Assam’s economy continues to be predominantly agrarian, with more than 55 percent of its workforce engaged in agriculture and allied activities (Government of Assam, 2023; Ministry of Statistics and Programme Implementation, 2022). Despite favourable agroclimatic conditions, fertile alluvial soils and abundant rainfall, agricultural productivity remains relatively low, largely due to recurrent flooding, fragmented landholdings, traditional cultivation practices and inadequate rural infrastructure (Mandal, 2010; Government of Assam, 2023). Flood-related production and income risks remain important in Assam’s agricultural economy, with farm-level evidence showing substantial financial losses and the need for effective mitigation strategies (Borah and Buragohain, 2025).
       
Agricultural diversification has been increasingly recognized as a key strategy for improving farm incomes and enhancing livelihood security in rural areas. Horticultural crops including fruits, vegetables and spices have gained considerable importance due to their relatively higher value per unit area and growing domestic and export market demand (Birthal et al., 2007; Choudhary, 2013; Rao et al., 2006). For states like Assam, where a significant share of agriculture is rainfed and dominated by smallholders, expansion of horticulture presents a critical opportunity for income enhancement and risk diversification (Bezbaruah and Mandal, 2013; Bhattacharya, 2008).
       
Assam possesses considerable horticultural potential due to its diverse agroecological conditions and climatic suitability for a wide array of crops such as banana, pineapple, citrus fruits, potato, onion, turmeric and chilies. According to official estimates, horticultural crops account for a notable share of the cropped area and contribute significantly to state agricultural production (Government of Assam, 2023). However, despite this potential, the sector continues to face structural and institutional challenges including high postharvest losses, weak supply chains, limited market access and climatic vulnerability (NABCONS, 2022; Nuthalapati et al., 2022; Jha et al., 2015).
       
At a broader level, reducing post-harvest losses and improving supply chain efficiency have been identified as important strategies for enhancing food security and farm incomes across India (FAO, 2019; Rais and Sheoran, 2015; National Centre for Cold Chain Development, 2015). In Assam, these challenges are particularly pronounced due to infrastructural gaps and geographical constraints. Recent studies have highlighted the importance of cold chain infrastructure and market linkage improvements for sustaining horticultural growth in the northeastern region (Patowary, 2023; Nuthalapati and Sharma, 2021; Minten et al., 2014). Additionally, Pingali and Rosegrant (1995) and Wainright (1994) have demonstrated that agricultural commercialisation and export diversification through horticulture can significantly improve farm incomes when structural constraints are adequately addressed.
       
Although existing literature has examined agricultural diversification and horticultural development in India, there is a relative scarcity of empirical studies focusing specifically on growth dynamics and production determinants of horticultural crops in Assam using time-series data. Bhuyan and Kotoky (2023) documented growth and instability in Assam’s horticulture sector, while time-series approaches have also been applied to other major agricultural commodities in the state, including tea (Debnath et al., 2026). However, relatively few studies have systematically compared horticultural growth across distinct time phases while assessing the relative associations of cultivated area and yield with production. To address this research gap, the present study analyses growth dynamics and production determinants of horticultural crops in Assam over the period 2005-06 to 2022-23, guided by the following specific objectives:
• To analyse Growth trends in area, production and productivity of horticultural crops in Assam.
• To compare Phase-wise performance of fruits, vegetables and spices.
• To assess the relative contribution of area and yield to horticultural production.

Data sources and study period
 
The study is based on secondary time-series data covering the period 2005-06 to 2022-23 (18 years). Data on area (lakh hectares), production (lakh metric tonnes) and yield (kg per hectare) for major horticultural crops in Assam were compiled from official sources, including the Economic Survey of Assam (various issues), the Directorate of Horticulture, Government of Assam and publications of the Ministry of Statistics and Programme Implementation (MOSPI), Government of India. The study focuses on three major crop groups, i.e., fruits, vegetables and spices, as well as selected individual crops, namely banana, pineapple, potato, onion, turmeric and chillies. The annual time-series data for the aggregate crop groups are reported in Appendix Table A1, while the selected fruit and vegetable crop series used in the crop-specific analysis are reported in Appendix Table A2 and A3.






 
Analytical framework
 
The study employs a combination of growth analysis and regression techniques to examine the dynamics and determinants of horticultural production.
 
Compound growth rate
 
Growth trends in area, production and yield were estimated using an exponential growth function:
 

Y_n  = a · b^t,

 
Which in logarithmic form is expressed as:
 

Y_n  = ln a + t · ln b.

 
The compound growth rate (CGR) is computed as:
 

CGR = (b - 1) × 100

 
Where,
Y_n = Value of the variable in year t.
a = Intercept.
b = Estimated regression coefficient and CGR is expressed as a percentage per annum.
       
The coefficient of determination (R²) and t-statistic were used to assess the statistical significance of estimated growth rates.
        
The study period was divided into two phases of equal length to enable systematic comparison of growth performance. Phase I (2005-06 to 2013-14) broadly corresponds to a period of initial policy push under the Horticulture Mission for North East and Himalayan States (HMNEH), while Phase II (2014-15 to 2022-23) aligns with subsequent national and state-level agricultural policy shifts, including restructuring under the Mission for Integrated Development of Horticulture (MIDH) from 2014-15 onward. This phase structure allows for examination of whether changes in the policy environment were associated with shifts in growth trajectories.
 
Regression models
 
To assess the determinants of horticultural production, separate simple linear regression models were estimated using production as the dependent variable, with area and yield as explanatory variables:
 

Area effect model:   P^t = α + β₁A^t + ε^t
 
Yield effect model:  P^t = α + β₂Y^t + ε^t

 
Where,
P^t = Production.
A^t = area.
Y^t = Yield in year t.
α = Intercept,
β₁ and β₂ = Coefficients to be estimated.
ε^t = Stochastic error term.
       
The study employs simple (bivariate) regression models rather than multiple regression or more advanced econometric techniques for the following reasons. First, the primary objective is to assess the individual and comparative influence of area and yield on production, which is best captured through separate bivariate models. Second, area and yield are algebraically related to production (Production = Area × Yield/constant), making their simultaneous inclusion in a multiple regression model prone to multicollinearity. Third, the relatively small sample size (N = 18) imposes constraints on the degrees of freedom available for estimating multiple parameters reliably. It is acknowledged that other important determinants such as rainfall, irrigation coverage, fertilizer use and technology adoption influence production but were excluded due to data unavailability at the disaggregated crop level for the entire study period.
       
The standard assumptions of ordinary least squares (OLS) regression are noted: (i) linearity of the relationship between dependent and independent variables; (ii) zero mean of the error term; (iii) homoscedasticity of error variance; (iv) no autocorrelation in error terms; and (v) no perfect multicollinearity. Given the time-series nature of the data and small sample, results should be interpreted in the context of these assumptions and readers are cautioned that high R² values in time-series models may partly reflect spurious correlation rather than genuine causal relationships (Granger and Newbold, 1974). Model performance was evaluated using R² and t-statistics.
 
Limitations of the study
 
The analysis is subject to certain limitations. The relatively small sample size (N = 18) limits the statistical robustness of timeseries regression estimates. The models are specified with a restricted set of explanatory variables (area and yield) and do not explicitly account for other determinants such as climatic variability, input use and technological adoption. Additionally, the absence of unit root testing means that non stationarity of individual time series cannot be formally ruled out, which is a recognized limitation when interpreting regression outputs from time-series data (Goswami, 2016).

Overview of horticultural production in Assam
 
The horticulture sector in Assam has demonstrated steady expansion over the study period in terms of both cultivated area and aggregate output. The annual trends in area, production and yield of fruits, spices and vegetables in Assam during 2005-06 to 2022-23 are presented in Table A1. By 2022-23, total horticultural area reached approximately 7.64 lakh hectares, with total production amounting to about 94.95 lakh metric tonnes (Government of Assam, 2023). This growth reflects the increasing integration of horticulture within the state’s agricultural economy.
       
However, growth has not been uniform across crop groups. Fruits and spices exhibit relatively stable increases in area and production, whereas vegetables display greater variability, particularly in the latter years of the study period. These fluctuations point to differing growth dynamics and potential structural constraints affecting specific crop categories. The differential performance underlines the need for a disaggregated, phase-wise analysis, as undertaken in the following sections.
 
Phase-wise growth trends
 
To examine temporal variations in horticultural performance, the study period was divided into Phase I (2005-06 to 2013-14) and Phase II (2014-15 to 2022-23). The compound growth rates (CGR) of area, production and yield are presented in Table 1.


       
The results reveal a clear structural shift in horticultural growth trajectories across the two phases. During Phase I, growth was broad-based, with vegetables exhibiting the highest area expansion (5.10%) and positive production growth (3.77%), indicating that cultivation area played a central role in driving output during this period. Fruits also posted strong production growth (4.50%) supported by area expansion (2.57%). Spices showed modest but positive growth in both area (2.10%) and production (2.50%). These patterns are broadly consistent with findings of Deokar and Shetty (2014), who documented area-led agricultural growth in India during the mid-2000s.
       
Phase II reveals a marked structural slowdown, most prominently in vegetables, which registered negative growth in both area (-3.01%) and production (-0.09%). This contraction is consistent with Bhuyan and Kotoky (2023), who identified instability in horticultural production in Assam. It also accords with broader evidence from Eastern India showing marked inter-state and crop-wise variation in vegetable growth and instability (Gupta et al., 2024) and with reviews documenting substantial spatio-temporal heterogeneity in vegetable area, production and productivity (Sethi et al., 2024). The declining vegetable area in Phase II may be attributed to multiple interacting factors. Vegetables are inherently perishable crops with limited shelf life, making them vulnerable to price volatility and post-harvest losses in the absence of adequate cold storage and transportation infrastructure (NABCONS, 2022; National Centre for Cold Chain Development, 2015). Recurrent floods in Assam, which have become more frequent and severe due to climatic variability, disproportionately affect vegetable cultivation owing to its shorter crop duration and higher sensitivity to waterlogging (Mandal, 2010; FAO, 2019). Market uncertainties driven by price fluctuations and limited access to remunerative markets further reduce farmers’ incentive to allocate area to vegetables. These structural and climatic factors collectively explain the observed contraction in vegetable area during Phase II and call for targeted risk-mitigation measures in the sub-sector.
       
In contrast, spices exhibited relatively strong production growth (4.10%) in Phase II despite limited area expansion (0.70%), suggesting that factors beyond mere area extension such as improved crop management practices, selection of higher-yielding varieties and stronger market demand contributed to output growth. This finding aligns with Patowary (2023) and Rao et al., (2006), who observed that crop-specific market signals and productivity improvements are important drivers of horticultural diversification. The observed divergence in spice performance also reflects a potential shift in farmer behaviour toward crops perceived as more economically resilient under market uncertainty.
       
Yield dynamics further reinforce these observations. Fruits maintained stable yield growth across both phases (approximately 1.82-1.89%), suggesting consistent productivity improvements. Spices experienced a decline in yield growth from Phase I (4.10%) to Phase II (0.46%), indicating that production gains may not have been driven by productivity improvements alone. Conversely, vegetables recorded an increase in yield growth (4.64%) in Phase II, but this did not translate into production gains due to declining cultivated area, highlighting a structural disconnect between productivity improvements and area allocation in this crop group.
 
Area-production relationship: Regression analysis
 
The relationship between cultivated area and production was examined through simple linear regression. Results are presented in Table 2. The annual area, production and yield series for the selected fruit crops (banana and pineapple) and vegetables crops (Potato and Onions) in Assam during 2005-06 to 2022-23 used in the specific regression analysis are presented in Table A2 and A3.


       
The regression results indicate that area is positively and significantly associated with production across most crops, though the strength of this association varies considerably. Strong area-production relationships are observed for total fruits (R² = 0.820) and total spices (R² = 0.829), indicating that expansion in cultivated area explains a substantial share of production variation. Pineapple (R² = 0.659) and turmeric (R² = 0.811) also show relatively strong associations. The coefficient for total fruits (β = 24.68) implies that, on average, each additional lakh hectare of fruit area is associated with an increase of approximately 24.68 lakh metric tonnes in fruit production, holding other factors constant.
       
In contrast, banana (R² = 0.155) and onion (R² = 0.200) exhibit weaker area-production relationships. For banana, the statistically non-significant coefficient (t = 1.72) suggests that area alone is a poor predictor of banana production, which may reflect the importance of yield-determining inputs such as irrigation and variety-specific management (Birthal et al., 2007). For onion, the weaker association (t = 1.90, p<0.05) reflects the crop’s vulnerability to demand-side shocks and price-induced area volatility (Minten et al., 2014). For vegetables in aggregate, the moderate explanatory power (R² = 0.504) confirms that area contributes to production but does not fully account for its variability. These results must be interpreted with caution, as high R² values in time-series regressions may partly reflect spurious correlation arising from shared trends rather than genuine structural relationships (Granger and Newbold, 1974).
 
Yield-production relationship: Regression analysis
 
The influence of yield on production is examined through a separate set of regression models. Results are presented in Table 3.


       
The yield-based regression models demonstrate substantially higher explanatory power compared to area-based models across nearly all crops. Spice crops, i.e., chillies (R² = 0.970) and turmeric (R² = 0.969) exhibit very high yield-production associations, indicating that output variations in these crops are closely aligned with changes in per-hectare productivity. The coefficient for chilies (β = 0.030) implies that each unit increase in yield (kg/ha) is associated with an increase of 0.030 lakh metric tonnes in production, representing an economically significant relationship given the relatively small area under chilli cultivation in the state.
       
Fruit crops, i.e., banana (R² = 0.953) and pineapple (R² = 0.882), also exhibit strong yield-production relationships, corroborating the importance of productivity enhancement in sustaining output growth. The very high R² for onion (R² = 0.998) suggests that production variations in this crop are almost entirely explained by yield changes, reflecting the sensitivity of onion output to agronomic practices and input use intensity (Minten et al., 2014; Rais and Sheoran, 2015).
       
However, these results must be interpreted cautiously. Very high R² values in time-series models, particularly with small samples are susceptible to spurious correlation and trend-driven associations rather than genuine structural linkages (Granger and Newbold, 1974). Moreover, yield itself is not an exogenous variable; it is determined by a combination of climatic conditions, input use, varietal technology and management practices. The models therefore capture statistical associations rather than causal pathways.
       
Comparatively, yield-based models consistently outperform area-based models in terms of explanatory power across all crop groups. This finding suggests that productivity improvement, rather than area expansion, is the primary driver of horticultural production variation in Assam, a result consistent with findings from other Indian states reported by Birthal et al., (2007), Rao et al., (2006) and Deokar and Shetty (2014). From a policy perspective, this implies that investments in improved seed varieties, irrigation, pest management and agricultural extension services are likely to yield greater returns in terms of production growth than strategies centered purely on area expansion (Pingali and Rosegrant, 1995; Jha et al., 2009).
 
Synthesis and policy implications
 
The combined evidence from the CGR analysis and regression results yields several important insights for horticultural policy in Assam. First, the horticulture sector is undergoing a structural transition from area-led growth (Phase I) toward productivity-led growth (Phase II), though this transition remains incomplete and uneven across crop groups. Second, crop-specific heterogeneity in growth performance underlines the inadequacy of uniform policy approaches and the need for targeted crop-level interventions. Third, the performance of the vegetable sub-sector in Phase II raises concerns about climatic vulnerability, post-harvest losses and market access that need urgent policy attention. These concerns are consistent with evidence on vegetable production instability in Eastern India (Gupta et al., 2024) and flood-related agricultural losses in Assam (Borah and Buragohain, 2025).
       
These findings are broadly consistent with the emerging literature on Indian horticulture. Bhuyan and Kotoky (2023) identified instability in horticultural production in Assam and recommended strengthening support systems, while (Sethi et al., 2024) highlighted the spatial and temporal variability of vegetable area, production and productivity in India. Bezbaruah and Mandal (2013) highlighted the importance of diversification as a risk-management tool in flood-prone regions and Patowary (2023) demonstrated the role of cold-storage infrastructure in improving price realisation for farmers. The wider sustainability context is also important: assessment of farming systems in Northeast India using a Pressure-State-Response framework emphasizes the multidimensional interaction of environmental pressures, farming conditions and institutional responses (Debnath et al., 2025). The present study reinforces these insights within a phase-wise analytical framework that explicitly distinguishes area and yield effects. Evidence on post-harvest loss and cold-chain gaps further supports investments in storage, transportation, processing and market connectivity as enabling conditions for sustained horticultural growth (FAO, 2019; NABCONS, 2022; National Centre for Cold Chain Development, 2015).

The study reveals a gradual shift in Assam’s horticultural sector from area-led expansion towards productivity-oriented growth during 2005-06 to 2022-23, although the pattern varies across crop groups. Fruits and spices showed relatively stable or improving performance, while vegetables experienced contraction in area and production during Phase II. Regression results indicate that yield has a stronger statistical association with production than cultivated area across most crops, highlighting the growing importance of productivity enhancement. These findings underscore the need for crop-specific interventions focusing on improved varieties, irrigation, extension services, post-harvest infrastructure and market linkages. Given the limited time-series observations and restricted explanatory variables, the results should be interpreted as statistical associations rather than causal relationships. Future research incorporating climatic, technological and market-related factors could provide a more comprehensive understanding of horticultural production dynamics in Assam.

The authors would like to acknowledge Mizoram University, Aizawl for providing institutional and academic support for this work. The authors also extend sincere thanks to the reviewer for valuable and constructive suggestions that have substantially improved the quality of the manuscript.
 
Disclaimer
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided but do not accept liability for any direct or indirect losses resulting from the use of this content.

The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.