1, pp. 21–37, 1977. http://www.iaeme.com/IJMET/index.asp 1710 editor@iaeme.com
The Impact of Migration on Rice Farming Technical Efficiency in Indonesia [18] W. Meeusen and J. van den Broeck, “Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error Author ( s ): Wim Meeusen and Julien van Den Broeck Source : International Economic Review , Vol . 18 , No . 2 ( Jun ., 1977 ), pp . 435-444 Published by : Wiley for the Econ,” Int. Econ. Rev. (Philadelphia)., vol. 18, no. 2, pp. 435– 444, 1977. [19] G. E. Battese and T. J. Coelli, “A model for technical inefficiency effects in a stochastic frontier production function for panel data,” Empir. Econ., vol. 20, no. 2, pp. 325–332, Jun. 1995. [20] Suharyanto, J. H. Mulyo, D. H. Darwanto, and S. Widodo, “Analisis Efisiensi Teknis Pengelolaan Tanaman Terpadu (PTT) Padi Sawah Di Provinsi Bali [Technical Analysis of Integrated Crop Management (ICM) of Rice in the Province of Bali],” SEPA, vol. 9, no. 2, pp. 219–230, 2013. [21] B. T. Omonona, O. A. Egbetokun, and A. T. Akanbi, “Farmer Resource - Use and Technical Efficiency in Cowpea Production in Nigeria,” Econ. Anal. Policy, vol. 40, no. 1, pp. 87–95, 2010. [22] R. Villano and E. Fleming, “Technical Inefficiency and Production Risk in Rice Farming: Evidence from Central Luzon Philippines*,” Asian Econ. J., vol. 20, no. 1, pp. 29–46, Mar. 2006. [23] D. A. Kodde and F. C. Palm, “Wald Criteria for Jointly Testing Equality and Inequality Restrictions,” Econometrica, vol. 54, no. 5, pp. 1243–1248, Mar. 1986. [24] Tim Coelli, “A Guide to Frontier Version 4.1: A Computer Program for Stochastic Frontier Production and Cost Function Estimation.,” Australia, 1996. [25] Central Bureau of Statistics, “Laporan Hasil Sensus Pertanian 2013 [Report of the Agricultural Census 2013],” Jakarta, 2013. [26] S. H. Susilowati and M. Maulana, “Luas Lahan Usaha Tani dan Kesejateraan Petani: Eksistensi Petani Gurem dan Urgensi Kebijakan Reforma Agraria [Land Farming and Farmers’ welfare: The Existence of Small-Holder Farmers and the Urgency of Agrarian Reform Policy],” Anal. Kebijak. Pertan., vol. 10, no. 1, p. 17, Aug. 2016. [27] Ministry of Agriculture, “Acuan Penetapan Rekomendasi Pupuk N,P, dan K pada Lahan Sawah Spesifik Lokasi (per kecamatan) [Reference to Determination of N, P, and K Fertilizer Recommendations in Specific Location of Rice Fields (per sub-district)],” Jakarta, 2007. [28] Atman, “Teknologi budidaya padi sawah varietas unggul baru Batang Piaman [The cultivation technology of new high-yield variety (Batang Piaman) for lowland rice.],” J. Ilm. Tambua, vol. VI, no. 1, pp. 58–64, 2007. [29] E. L. Tinubaya, B. Sigit Priyono, and W. Rasyid, “Analisis Komparasi Usahatani Padi Sawah Sistem Tanam Sri Dan Konvensional Di Desa Bukit Peninjauan I Kecamatan Sukaraja Kabupaten Seluma [Comparative Analysis of Paddy Farming Between Rice Intensification and Conventional System in Bukit Peninjauan I District of Sukaraja Regency of Seluma],” AGRISEP, vol. 10, no. 2, pp. 188–206, 2011. [30] K. H. Koirala, A. Mishra, and S. Mohanty, “Impact of land ownership on productivity and efficiency of rice farmers: The case of the Philippines,” Land use policy, vol. 50, pp. 371– 378, Jan. 2016. [31] A. Ebers, T. T. Nguyen, and U. Grote, “Production efficiency of rice farms in Thailand and Cambodia: a comparative analysis of Ubon Ratchathani and Stung Treng provinces,” Paddy Water Environ., vol. 15, no. 1, pp. 79–92, Jan. 2017. [32] A. Bhattacharyya and R. Mandal, “A generalized stochastic production frontier analysis of technical efficiency of rice farming A case study from Assam, India,” Indian Growth Dev. Rev., vol. 9, no. 2, pp. 114–128, 2016. http://www.iaeme.com/IJMET/index.asp 1711 editor@iaeme.com
Defidelwina, Jamhari, Lestari Rahayu Waluyati and Sri Widodo [33] I. N. Widyantari, Jamhari, L. R. Waluyati, and J. H. Mulyo, “Does the tribe affect technical efficiency ? Case study of local farmer rice farming in Merauke,” Int. J. Mech. Eng. Technol., vol. 9, no. 11, pp. 37–47, 2018. [34] Z. Yang, A. Mugera, and F. Zhang, “Investigating yield variability and inefficiency in rice production: A case study in Central China,” Sustainability, vol. 8, no. 8, pp. 1–11, 2016. [35] W. Alwarritzi, T. Nanseki, and Y. Chomei, “Analysis of the Factors Influencing the Technical Efficiency among Oil Palm Smallholder Farmers in Indonesia,” Procedia Environ. Sci., vol. 28, no. SustaiN 2014, pp. 630–638, 2015. [36] A. Roy and F. Hamid, “Efficiency Measurement of Rice Producers in South-West Region of Bangladesh,” J. Humanit. Soc. Sci., vol. 19, no. 7, pp. 145–153, 2014. [37] M. J. Ogada, D. Muchai, G. Mwabu, and M. Mathenge, “Technical efficiency of Kenya’s smallholder food crop farmers : do environmental factors matter ?,” Env. Dev Sustain, no. 16, pp. 1065–1076, 2014. [38] M. Arif and A. W. Mugera, “Journal of Hydrology : Regional Studies Econometric estimation of groundwater irrigation efficiency of cotton cultivation farms in Pakistan,” J. Hydrol. Reg. Stud., vol. 4, pp. 193–211, 2015. [39] O. A. Sunday, R. Adeyemo, A. S. Bamire, and S. O. Binuomote, “Impacts of migration on agricultural productivity in Osun state , Nigeria,” Agriculture, vol. 64, pp. 18766–18770, 2013. http://www.iaeme.com/IJMET/index.asp 1712 editor@iaeme.com