A STUDY ON THE GROWTH AND DETERMINANTS OF AGRICULTURAL PRODUCTION IN MIZORAM

Purpose : This study attempts to evaluate the trends in the area, production and productivity of selected crops from 2001 to 2011. It also analyses the determinants of production of selected crops. Theoretical framework: 55.17 per cent of the primary workforce is employed in agriculture and related industries (Census, 2011). Although agriculture's contribution to GSVA is declining over the year, agriculture and allied sectors continue to be the primary source of income for rural communities, so policymakers and planners must pay careful attention to the sector. Design/methodology/approach: Secondary data such as statistical abstract of Mizoram (Various issues) was used to collect data and analyzed quantitatively using multiple regression to ascertain the determinants of selected crops and also Compound annual growth rate evaluate the trends in the area, production and productivity of selected crops grown in Mizoram from 2001 to 2011. Findings: The findings indicated that the area and production of selected agricultural crops have a positive CAGR over the past two decades, while the productivity of turmeric, orange, banana, and grape have a negative CAGR. The regression analysis revealed that increase or decrease in production is strongly influenced by land area and yield. Research, Practical & Social implications: The study would be beneficial and applicable for decision-makers, planners, and civil society organisations for developing effective strategies for starting up future development plans.


INTRODUCTION
Agriculture is the foundation of the Indian economy and is crucial to the nation. It is the main source of revenue for a substantial percentage of the population. According to the 2011 Indian Census, it employs around 60 per cent of the Indian labour force. It supports to general economic development and poverty reduction by supplying mankind with essential components, work opportunities for the vast majority of the people, and industrialization's raw materials (Lekhi and Singh, 2013). While agriculture accounting for just 20.19 per cent of GDP, more than 70 per cent rural families rely on it as their primary source of income. (Ministry of Statistics and Programme Implementation, 2021).
The economy of the Indian state of Mizoram is primarily based on agriculture. Shifting cultivation, an age-old practise, is still the dominant mode of agriculture in the state. According to the 2011 census, 55.17 per cent of principal workers are employed in agriculture and allied sectors of the economy. But since agriculture and allied industries remain the primary source of income for the rural community, policymakers and planners must pay close attention to the sector (Mizoram Economic Survey, 2021-22). The output of agricultural activities in terms of money made, however, is notably underwhelming.
Consistent institutional and policy support for agricultural growth, which is sustained, could portend expansion of the rural economy. In fact, agriculture at the national level displayed impressive growth during the 1980s. Its slowdown in the 1990s was attributed to unfair economic reforms, ineffective extension services, and decreased or stagnant public spending on agricultural infrastructure (Balakrishnan 2000;Hirashima 2000;Mahendradev 1987;Rao 2003).
The key objectives of the study are as follows: 1) To evaluate the area, production, and productivity trends of selected crops in Mizoram from 2001 to 2021. 2) To examine the production-determining factors in Mizoram. Lee and Hsu (2009) examined the relationship between public investment in agriculture (public investment/land) and agricultural land productivity (output/land) in Taiwan using timeseries data for Taiwan's agricultural sector. The government's public investment in the agricultural sector served as a proxy variable for nonfarm current inputs, in addition to the original labour and capital input variables typically considered. The cointegration test reveals that public investment in agriculture and the productivity of agricultural land exhibit a significant positive relationship over the long term, with an elasticity of 0.55 between land productivity and public investment in agriculture. Mukhopadhyay and Sarkar (2001) used the structural break of modern time series specification technique to test for acceleration in food grains production in West Bengal. They discovered that there is a negative effect on the level of food grains production in West Bengal, beginning in 1982Bengal, beginning in -1983. They also discovered that the underlying series is a Different Stationary (DS) series with drift, implying that there is no deterministic trend in the level of food cereal production. However, their analysis is founded on the entire economy of West Bengal. District-to-district differences in land capacity, climate, fertilizer use, irrigated area, etc. contribute to a great deal of variation in West Bengal's agricultural output. Consequently, one may not obtain a homogenous growth rate across all districts. Virmani (2005) utilized growth regression analyses and introduce dummy for the period of 1965-6 to 1979-80. Also tested were dummies with commencing years of 1963-1964, etc. All of these fools are rendered insignificant. Whereas other studies have implied breakdowns in the 1970s, he discovered that surrogate variables for prospective breaks in 1971-1972 and 1975-1976 are even less significant than previously thought. Once the 1980-81 gaps are accounted for, there are no statistically significant pauses in GDP growth from 1951-1952to 1979-1980. Ghose and Pal (2007 calculate the interdistrict disparity in the development of food grain production in West Bengal by employing both exogenous and endogenous structural break analysis to assess for acceleration in food grain production. In the case of an exogenous structural break, the impact of liberalization policies was analyzed using 1991-1992 as the break point. There is no evidence of acceleration or deceleration in the level of food cereal production after 1991-92 23 with the exception of the district Malda, where there is a statistically significant 5% increase in the level of the series. Bhattacharyya and Bhattacharyya (2007) analyzed the expansion of West Bengal's agrarian economy from 1980-1981to 2002-2003. In 1992-1993, which marked the beginning of the liberalisation era for the Indian economy, they discovered a significant negative trend break. Chand and Parappurathu (2012) analyzed the trends in agricultural productivity at the national and state levels and attempted to identify the main factors underlying the varying performance of agriculture during various time periods and states. The results of structural breaks indicate that trend growth rates for the seven phases corresponding to the six break points identified were calculated and found to be 0.70 percent, 1.93 percent, 2.26 percent, 2.34 percent, 3.21 percent, 2.31 percent, and 3.13 percent for the periods 1960-61 to 1968-69, 1968-69 to 1975-76, 1975-76 to 1982-83, 1982-83 to 1988-89, 1995-96 to 2004-05, and 2004-05 to 2010-11, respectively. As agricultural GDP increased in a similar manner during the third and fourth phases 24 with commensurate trend growth rates, it was reasonable to consider them as a single phase. Matsuyama (1992) examined the role of agricultural productivity in economic development in a two-sector model of endogenous growth in which (a) preferences are nonhomothetic and the income elasticity of demand for the agricultural good is less than unity, and (b) the engine of growth in the manufacturing sector is learning-by-doing.

METHODOLOGY
This study employs secondary data only. Secondary data were acquired from a variety of published and unpublished government sources, including the Economics Survey, Statistical abstracts, agriculture department publications, manuals, etc. At the same time, the relevant department was contacted for policy manuals and government initiatives. In addition, individual researcher papers and other internet sources were explored. The selection of Birdeye Chilly, Turmeric, Ginger, Orange, Banana, and Grape for this research is based on their importance as subsistence and commercial crops. For analysis, secondary data on area, production, and productivity were utilized. Using time series data from 2001-02 to 2020-21, the trend in area, production, and productivity has been determined.
In this research, the collected data were analyzed using multiple linear regression (MLR) and Compound Annual Growth Rate (CAGR).
Multiple regression is used to estimate the factors affecting the production of several agricultural products in Mizoram. Area and productivity are the explanatory factors while production is the dependent variable. Multiple regression model specifications are as follows: Following model was used to calculate the compound annual growth rate (CAGR) of area, production, and productivity of selected crops.

RESULTS AND DISCUSSIONS
Agriculture's performance in Mizoram from 2001-02 to 2020-21 ushered in an era of dynamism in which improved technology became the order of the day in a set of economic policies. Numerous commodities saw enormous increases in production and productivity as a result of social, economic, institutional, and environmental reforms.
It is seen from the table no. 1 that the area and production of Orange in Mizoram indicates an increasing trends over the years from 5,482 Ha, 32,099 MT in 2001-02 to 16,566 Ha, 54,170 MT during 2020-21 respectively while the productivity diminished from 6 Kg/Ha in 2001-02 to 3 Kg/Ha in 2020-21. The productivity of orange was affected by bamboo flowering that was occurred during 2007-08. The area and production of Orange depicts a growth rate at 6 per cent, 3 per cent over the study period while the productivity grew a negative of -3 per cent over 20 years.
The   It is seen from the table no. 2 that the area, production and productivity of Birdeye Chilly, Turmeric and Ginger have increased over the study period except productivity of Turmeric. The area, production and productivity of Birdeye Chilly raised from 590 Ha, 401 MT, 0.68 Kg/Ha in 2001-02 to 11,196 Ha, 10,918 MT, 0.97 Kg/Ha in 2020-21 with a remarkable compound annual growth rate at 17 per cent, 19 per cent, 2 per cent respectively in the state. Likewise, the area, production and yield of Ginger have increased from 7,287 Ha, 46,648 MT, 6.4 Kg/Ha in 2001-02 to 8553 Ha, 60,131 MT, 7.03 Kg/Ha in 2020-21 respectively, highlighting a slow annual compound growth rate at 1 per cent both in area and production while the compound annual growth rate of yield of Ginger remaining the same over the study periods.
On the other hand, the area and production of Turmeric have increased from 280 Ha, 2,808 MT in 2001-02 to 7,738 Ha, 29,823 MT in 2020-21, depicting a notable annual compound growth rate of 19 per cent, 13 per cent respectively over the last 20 years. However, the yield of Turmeric has fall from 10 Kg/Ha in 2001-02 to 3.85 Kg/Ha during 2020-21 with a negative growth rate at -5 per cent over the years.
The downward trends in the area, production and productivity of Birdeye Chilly, Turmeric and Ginger during 2007-08 was due to the prevalence of famine in the state, while the peak levels were reached in the following year i.e., 2008-09 since the government had taken several initiatives to combat famine and launched a scheme known as BAFFACOS and implemented the New Technology Mission programme in Mizoram.
The study attempts to evaluate the impact by using simple regression analysis to analyse the stability or instability of production in all selected agricultural crops in Mizoram. In order to attain comparability, production of a specific crop is used as a dependent variable, and area and productivity are used as independent variables. The study calculated the production's growth rate over the corresponding time period. It is evident from the above table no. 3 that the coefficient of multiple regression for the production of Orange in Mizoram is 0.897, which reflects the level of association between actual and anticipated values. As the anticipated values are derived from a linear combination of area and productivity, the value of coefficient 0.897 shows that the association between production and the two variables that are independent is consistent and positive. The R-square (R 2 ) that is Coefficient of Determination assesses the goodness of fit of the estimated Sample Regression Plane (SRP) in terms of the percentage of the variation of the response variables that is described by the sample regression equation. Hence, the R 2 value of 0.804 indicates that about 80.4 per cent of the variance in production is described by the estimated SRP, which employs area and productivity as independent variables, and that the R 2 value is significant at the 1 per cent level. Keeping all other factors fixed, the area coefficient of 2.86 shows the proportional impact of area on production. The calculated positive sign indicates that this impact is favourable, with production increasing by 2.86 for every unit increase in area, and the value of the coefficient is significant at 1 per cent. The coefficient of productivity is 5603.08, which measures the yield's impact on production when all other factors are held constant. The calculated positive sign indicates that this impact is positive, with a 5603.08 rise in production for every 1 per cent improvement in productivity, and the value of coefficient is significant at the 1 per cent level.
The regression coefficient for Banana is 0.876, indicating a significant association between production and the two independent variables. The coefficient of area indicates that production would improve by 10.14 per unit of increase in area, and the value of coefficient is significant at the level of 1 per cent. Although the production would increase by 1,603.08 units for each unit improvement in productivity, the productivity coefficient is insignificant. The R 2 results revealed that 76.7 per cent of the variation in banana produce is explained by the yield and area.
It can also be observed that the coefficient value of Grape is 0.927 which represents the strong relationship between production and two predictors. The estimated positive effect of area and yield infers that production would rise by 10.69 and 573.98 for every unit increase in area and productivity respectively. The value of the coefficient of both area and productivity are significant at the level of 1 per cent. The R 2 value of 0.860 indicates that about 86 per cent variation in Grape production is substantially impacted by explanatory factors.
It is evident from the table that the regression coefficient of Birdeye Chilly (0.840) shows a strong relationship between production and the two explanatory variables. The positive sign of the partial impact of area on output implies that for each unit increase in area, output rises by 1.98, and the coefficient is statistically significant at the 1 per cent level. Productivity has a partly positive influence, with output of Birdeye Chilly rising by 4163.22 for each unit upsurge in yield, and the value of this coefficient is also significant at 1 per cent. The R 2 value of 0.705 indicates that explanatory variables have a strong influence on production variation.
There is a significant connection between production and two explanatory variables which is shown by the regression coefficient value of Turmeric i.e., 0.978. The coefficient of area exhibits a positive value, indicating that for each unit increase in area, Turmeric output will increase by 5.77 per cent, and the coefficient is statistically significant at 1 per cent level.
The productivity values indicate that with every unit increase in Turmeric yield, output would expand by 545.97, and the coefficient is significant statistically at 5 per cent level. The R 2 value depicts that about 95.6 per cent variation in production is expounded by the two explanatory variables.
Moreover, the regression coefficient of Ginger is 0.969 that indicates the strong relationship between response variable and explanatory variables. The positive sign of coefficient of area and yield depicts that for every unit increase in area and productivity, the production would rise by 5.90 and 4609 respectively. The R 2 value of 0.938 indicates that area and productivity explain 93.8 per cent of the variation in ginger output in Mizoram.

CONCLUSION
This study showed that Mizoram has enormous potential for cultivating a wide range of crop species due to favourable landscape and climatic conditions. The current study on trend of area and production of selected agricultural crops grown in Mizoram have a positive compound annual growth rate while the productivity of Turmeric, Orange, Banana, and Grape have a negative CAGR over the last twenty year. The regression analysis of all crops under the study indicated that increase or decrease in production is highly influenced by the area and productivity.
Investment in technology, higher capital formation, the opening of better market channels, improved processing sector efficiency, better cropping systems, improved extension activities, adequate focus on post-harvest management, pest and disease control, and encouraging scientific methods shall significantly contribute to the development of agriculture. There will also be an increase in the productivity of agriculture as a result of these measures.
A comprehensive policy measure addressing crop farming's long-term development should be framed and carried out in the state. The production of cash crops, which includes fruits, vegetables, and spices, should be given preferential treatment. In addition, it is essential to prohibit shifting agriculture, which is detrimental to the environment and economically unfeasible.
Furthermore, since the vast majority of farmers in the nation, especially in Mizoram, are poor and marginalized farmers, the majority of whom are uneducated, it is difficult for them to practise improved agricultural practises on their own. Therefore, organising farmers into groups