Tóm tắt Luận án Determining the optimal farm size in agricultural production of households Mekong delta

techniques with the relevant technology level (Li et al., 2013) and this is also the index not affected by the price of inputs as well as the price of output products. Therefore, the thesis uses TFP criteria to determine the optimal farm size threshold to maximize HQHDSX, at the same time, the thesis still analyzes the other four criteria as a basis to prove the assertion that “each of these indicators is not the best measure the efficiency of production activities”. 2.1.2 Theoretical basis of the influence of farm size on the efficiency of production activities Wickramaarachchi and Weerahewa (2018) productivity is defined as the ability of an input unit to produce a given output unit. Agricultural productivity shows the level of efficiency of households in using a specific input with a certain level of technology. The inverse relationship between farm size and the efficiency of production activities plays an important role in many regions at different times and this relationship was first discovered in agricultural production in Russia by Chayanov (1926), then inherited and developed widely in the 1960s and 1970s (Sen, 1962; Bardhan, 1973). Sen (1962) the small farms in India, which obtaining higher the efficiency of production activities as they apply more inputs (especially family labor). Berry and Cline (1979) also demonstrated a relationship similar to Sen in other developing countries and Deolalikar (1981) argued that this relationship was only true in traditional agriculture. This relationship became a hotly debated topic between agricultural economists and development economists (Carter, 1984; Feder, 1985; Benjamin, 1995). Imperfections in the market of inputs also contribute to the formation of a strong inverse relationship between farm size and the efficiency of production activities. First, the analysis of data from fifteen developing countries, Cornia (1985) shows that systematic output per unit of agricultural land decreases as the farm size of increase by labor is more abundant and cheaper for small farm. Head of households' knowledge of land and local climatic conditions accumulated over generations contributes to an advantage over hiring workers (Rosenzweign and Wolpin, 1985). The advantage of supervision and knowledge of small farm will compensate for difficulties in accessing capital and formal insurance in rural markets (Feder, 1985). An inverse relationship between fram size and the efficiency of production activities is caused by the imperfections of credit and labor markets when 8 combined with fixed costs of production (Eswaran and Kotwal, 1986). Imperfection land and insurance motivate smallholders to use more family labor to reduce the potential adverse effects of price fluctuations (Barrett, 1996). Assuncao and Ghatak (2003) demonstrated an inverse relationship after controlling households' heterogeneity of skills. Thapa (2007) also discovered this relationship in Nepal because it used more labor and cash than large farms. Ansoms et al. (2008) found a strong inverse relationship between farm size and the efficiency of production activities Rwanda due to the scarcity of land that forced households to over-exploit their resources in the case of main household income from agricultural production. The research also found that increases in non-farm wages and technological advances will affect the exchange rate, management capacity, presence and level of market imperfections. It is these factors that will form the inverse relationship between farm size and HQHDSX (Otsuka, 2013). However, a number of other studies also explain the existence of inverse relationships due to the omission of other factors that affect the HQHDSX such as knowledge and technical understanding as well as socio-economic environmental issues. In which households must make decisions (Kalirajan, 1990) and based on previous researches, two of the socio-economic environmental indicators (include education and income other than agriculture) have been selected to measure the relationship between farm size and HQHDSX (Bravo – Ureta and Pinheiro, 1997), differences between households (Assuncao and Ghatak, 2003), land fragmentation (Wu et al., 2005), differences in soil quality (Benjamin, 1992; Lamb, 2003; Assuncao and Braido, 2007), soil characteristics and sand content (Barrett et al., 2010) and the other factors, at the same time omitting the different definitions that show the HQHDSX. Therefore, Li et al. (2013), Wickramaarach and Weerahewa (2018) added exogenous variables to control the influence of these factors on the efficiency of rice production activities of households. However, the level of the impact of the inverse relationship between farm size and HQHDSX tends to decline over time (Deininger and Byerlee, 2012; Deininger et al., 2015) due to the emergence of imperfect labor markets and technological change. On the contrary, some researches have demonstrated a positive relationship between farm size and the efficiency of production activities, meaning that large farm are more effective than small farm. The emergence of a Green Revolution has increased the role of capital and knowledge, which has led to the emergence of large farm achieving a higher level of HQHDSX in districts suitable for new technologies (Deolalikar, 1981). Recent innovations in plant breeding, tillage and information technology help households easier monitor labor, thus increasing the efficiency of production activites in traditional agriculture at Eastern Europe and South America (Helfand and Levine, 2004; Lissitsa and Odening, 2005). And this positive 9 relationship was also discovered in Nigeria due to the high quality inputs used by large farm of the households (Obasi, 2007), in Japan that having relatively well factor markets (Kawasaki, 2010) and China due to technological development and technological transformation (Chen et al., 2011). Mixed results obtained by Rahman and Rahman (2009) suggest that a positive relationship between HQHDSX and farm size occurs in advanced technology areas and the inverse relationship still exists in developing regions. Tamel (2011) in the US agriculture sector showed that in some areas, farm size and the efficiency of production activities is positive but in others may have a positive relationship (Kawasaki, 2010; Ali and Deininger, 2015; Lu et al., 2018) or the negative relationship (Paul and Githinji, 2017) with farm size depending on the fragmentation process. Hence, the inverse relationship is a local phenomenon rather than an indispensable law in production. The researches not only stop at a simple relationship (negative or positive relationsgip) but also show a nonlinear relationship (U-shaped or ∩-shaped) between farm size and HQHDSX. First, Mahmood and Nadeem-ul-haque (1981) have demonstrated the U-shaped nonlinear relationship between farm size and HQHDSX when estimating the inputs (farm size, square farm size) with output. Inheriting that achievement, researchers Byiringiro and Reardon (1996), Heltberg (1998b) have added the characteristics of soil and region characteristics, followed by Helfand and Levine (2004) and Ali and Deininger (2015) developed Heltberg's model on the basis of adding soil characteristics. However, Dorward (1999), Kimhi (2006), Barrett et al. (2010), Ali and Deininger (2015), Nkonde et al. (2015), Henderson (2015), Anseeuw et al. (2016), Wickramaarachchi and Weerahewa (2018) argue that there is an inverted U-shaped nonlinear relationship between farm size and HQHDSX through different models from simple (only farm size variables and squared farm size) to the complete model of information and characteristics of the head of household, land characteristics and quality, the ability to manage and care for rice fields, ... all show this relationship. 2.1.3 Theoretical backgrounds of optimal farm size According to economic theory and economics of agricultural production theory, Debertin (2002) demonstrated the optimal input threshold to maximize the output through the first derivative calculation method based on specific inputs. According to Greene (2003), consider finding the x where f(x) is maximized or minimized. Because f’(x) is the slope of f(x), either optimum must occur where 𝑓ᇱ(𝑥) = 0. Otherwise, the function will be increasing or decreasing at x. This result implies the first-order or necessary condition for an optimum maximum or minimum is ௗ௬ ௗ௫ = 0. Hence, to maximize or minimize a function of several variables, the first- 10 order conditions are డ௙(௫) డ௫ = 0 According to microeconomic theory, when the smaller farm, the higher the average cost will increase, and the larger farm expands, the lower the average cost will decrease, until a certain farm size (or the maximum farm size) the average cost will be the minimum and if the optimal farm size is exceeded, the average cost will increase with increasing farm size, which results in the opposite of the production function, implies obtain the largest average yield for the optimal farm size. According to Wickramaarachchi and Weerahewa (2018), the optimal farm size is the farm size at which the HQHDSX is maximized. Because when farm size is still small, if farm size continues to expand, the effectiveness will increase and achieve the highest efficiency at the optimal farm size threshold. At this farm size threshold, if farm size continues to expand, the efficiency decreases and the optimal farm size threshold is determined by ఉభ ଶఉమ . 2.2 Overview of references 2.2.1 The research of the effects of farm size on the efficiency of production activities 2.2.1.1 Effect of farm size on land productivity The inverse relationship is discussed and discovered through theory and experiment on many countries around the world (Mazumdar, 1965; Bharadwaj, 1974; Khan, 1977; Chaddha, 1978; Berry and Cline, 1979; Carter, 1984 ; Cornia, 1985; Feder, 1985; Bhalla and Roy, 1988; Chattopadhyay and Sengupta, 1997; Heltberg, 1998a&b; Assuncao and Ghatak, 2003; Fan and Chan-Kang, 2005; Barrett et al., 2010; Chen et al., 2011; Sial et al., 2012; Carletto et al., 2013; Holden and Fisher, 2013; Ali and Deininger, 2015; Desiere and Jolliffe, 2017) but with a focus on India (Sen, 1962; Bardhan, 1973; Ghose, 1979; Newell et al., 1997; Assuncao and Braido, 2007; Gaurav and Mishra, 2015). However, there are also many studies disagreeing with the above hypothesis and based on empirical evidence that have provided the opposite opinion, that large farm households will be more effective than households with small farm (Rao, 1966; Srivastave et al., 1973; Heltberg, 1998a&b; Khan, 1979; Khan and Maki, 1979; Rao and Chotigeat, 1981; Kevane, 1996; Akram-Lodhi, 2001; Van Hung and et al., 2007; Truong Hong Vo Tuan Kiet and Hua Tuan Tai, 2013; Akudugu, 2016). Thus, farm size can have an impact on land productivity in two dimensions, showing economies of scale and non-economies of scale. Studies (Mahmood and Nadeem-ul-haque, 1981; Byiringiro and Reardon, 1996; Heltberg, 1998b; Ali and Deininger, 2015) have demonstrated a U-shaped nonlinear relationship between farm size and land productivity. However, Dorward (1999), Barrett et al. (2010), Ali 11 and Deininger (2015), Nkonde et al. (2015), Henderson (2015), Anseeuw et al (2016), Wickramaarachchi and Weerahewa, 2018 suggest that having an inverted U- shaped nonlinear relationship between farm size and land productivity. 2.2.1.2 The effect of farm size on labor productivity The efficiency of production activities is measured by labor productivity that is not as commonly researched as land productivity but has been researched in recent years and shows a positive relationship between farm size and labor productivity (Lamb, 2003; Li et al., 2013; Adamopoulos and Restuccia, 2014). Researchers Byiringiro and Reardon (1996), Nkonde et al. (2015), Wickramaarachchi and Weerahewa (2018) also found an inverted U-shaped nonlinear relationship between farm size and labor productivity based on energy estimation labor productivity with different explanatory variables such as farm size, square farm size, variables showing the characteristics of the household head, the characteristics of the land, and differences in the residence area. 2.2.1.3 Effect of farm size on capital efficiency Although there are very few studies on this relationship, it still shows a clear relationship like other HQHDSX. First, Li et al. (2013), Wickramaarachchi and Weerahewa (2018) used capital efficiency measure to measure HQHDSX and show the positive relationship between farm size and capital efficiency. However, Nkonde et al. (2015) measured the capital efficiency use through cost efficiency and found an inverted U-shaped nonlinear relationship between farm size and capital efficiency in all three cases from single, semi-complete to complete variables. 2.2.1.4 The effect of farm size on economic efficiency Many researches have demonstrated an inverse relationship between farm size and economic efficiency (Lau and Yotopoulos, 1971; Tadesse and Krishnamoorthy, 1997; Bagi, 1982; Townsend et al., 1998; Xu and Jeffrey, 1998; Gorton and Davidova, 2004; Manjunatha et al., 2013). In contrast, Hall and Leveen (1978), Lund and Hill (1979), Hoque (1988), Kalaitzandonakes et al. (1992), Sharma et al. (1999), Alvarez and Arias (2004), Rios and Shively (2005), Tipi et al. (2009), Nguyen Huu Dang (2012) have demonstrated the positive relationship between farm size and production efficiency. Researchers not only stop in the linear relationship between farm size and production efficiency but also research and make judgments about the existence of nonlinear relationship between farm size and production efficiency. The U-shaped relationship between farm size and production efficiency is shown through the varius research of (Helfand and Levine, 2004). In contrast, Hoque (1988), Nguyen Tien Dung and Le Khuong Ninh (2015), Nguyen Tien Dung (2015) have demonstrated the inverted U-shaped nonlinear relationship between farm size and 12 production efficiency. 2.2.1.5 The effect of farm size on the total factor productivity Although the relationship between farm size and TFP is not as deeply concerned as the relationship between farm size and land productivity. However, it still shows that there may be a linear relationship (negative or positive relationship) between TFP and farm size or nonlinear relationship through some empirical researches. First, Van Zyl et al. (1996), Li et al. (2013), Gautam and Ahmed (2018) found an inverse relationship between farm size and TFP. In contrast, other researches have found a positive relationship between farm size and TFP through experiments in Czech Republic (Hughes, 1998), in Slovakia (Hughes, 2000), in Vietnam (Dinh Bao, 2014) and in Australia (Sheng and Chacellor, 2018). The research also found nonlinear relationship between farm size and TFP in two different forms. The U-shaped nonlinear relationship between farm size and TFP through the research of Nkonde et al. (2015) have proved that the inverted U- shaped nonlinear relationship between farm size and TFP. 2.2.1.6 The effect of farm size on the efficiency of production activities As just stated, most researcges only use a single measure of HQHDSX, in which land productivity is commonly used in many researches to explore the relationship between farm size and HQHDSX. Other ways of measuring HQHDSX such as labor productivity, capital efficiency, technical efficiency and TFP are rarely used. In recent years, a comprehensive measurement of the HQHDSX through various measurement aspects (using 3-5 measurements) was conducted by Li et al. (2013), Nkonde et al. (2015), Wickramaarachchi and Weerahewa (2018) have demonstrated a different relationship (linear as negative or positive relationship, U-shaped or inverted U-shaped nonlinear relationship) between farm size and HQHDSX depending on the measurement specifically the efficiency of production activities. 2.2.2 Researches on optimal farm size Many researches have demonstrated the optimal farm size threshold to maximize the efficiency of production activities according to one of five different measurements in the same data set (researches from 3-5 measures representing the HQHDSX) or different data set (single research a measure of HQHDSX). On the basis of the first derivative or ఉభ ଶఉమ based on the estimation results of the model of factors affecting on HQHDSX (Hoque, 1988; Hassanpour, 2013; Nguyen Tien Dung, 2015; Nkonde et al., 2015; Wickramaarachchi and Weerahewa, 2018) 2.3 Research methods 2.3.1 Research framework Labor Productivity Optimal farm size by NSLD Land Productivity Optimal farm size by NSDAT 13 Source:Research and design Hình 2.1 Proposes research framework 2.3.2 Data collection The study selected three provinces in the Mekong Delta with the same characteristics of the land with large farm rice cultivation of An Giang, Dong Thap and an average of Can Tho. The study collected randomly 498 rice-producing households in the Autumn-Winter 2016, Winter-Spring 2017 and Summer 2017 seasons, of which An Giang (225 households), Can Tho (90 households) and Dong Thap (183 households). 2.3.3 Data analysis Objective 1: Research using descriptive statistical methods Objective 2: Research to use 2 ways: - A two-step estimation method for four ways of measuring the efficiency of production activities including land productivity, labor productivity, capital efficiency and TFP. - An one-step estimation method for economic efficiency measure. Objective 3: Use necessary conditions and calculation formula of Greene (2003), Wickramaarachchi và Weerahewa (2018): 𝜕𝐻𝑄𝐻𝐷𝑆𝑋(𝑄𝑀𝐷𝐴𝑇) 𝜕𝑄𝑀𝐷𝐴𝑇 = 0 => 𝑄𝑀𝐷𝐴𝑇 = 𝛽ଵ 2𝛽ଶ (2.7) Objective 4: rely on the achieved results to propose the most effective solutions 2.4 Estimation model of farm size impacts on the efficiency of rice production activities of Mekong Delta households The general model measures the impact of farm size on the efficiency of production activities through various aspects as follows: 14 𝐻𝑄𝐻𝐷𝑆𝑋௜௝௞ = 𝛽଴ + 𝛽ଵ𝑄𝑀𝐷𝐴𝑇௜௝ + 𝛽ଶ𝑄𝑀𝐷𝐴𝑇𝑆𝑄௜௝ + 𝛽ଷ𝑄𝑀𝐿𝐷௜௝ + 𝛽ସ𝑁𝑈𝐶𝐻௜௝ + 𝛽ହ𝑇𝐷𝐻𝑉𝐶𝐻௜௝ + 𝛽଺𝑇𝑁𝐾𝐻𝐴𝐶௜௝ + 𝛽଻𝑆𝑂𝑀𝐴𝑁𝐻௜௝ + 𝛽଼𝐿𝐷𝑇𝐻𝑈𝐸௜௝ + 𝛽ଽ𝐿𝐷𝐺𝐷௜௝ + 𝛽ଵ଴𝐴𝑁𝐺𝐼𝐴𝑁𝐺௜௝ + 𝛽ଵଵ𝐷𝑂𝑁𝐺𝑇𝐻𝐴𝑃௜௝ + 𝛽ଵଶ𝑇𝑉𝑂𝑁௜௝ + 𝛽ଵଷ𝑇𝐻𝐴𝑀𝑁𝐼𝐸𝑁௜௝ + 𝛽ଵସ𝐾𝐶𝑅𝑈𝑂𝑁𝐺௜௝ + 𝛽ଵହ𝑇𝐴𝑃𝐻𝑈𝐴𝑁௜௝ + 𝜀௜௝ (2.8) where: HQHDSXk is the efficiency of production activities measured by different aspects, QMDAT is the farm size of rice cultivation on the largest field (ha), QMDATSQ is the square of farm size of the household, QMLD is the number of working-age members of the family involved in rice production (number of employees), NUCH is the dummy variable representing the gender of the head of household (= 1 if the head of household is female and = 0 otherwise) , TDHVCH is the educational level of the head of household (number of classes), TNKHAC is the household's non-rice income (million VND /year), SOMANH is the number of rice plots of the household (the number of plots), LDTHUE is the total labor days hired to work in rice fields (days/ha), LDGD is the total number of family labor days working on rice fields (days/crop), ANGIANG (= 1 if the household lives in An Giang and = 0 if in other province), and DONGTHAP (= 1 if the household lives in Dong Thap and = 0 if in another province), TVON is the total cost of the inputs (including family labor) (million VND/crop), THAMNIEN is the number of years of rice farming experience of the head of the household (year), KCRUONG is the distance from the household to the largest field (km), TAPHUAN (= 1 if the head of the household participated in training courses in the last 3 years and = 0 if otherwise), i indicate the number of i rice the households and j showing the number of j crops. 15 CHAPTER 3. OVERVIEW OF RESEARCH AREAS This chapter presents an overview of the Mekong Delta as well as of the provinces surveyed mainly regarding farm size. 3.1 Land resources in the Mekong Delta Land area in the survey area is concentrated on alluvial soil. This soil group has high fertility and balance, favorable for agricultural production, especially for rice, coconut, sugarcane, pineapple and fruit trees. 3.2 Current status of rice production 3.2.1 Farm size in the Mekong Delta * Farm size of agricultural production Number of agricultural households in the Mekong Delta in general and the three researched provinces in particular concentrated mainly on the farm size of 0.5 - 2 ha, accounting for 40.36%, followed by the scale of 0.2 - 0.5 ha accounts for 25.13% and the remaining is allocated at other ones. Table 3.1 Number of households using agricultural land in the Mekong Delta by farm size Unit: Household Location 2 ha Total An Giang 37.887 36.339 68.427 27.395 170.048 Can Tho 20.942 26.850 43.264 11.151 102.207 Dong Thap 49.341 53.713 89.269 24.086 216.409 Mekong Delta 509.795 598.932 961.914 312.455 2.383.335 Nguồn:Tổng điều tra nông thôn, nông nghiệp và thủy sản Việt Nam năm 2016 * Farm size of rice cultivation by locality Source:General Statistics Office 2017 Figure 3.1 Scale of paddy land in the Mekong Delta by location Farm size of rice cultivation is concentrated in An Giang, followed by Dong Thap and at least Can Tho. This result forms the number of households surveyed in the provinces in the study area. * Farm size of rice cultivation by scale The total number of rice-growing households in the Mekong Delta accounts for 19.12% of the total number of rice-growing households nationwide and concentrates on the scale of 0.5 - 2 ha. The number of rice-growing households in Dong Thap is higher than the other two provinces and when divided by farming size, 0 2000 4000 6000 2010 2011 2012 2013 2014 2015 2016 2017 ĐBSCL Đồng Tháp An Giang Cần Thơ 16 the farming scale of the people of 3 provinces of An Giang, Can Tho and Dong Thap is still similar to households in the Mekong Delta, which means that most people cultivate on a scale of 0.5 - 2 ha, accounting for about 40%, from 0.2 to 0.5 ha, accounting for 25% and under 0.5 ha accounts for about 22% and the scale of over 2 ha accounts for about 13%. This implies that people in the surveyed area are cultivating on a small farm and fragmented so not really bring about optimal the efficiency of production activities. Source: Vietnam Rural, Agriculture and Fishery Census 2016 Figure 3.2 Structure of households using rice land in the Mekong Delta by scale 3.2.2 Results of rice production in the research area Source: General Statistics Office 2017 Figure 3.3 Rice production in the Mekong Delta 2010 - 2017 Rice production in the three provinces of An Giang, Dong Thap and Can Tho followed the increasing trend over time for the whole period but began to decline slightly in 2016, of which the highest output of An Giang and Can Tho was the lowest. Can Tho has the lowest output but the growth rate is quite high when compared to 2010 and 2017 at about 16%, reaching the highest output of 1,408 million tons in 2015. For An Giang and Dong Thap due to the large output, the downward trend in the following years is more evident although the output increased during the period of more than 6% and 14% for each province. To conclude, the output of the provinces has increased but started to decline slightly in the following years and slower than the area so the provinces have lower productivity in the following years. 0.00 20.00 40.00 60.00 2 ha An Giang Cần Thơ Đồng Tháp ĐBSCL 0.00 2,000.00 4,000.00 6,000.00 2010 2011 2012 2013 2014 2015 2016 Sơ bộ 2017 Đồng Tháp An Giang Cần Thơ 17 CHAPTER 4. RESEARCH RESULTS AND DISCUSSION This chapter presents and discusses the results of each study. From there, proposing solutions for effective use of farm size contributes to improving the efficiency of production activities, improving incomes and raising the living standards of households in the Mekong Delta. 4.1 Actual situation of rice production in Mekong Delta households 4.1.1 Land Table 4.1 Actual situation of farmland Soil tyle Households (m 2/household) Average (m2/person) Average (%) Bình quân Tỷ lệ (%) Residential 755,63 3,93 172,52 3,93 Agricultural 18.401,43 95,82 4.201,24 95,82 Aquaculture