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The technological change of the mid-sixties was considered a penance toward the growing food crises in the country. The growth of the agriculture sector is a key strategy for sustainable development as it has the potential not only to improve the food security of the nation but also can resolve many other challenges such as growing unemployment, poverty, and inequality in society. It will improve food security issues of a household via nutritive food, and supplementary income for the family. So, a series of institutional reforms were revamped to induce the growth of the agricultural sector under the recommendation of Shri L.K Jha was a milestone to achieve the planned growth through price incentives for the betterment of consumers as well as producers. The instruments of Minimum Support Price (MSP), food subsidy, and input subsidy have played a significant role in the growth of agriculture via the growth of food security (Acharya, 1997). Effective implementation of minimum support prices has helped in improving the production and productivity of agriculture. Non-price factors such as improved seed assured irrigation are significant determinants of robust growth of rice in Punjab (Ali, et al., 2012). Price support through buffer stock is used as a policy option used by many governments of developed and developing countries for addressing the low and unstable income of farmers (Deuss, 2015; PU & Zheng, 2018). Price support is an important tool used across the country to help farmers against loss of income due to output price fluctuation (Aditya et al.,2016 & Allen, 2016). Output price support is a critical tool for enhancing the income of the household. The study also supports that age, gender, access to the market, use of extension services, and transport and packing costs are significant drivers of the participation of smallholder farmers in buffer stock operations (Abokyi et al., 2020). The initial emphasis of the agricultural price commission was on reducing the fluctuation in food grain prices and providing price incentives to the producer for sustainable crop production. So, the need of revisiting minimum support prices and multi-dimensional alternatives for more sustainable growth of this sector is continuously getting attention among policymakers as adoption of new technology, rational use of land and resources, and crop diversification. Traditionally, agricultural diversification is considered to be a subsistence kind of farming with the objective of an increase in the security of household food and income security but in the recent decade, it is referred to as a remedial measure to revamp several issues at the micro and macro level. It is a ray of hope for small and marginal farmers for their economic upliftment. It is supposed to enhance farm income as a solution to the adverse impact of mono-cropping on soil whereas, at the micro level, it is a panacea to self-sufficiency. It is a stress-relieving activity for economic growth for the farming community. It refers to the addition of new crops or cropping systems to agricultural production or is viewed as a crop substitution for the area suffering from some soil-related problems like salinity, solidity, and acidity. In short, it can be looked at as an option for increase of income for small farm holding, less risk for price fluctuation, climate variability, balancing food demand, and reducing dependency on farm inputs (Khanam et al., 2018). Historically diversification is constituted as the opposite of concentration or specialization but in the recent era increase in area under high-value commodities of exportable commodities such as fruit and vegetable is a criterion of diversification. At the national level diversification is supposed to be a solution to boosting farm income, food security, poverty alleviation, and employment generation whereas at the regional level it is a prominent solution to regional imbalances and negative externalities added with the mono-cropping culture prevailing in agriculture (Jha et al., 2007). Diversification of cropping culture is one of the potential strategies for sustaining agriculture productivity and reducing agriculture risk across the globe. (Komboi et al., 2020). It is a transitional move from subsistence agriculture to the commercialization of the cropping system (Rehima et al. 2013) and yields stable and quality-driven nutrition diversity and food security for a nation ( Mango et al. 2018). It is also a remedial measure to enhance the farm income of a household, optimal use of resources, and efficient land utilization for small and marginal farmers (Behera et al. 2007; Meha, 2005). Akber et al. (2017) pinpointed the gradual transition of crop diversification in Southwest Coastal Bangladesh and showed that diversification in farming is a remedial measure to overcome the increasing socio-ecological issues in farming. Diversification of agriculture is reckoned a high weightage option to resolve the adverse ecologically impact of monoculture practices followed for a long back. It can be considered as a tool for the advancement of household income from the farm, employment generation, poverty alleviation, and reduction of risk involved in farming (Braun, Pingali, and Rosegrant, 1995; Chand, 1996; Ryan and Spencer, 2012). Diversification is termed as a transition of resources from mono-cropping to a multi-cropping culture of cultivation with optimum and best allocation of resources or it is a movement of the shift of resources from the production of low-value commodities such as grain and cereals to high-value commodities like fruit, vegetable, etc. (Hayami and Otsuka 1992; Vyas, 1996; Delgada and Siamwalla,1999). Crop diversification in the place of monoculture farming has been reported as a potential solution for sustainable adoption of ecological issues of the farm as an increase in salinity in soil and water ( Bunting, Kundu, and Ahmed, 2017; Islam et al., 2019; Johnson et al., 2016) In this backdrop, the present analysis is an effort to examine the relationship between crop diversification index and responsible factors as credit flow, road density, per capita income, size of operational land holdings, gross irrigated area, market intensity, regulated market, gross cropped area, net sown area, the structure of landholding of Haryana economy for the period 2009-10 to 2020-21.

A review of literature in research is a powerful tool that provides some insight into already done work in terms of variables used, concepts, and methodology analysis. It also puts light on research gaps where the researcher explores a new dimension of work. So, a present literature review is a synoptic display of various studies done on national, international, and regional analysis of crop diversification. Diversity in the crops always meant to spur the productivity of agriculture and for the better living standard opportunities of less developed countries people (Papademetriou and Dent, 2001). It has positive effects on soil, environmental damage, increment in the food basket of farmers, and diversified food (Caviglia and Sills; 2003, Kurosaki, 2003; Yao, 1997).  It is supposed to be a tool to enhance farmer income, alleviate poverty, soil conservation, and efficient use of limited resources of households (Joshi et al. 2004). Diversification in crops has a positive correlation between the number of crops cultivated, household income from crops, and diversity in the diet in transition economies (Pellgrini and Tasciotti, 2014). A diversified cropping pattern has proved a more viable and sustainable solution against mono cropping culture with more farm income and less crop failure risk (Faruque et al. 2017; Kabir et al. 2020). Crop diversification is always considered as a potent solution for an agricultural crisis like water and soil salinity due to specialization in cropping patterns (Bhodal and Vatta, 2021). Although crop diversification is a viable solution for agricultural sustainability through groundwater conservation, revitalization of soil, productivity improvement, efficient use of resources, ecological gain, and employment generation but still a majority of the farmer are reluctant to adopt it.

Institutional and Structural Change in Agriculture and Crop Diversification

Farm size and diversification has a negative relationship and the studies reveal that large farm is more specialized due to the risk associated with diversification. Wealthier farmers are capable to bear the risk (White and Irwing, 1972).  Farm size is a significant determinant of crop diversification (Gupta et al., 1985) and Singh et al. (1985) also explored the negative association between farm size and diversification. Similarly  another study (Windle and Rofle, 2005) also explored that land holding size along with the level of education of farmers emerged as a significant factor for crop diversification in central Queensland of Australia. A study by Jha et al. (2007) shed light on the factors responsible for diversification at the national, state, and regional levels with various measures of diversification and found that size of land holding is indifferent to diversification at the national level whereas at the regional level it is inversely related. The positive impact of small and marginal farmers on crop diversification has been highlighted by the research work of De and Chattopadhyay (2010). Pop & Precott (2022) focused on the relationship between farm size and other socio-economic variables in agricultural diversification in California and found a strong and significant negative correlation between farm size and crop diversification.

H₀: institutional and structural changes are independent to crop diversification in agriculture.

Infrastructural and Technical Advancement in Agriculture and Crop Diversification

Crop diversification is a significant measure of risk reduction in agriculture and found that irrigation intensity is an important determinant of crop diversification with other significant variable as cropping pattern, amount of credit, and education of household head. The intensity of irrigation is also an influential factor for diversification in agriculture (Walker, 1983) and has been negatively correlated to crop diversification as better facilities of irrigation reduce the uncertainty and risk to some extent in agriculture.  Crop diversification is also influenced by agro-climate conditions like the quality of soil, irrigation facilities, annual rainfall, and access to modern technology (Gupta and Tewari, 1985). The research of Valera et al. (1989) also explained seasonal rainfall, availability of irrigation, and limited irrigation management are among the core determinants of crop diversification in agriculture. Diversification in crop production is triggered by fast technical improvement, rapid infrastructural development, and diversity of food demand (Pingali and Rosegrant, 1995). Better irrigation facilities enhance the feasibility of diversified crops (Kurosaki, 2003). Crop diversification is positively linked with irrigation intensity, market access, and better road infrastructure development (Ashok et al., 2006). Research by Islam and Rahman (2012) has also explored the positive impact of better irrigation facilities, and the use of modern agro-technology on diversity in crop production in Bangladesh. Pitipunya (2005) shed some light on the importance of training programs and extension services in crop diversification. Boosting extension services can play an important role in more diversification in agriculture (Kemboi et al., 2020) via access to credit and irrigation up gradation facilities.

H₀:  Infrastructural development and technical investment have no relationship with crop diversification in agriculture

The present study is based on panel data analysis of 21 cross section information (District of Haryana State) on 10 indicators in terms of structural, instructional change, infrastructural development and technical advancement in agriculture for the year 2009-10 to 2021-21. The impact of institutional and structural changes in agriculture has been considered with the variables as average size of land holding, credit flow (loan advancement by PACS (in lakh), number of regulated market whereas technical advancement and infrastructural development has been carried out by the variables like road density, fertilizer consumption, number of tractors, average rainfall, gross cropped area irrigated, irrigation intensity index and number of tube well and pumping set. These have been compiled from various issues of Economic Survey, various issues of Statistical Abstract of Haryana, published by Economic and Statistical Organisation (ESO, Panchkula). Due to lack of information the district Charkhi Dadri has been excluded from analysis. The generalized Least Square (GLS) method with pooled model, fixed effect model and random effect model has been used to trace the impact of various considered drivers of diversification in agriculture. The GLS has the advantage of alleviation of heteroskedasticity effect due to the cross-section nature of the information used in analysis and autocorrelation emerged due to time series data. The model is specified as follows. CD(HI/MEI)it = β0t+β1ASOHit+β2FCit+β3GCAIit+β4ARit+β5NRMit+β6RLit+β7NOTit+β8IIit+β9TAPSit+ β10CFit+uit Where CD (HI/MEI) stands for Herfindal and Modified Entropy index of crop diversification ASOH- Average size of land holding in hectare; FC- Fertilizer Consumption; GCAI- Gross crop irrigated area; AR- Annual Rainfall (mm); NRM-Number of Regulated Market; RLM- Road Length Maintained by PWD(km); NOT- Number of Tractors; III- Irrigation Intensity Index; TAPS- Tube well and pumping set; CF- Credit Flow( Loan Advance by Primarily Agricultural Cooperative Societies. Uit- Disturbance term satisfying all OLS assumptions

Table 1 displays the  parameter estimates of pooled data for pooled  analysis techniques for  both Herfindal index and  Modified Entropy Index of crop diversification  for the period 2009-10 to 2020- 21 and found that  the model is good fit as p value is less than common  alpha value (0.05) for  the all techniques. Analysis also shows that average size of land holding is significant factor for crop diversification and negatively correlated with diversification means household owning small size of land in average followed more diversification in crop production.  

With this, analysis also reveals that the road length maintained by PWD and number of tube well and pump sets are also significant and positively  correlated with crop diversification whereas gross crop area irrigated, irrigation intensity index are also significant but negatively  correlated with diversification for  Herfindhal index model.

 Further, the number of regulated market, average size of land holding and road length maintained by PWD   appear as a significant driver for crop diversification for Modified Entropy Index approach. In short, it can be observed from the above analysis that average size of land holding, irrigation facilities, and road infrastructure development is key drivers for crop diversification but the major issues with this model are that it does not capture the heterogeneity or individuality among various regions or considers the intercept term of all individual is same. So, we have used other techniques of panel analysis.

 Table 2  put a shed on more appropriate techniques (fixed  effect model) of panel analysis  and the analysis displays that still model is good fit for both chosen techniques of crop diversification as p value is less than 0.05 for common α value. Still average size of land holding, irrigation intensity index,  gross crop area irrigated are significant factors  but negatively related to crop diversification for both  techniques whereas number of regulated market is significant but positively correlated with  Modified Entropy index  of diversification.

Random effect model of panel analysis is also a good fit for present analysis and reveals the same drivers as gross cropped area irrigated, average size of land holding, irrigation intensity index are significant but negatively correlated with crop diversification whereas number of regulated market is positively correlated with  the entropy index of crop diversification.

Table 4 reported the parameter estimates of Hausman test for appropriateness of fixed and random effect of the model and found that probability value is very high for both model and more than common alpha value 0.05. So, we have no reason to accept the null hypothesis and conclude the random effect model is more appropriate for present analysis.

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