Influence of the Farm Harvest Prices on Acreage and Income From Sugarcane Crop in Haryana State of India

Motivation: The purpose behind incorporating this research question is to figure out how the growth rates of area, prices, yield, production, and income have contributed to the accomplishment of the objectives set forth for the further development of the agriculture sector. It is normally presumed that the efforts to raise incentives made by the government bring positive results. The analysis of the general trend of the growth of these variables becomes imperative as these mirror the level of successful execution of price policy in any economy. Moreover, the response of those concerned determines the degree to which a particular policy is successful.

Motivation: In order to make an economically prudent interpretation, the boost in income increase alone is inadequate. Multiple factors can influence the sources of the growth. The income of the farmers can be augmented by supplying them either a price hike or by improving the yield of the crop through efficient use of the resources at their disposal. Thus, it becomes an important inquiry that requires being resolved with statistical evidence for the purpose of evaluating the realistic expansion of the sector in the state. Whether the income increase of farmers simply comes either because of a hike in prices or a favorable reaction in the shape of higher per-hectare yield is the central issue that needs to be answered.

Motivation: The measurement of the exact degree of correlation gives better understanding of the economic behavior of the variables and is also helpful in identifying the critical values that are significant for the purpose of making policies. The affirmation of fair and stable price is considered prominent for increasing area, yield, output, and income at farms. But the performance of such efforts depends on the degree of relationship these variables have with the price incentives. It will be helpful in understanding the dynamics of production and for planning programs for the public, bearing in mind the responses of the target groups.

Motivation: Farmers are motivated by the policies to include high-value crops in their cropping pattern. Whether and to what extent economic stimulants exert an influence on the Indian farmers in their decisions regarding area shifting is a question of paramount importance for agricultural planning in India and is the subject of discussion.

Gupta (1980) looked into the effectiveness of prices on the farmers’ income from important crops by using the information collected from 1965/66 to 1974/75. Juxtaposing it with non-agricultural income, he discovered that prices for agricultural goods had gone up more rapidly. He showed that pricing played a favorable and substantial role in increasing farm income through stimulating yield and marketable surplus. Singh and Das (1988) found that the pricing of products remained the primary instrument to boost the output of the agriculture sector and encourage farmers’ income. It was also highlighted that an incentive price regulation was by itself unlikely to be output-driven as long as the restrictions on capital were either abolished or lessened. Pal and Sirohi (1989) noted that variability in productivity was the most important factor responsible for the fluctuations in output of all selected crops barring the sugarcane crop. The period taken for the research coincided with the post-technology period, during which the level of uncertainty in output remained unchanged. Rao and K.S.C. (1995) stated that income and welfare of the farmers in the economy largely depend on the level of agricultural commodities. Farmers normally sell larger quantities when prices are high. Jagdish (1997) found that the output of sugarcane increased noticeably between 1951 and 1994 with year-to-year modest fluctuations. Although an increasing trend was observed in the yield of the sugarcane, the rise in area contributed most to the rising production of sugarcane, and the rates of growth under sugarcane were highest in Gujrat, which was followed by Karnataka and Tamil Nadu. Singh and Srivastava (2003) found that acreage had risen considerably in all regions as well as in the whole of the Uttar Pradesh state. The growth rate for the area was estimated at 1.60 at the state level. The output of sugarcane in the state propagated at a significant rate of 3.48%. In the case of the productivity of sugarcane, positive results were observed in all regions of the state.

Singh and Srivastava (2003) found a higher degree of variation in sugarcane in the central regions of India. The central regions demonstrated the most dramatic level of variability in the output sugarcane crop, and that was followed by the eastern and western regions. The pricing policy for sugarcane and payment system also contributed to making it feasible. Samui et al. (2005) found that during the period of the green revolution, Maharashtra underwent a major shift in the production of sugarcane. During the extended green revolution period, the advent and development of high-yielding cultivars and the implementation of cutting-edge technology contributed to the extensive rise in the yield and production of the sugarcane crop. During the post-green era (1980–2001), these had a damaging impact on sugarcane output. Positive growth rates were exhibited by the area of the crop in all regions of the state. Batool et al. (2015) examined the growth behavior of area and yield in Pakistan by using the time-series data from 1980 to 2013 and found positive trends for the selected variables. Sudhakar and Wale (2017) analyzed the trends of MSP and observed noticeable hikes in MSPs between 2011/12 and 2012/13, while making a comparative analysis with the previous years selected for the study. However, between 2013/14 and 2014/15, MSPs were increased moderately. It was suggested by the study that the margin of minimum support prices over the cost of production varied to a greater extent. Rai and Arti (2017) undertook research to find out the growth rates of area, production, and yield of the sugarcane in Uttar Pradesh by using time-series data from 1950 to 2015. Positive growth rates were revealed by the selected variables for the sugarcane. The yield of sugarcane showed steady and stable results, and the area and production of the sugarcane crop increased at the rates of 2.25 and 1.96%, respectively.

Mehta et al. (2020) examined the effectiveness of the minimum support prices on major crops in Haryana by collecting the data from secondary sources for the period from 2007/08 to 2017/18. The effect of farm harvest prices remained higher in the case of area and non-significant in the case of productivity. Significant variation took place in the area of major crops due to the previous year’s MSP, while it remained non-significant in the case of the productivity of the crops. Bee and Rahman (2020) found positive results for the growth of the acreage, productivity, and output of the sugarcane crop, and different factors like monsoon conditions and government price policies were accountable for the fluctuation in area and production of sugarcane. Joshi et al. (2021) conducted a study in Nepal to examine the growth behavior and instability by using the time-series data of 29 years from 1990/91 to 2018/19. The results were also obtained by dividing the study period into three sub-periods. It was found that area, production, and productivity of most of the cases declined during the second phase of the research period. The growth of the area of sugarcane, oilseeds, and maize increased during the third period of the study, whereas production increased in the case of sugarcane, oilseeds, lentils, and rice. It was also suggested by the study that crop-specific strategies regarding area expansion and technological intervention should be promulgated. Gupta and Badal (2021) concluded that trends in sugarcane productivity remained stagnant from 1950/51 to 2019/20 in Maharashtra. The results related to instability indicated that a drastic increase in area, output, and yield was noticed in Maharashtra. Kumar Rana and Kumar (2022) conducted a study between 2010 and 2019 in selected states, i.e., Uttar Pradesh, Maharashtra, Andhra Pradesh, and Karnataka in India, by incorporating statistical measures such as compound growth rate, percentage change, and percentage share. It was found that during the 2010–2019 period, a rapid and steady growth was the case in the output and yield of sugarcane. In Uttar Pradesh, while a slight decrease was noticed in the case of area, production, and yield of sugarcane in Andhra Pradesh. Negative results were obtained for all variables of sugarcane in Karnataka. Arun and Premkumar (2022) focused on area, productivity, and production of sugarcane from 2001/02 to 2017/18. They found that all the selected variables registered positive growth rates, and almost 65% of the production was the result of the efforts of Uttar Pradesh and Maharashtra. Impressive results were shown by the states falling under the tropical region than the states of the sub-tropical region.

Qian et al. (2020) found that the price policy for agriculture increased self-sufficiency in China. However, as a result of an increase in costs of production, the impact of an increase in price on income is smaller. Padmavathi and Ravi (2022) conducted a secondary data-based study to capture the agricultural growth of India and also for key grower states of sugarcane. Positive signs for the growth of sugarcane were exhibited by the acreage, productivity, and output. Due to a variety of factors, including monsoon conditions, government price regulations, and others, sugarcane farming is demonstrating a shifting pattern in both area and production. Dwivedi et al. (2023) used secondary data of 14 states in India for the period from 1998/99 to 2018/19 to know the factors affecting sugarcane output and found that fair and remunerative prices had a weaker relationship with the sugarcane output. Although there was a weaker relationship between area and production, it remained positive.

On the basis of the previous research, it can be observed that there was no study that took the complete issue of impact of farm harvest prices on the acreage and income of sugarcane growers in Haryana during recent years. The present study was taken up with relevant issues related to growth rates, variation, decomposition analysis correlation, and acreage response with relative profitability by incorporating the all-important variables like area, prices, yield, production, and income from sugarcane in Haryana, which was not found in previous publications. The present study aims to fill this gap. There is also a dearth of studies focused on sugarcane growers in Haryana that were conducted in the latest years with such a 40-year-long time span, and thus it is justified to make an analysis for such long period to observe the concrete change in sugarcane crop in Haryana.

The Indian state of Haryana served as the location for the study. The present study was pertained to the entire Haryana state, located in the northwest of India. Haryana became a fully-fledged state on November 1, 1966, when Punjab was reorganized. The border of Haryana state adjoins Himachal Pradesh in the north and Rajasthan in the south. In the east, Haryana borders Uttar Pradesh and Punjab in the west. The sugarcane crop has been selected to answer the stipulated questions of the study. After the green revolution period, the period from 1980 to the present day, the government of India initiated many institutional reforms to promote agriculture and safeguard the interests of farmers. Four decades of experience are enough to have a close look at the results of the combined results of the fair price policy and other schemes and reforms initiated in India. Therefore, the present study was planned to delineate the outcome for the period ranging from 1982/83 to 2021/22 for the Haryana state. Albeit the data for area, farm harvest prices, yield, production, and income were collated and perused, the analysis of the farm harvest prices remained at the forefront owing to the fact that it has a direct impact on the decision and choice of farmers as to what to produce on their farms. The study period was divided into four decades, which are as follows:

First period: from 1982/83 to 1991/92

The current study is grounded on secondary information and statistical abstracts of Haryana for 40 years served as the major source for obtaining the desired data related to area, farm harvest prices and yield of sugarcane crop in Haryana. To extract the data results related to the production of sugarcane during the set time-period, area and yield were multiplied, while data related to income were calculated by multiplying area and farm harvest prices per quintal of sugarcane crop in Haryana.The data related to production and income were calculated as follows: Production for the base year: Production(Q0)=A0×Y0

Q₀ – production of the base year,

The production for the current year was calculated as follows: Production(Qn)=An×Yn

Q_n – production of the current year,

To calculate the income of the base year, price and yield of the base year was multiplied. Income(I0)=Y0×P0

I₀ – income of the base year,

Likewise, the income of the current year was calculated: Income( I n )= Y n × P n

I_n – income of the base year,

Exponential function was used to calculate the compound growth rates of area, prices, yield, production, and income of sugarcane crop in Haryana state. Y=ABt where:

Y – area / farm harvest / yield / production / income of the crop,

The compound growth rate (r) is equal to (B – 1) × 100. In log form B was calculated by formula given below: LogB=∑ tlogY−∑ t∑ logY/N∑ t2−(∑ t)2/N

The t value was calculated to test the significance by using the formula t = r/s, where s is the standard error. Following formula was used to calculate the value of the standard error: Standard error (r)=100Blog10e∑ (logY)2−(∑ logY)2/N−(log10b)2∑ T2(N−2)∑ T2 where:

T=t−t¯ ,

To measure the extent of variability in area, farm harvest prices, yield, production, and income of sugarcane crop, the following type of formula was used: Standard deviation=Σ(X−X¯)2N where: X – area/price/yield/production/income.

By using this standard deviation, the coefficient of variation has been calculated to show the variability by using the formulae: Coefficient of variation =Standard deviationMean×100 or Coefficient of variation=Standard deviationX×100

The examination of the decomposition of the sources of output is not new in the agricultural research. The decomposition method was first propounded by Minhas and Vaidyanathan (1965) including the factors such as area, yield, cropping pattern and their interaction. Their method was additive in nature, while Parikh (1966) adopted multiplicative method for decomposition analysis. At the latter stage, four-factor model was expanded to seven-factor model by Mishra (1971), Sagar (1977), Sondhi and Singh (1975). Jamal and Asad (1992) also used price, quantity, and yield indices to decompose the conventional residue term. Dashora et al. (2000) also used seven-factor by additive method.

The formula used by Sharma (1977) was used to analyze the yield and price effects on the increased income of sugarcane crop in Haryana. The price-yield interaction effect was also computed by the following model given below: ΔI=P0ΔY+Y0ΔP+ΔPΔY where:

ΔI = I₀ – I_n

PΔY gives the yield effect and when divided by ΔI and multiplied by 100 it gives the effect in terms of percentage. Therefore, yield effect =P0ΔYΔI×100Similarly, price effect =Y0ΔPΔI×100

In the above formula:

P₀ – price in the base year,

Similarly, price effect P = P_n – P₀, i.e., difference between the current year and base year price.

Y₀ – yield in the base year,

To measure the degree of correlation between price and area, yield, production, and income of sugarcane crop, method developed by Karl Pearson was used. r=(n(∑ xy)−(∑ x)(∑ y))/([ n∑ x2−(∑ x)2 ][ n∑ y2−(∑ y)2 ]) where:

r – Pearson’s coefficient of correlation,

The base of supply response of agriculture is the adoptive expectation hypothesis developed by Nerlove (1958). Numerous researchers (Askari & Cummings, 1975; Cummings, 1975; Krishna & Rao, 1965; Narain, 1965; Rao & Krishna, 1965) applied the Nerlovian framework to analyze supply response. Partial adjustment and lag distributed model incorporated non-price factors in the examination of acreage or supply response of agriculture (Misra, 1998; Mythili, 2012; Palanivel, 1995; Parikh, 1971; Rao, 2004).

The Nerlovian distributed lag analysis was followed in the present study to examine the impact of the prices on the relative profitability acreage response of sugarcane crop in Haryana state. The current area under sugarcane crop was regressed on the last year area of sugarcane crop, farm harvest prices lagged by one year, yield during previous year and lagged farm harvest prices of paddy and lagged yield of paddy assuming that these variables leave influence on the current year acreage of sugarcane crop.

The lag linear function of the following form was fitted: Yt=a+b1(X t−1)+b2(Pt−1)+ b3(Yt−1)+b4(RPt−1)+b5(RYt−1)+et where:

Yt – area of the sugarcane crop in thousand hectares in year t,

The coefficients of variation for area, prices, yield, production, and income were shown in Table 2. An in-depth study of variation and identifying the factors responsible for it is of great significance to the planners for mitigating the variation and ensuring the stabilization of farm income in Haryana state. Undoubtedly, higher levels of production and income contribute to competitiveness. However, this might not be sufficient to be competitive. It is not an easy task for a particular crop to secure a place in the market if a greater degree of variability and uncertainty exists regarding output and income. Table 2 shows that a great deal of volatility and uncertainty were exhibited by almost all variables during most of the phases under consideration.

The extent of variability during the first period of the study was observed to be the highest in the case of sugarcane income (37.84%), followed by price (26.81%), and production (21.16%). Area exhibited the lowest fluctuation during the phase under consideration. During the second period, maximum variability was once again perceived in the case of income, but it is relevant to note here that the variability in all the selected variables has lessened to a certain extent during the second period of the study in comparison to the previous decade. Variation may be mitigated through irrigation facilities, high-yielding varieties of seeds, use of agro-chemical fertilizers, pesticides, and favorable weather conditions.

During the third period of the study, the maximum variation took place in the case of the income of sugarcane crop as the coefficient of variation remained at 54.67%. Price stood second as far as an examination of the variability in different variables is taken into account. Area, production, and yield were accorded the third, fourth, and fifth rank in accordance with the level of variance shown by these variables. The fourth period witnessed less variability in the case of all variables, the reason being that growth during that decade was also minimal in all variables. Thus, less growth was accompanied by less variance in that case. During the overall study period, it was found that the coefficient of variation remained at 92.43% in the case of the income of the sugarcane crop, while it was noticed at 78.55% in the case of the price of sugarcane crop. The coefficients of variation were calculated as 20.99% in the case of yield, 20.25% in the case of area, and 18.97% in the case of sugarcane production. Thus, it can be concluded on the basis of the findings that income and price emerged as the factors, in which the highest variation was identified. Of the all-selected variables studied, area and yield were identified as the variables with the smallest coefficients of variation during all the study periods. A rise in income was accompanied and partially offset by increased income instability, which could be explained by the unpredictable behavior of the supply of monsoon rain, the widespread use of a motley of varieties vulnerable to common yield reducers and also fluctuations and irregularities in the availability of inputs, along with the instability and unpredictable nature of the sown crop area. Hence, a suitable price support scheme, a crop insurance scheme, and a more assured supply of inputs might be helpful to safeguard farmers against income instability. The income variability can also be reduced by providing better marketing facilities and declaring of support prices for the crop under discussion. Besides, determined and resolute endeavors are called for to boost the low productivity of the crop under discussion in Haryana.

The contribution of price, yield, and their interaction are depicted in Table 3. It was revealed that the price effect to the increased income of the farmers from sugarcane crop accounted for 52.88%, while the yield effect remained at 21.08%. During this phase, the interaction between price and yield remained at 26.03%. During the second period of the study, it was found that the price effect proved to be the most significant factor in increasing the farmers income from sugarcane in Haryana, as price amounted to 73.39%. The yield effect and interaction effect were estimated at 13.17 and 13.44%, respectively.

Regarding the contribution of different components to the increased income of farmers from sugarcane during the third decade of the study, it was found that sugarcane price accounted for 70.46%. Yield effect and interaction effect were estimated at 8.48 and 21.06%, respectively. During the fourth period of the study, the price effect came down to 52.49%, but in spite of this, price remained the most powerful factor to increase farmers income from sugarcane in Haryana state. The yield effect was 39.22%, while the interaction of price and yield of the sugarcane crop accounted for 8.28%. During the overall study period, the interaction effect overpowered the price effect, as a 54.81% contribution was made by the interaction of price and yield effect to the increasing income of farmers, while the yield effect as 2.91% was made by the yield effect. The price effect was found to be 42.28%.

Therefore, it leads to the conclusion that throughout the study period, farmers’ income grew as a result of rising prices of sugarcane crop in Haryana. The farmers were not encouraged by the increasing prices to put in more effort to boost their income by growing sugarcane productivity through the use of improved seeds or technologies. This also leads to the conclusion that the government was unable to link the required training program or technology to boost productivity with the price incentives provided, which prevented the real revenue. Farmers’ income has increased due to rising sugarcane prices, but yield has not kept with these growing prices. During most of the periods, prices were the most significant factor. It shows the failure of the efforts made by the Indian government to boost the yield of the agricultural sector. Therefore, it is necessary to adopt improved measures be adopted by the government to make some difference in the existing situation. Therefore, the government should strive hard to further develop the infrastructure to modernize and widen the irrigation network and system in the state. Extension agencies and organizations should engage and collaborate with farmers to persuade them to manage more technologically advanced and a bit more contemporary inputs on their farms. The employment of high-yielding varieties has a profound effect on the yield of the crop, but considering the enormous proportion of area under the representative case is already being blanketed by high performing varieties of seeds, it is highly recommended that researchers should investigate and explore more different varieties of sugarcane crop that envisage a higher degree of efficiency. For that reason, academic research and scientific endeavors should be directed towards evolving and bolstering the productivity of the crop. Policy should be formulated and implemented at the macro level in the agriculture sector so that improved ways of irrigation, cultivation would be accessible to the farmers.

The final results related to the correlation of price with area, yield, production, and income of sugarcane are depicted in Table 4. During first phase of the study, the value of r was observed as 0.28, which shows the moderate degree of relationship between price and area of sugarcane in Haryana state. The relationship of price with yield also remained of a moderate degree, as the value of r in the case of yield was found to be 0.74 and it also remained of a moderate degree in the case of the production of the sugarcane crop in Haryana state. In the case of income, the calculated r was found to be 0.96, which proved that there had been a higher degree of correlation between price and income of sugarcane. It was also found that in cases of yield, production, and income, the correlation was observed to be statistically significant at 5% level of probability. During the second period of the study, there was a lower degree of correlation between price and area of sugarcane in Haryana, while in the case of yield and production, a moderate degree of correlation was exhibited. However, in the case of income, the value of r was at 0.98, which proved that price and income of farmers are highly correlated when it comes to the relationship between price and income of sugarcane crop in Haryana state.

During the third phase of the study, the relationship between price and acreage remained negative, as the value of r in that case was found to be -0.89, which proved that with the rise in the price of sugarcane, the area under sugarcane decreased and almost equivalent findings were obtained in the case of production, indicating that rising prices of sugarcane had led to a decline in the production of sugarcane crop in Haryana state. However, price and income showed a higher degree of positive relationship. During the fourth period of the study, the area had a lower degree of positive correlation with sugarcane price, while it had a moderate degree of correlation with yield and production of sugarcane crop. The price of sugarcane and income of farmers from sugarcane were observed to have a higher degree of correlation, which shows that as a result of the increasing price of the sugarcane crop, the income of the farmers increased in almost the same range.

Regarding the results related to the overall study period, it was observed that farmers shifted the land against sugarcane even after getting higher prices for sugarcane crop as the value of r in that case was negative. However, the relationship of sugarcane price remained positive with yield, production, and income of sugarcane crop. It was also surfaced through the figures that values of r showing correlation between price and area of sugarcane crop were found statistically significant at the 5% level of probability only during the third period of the study. The values of r depicting the relationship between price and yield of sugarcane crop were found to be statistically significant at 5% level of probability during all the study periods, barring the second period of the study. It also emerged from the results that values of r obtained for determining the degree of relationship between price and production remained statistically significant during the first and third periods of the study, but statically significant results were observed during all selected phases in the case of income.

The projected coefficients of different specific parameters determined by the way of multiple linear regression equations of the crop sugarcane are shown in Table 5. During the first phase of the study, the coefficients used in the function of sugarcane with regard to its area, price, and yield during the previous year, were positive. It was identified by the obtained value of the coefficient that for every 1% increase in the lagged area of the sugarcane crop in Haryana state, agricultural producers may predict a 1.29 % spike in the current area under the crop. It follows that growers’ expertize and understanding of production culture and societal customs had an enormous effect on the survival and development of the crop. It was demonstrated by the observed coefficient of lagged price of sugarcane that corresponding to the positive value of coefficient, a 1% upsurge in lagged year price had caused a 0.72% escalation in the current area of sugarcane. In light of the findings, it is apparent that a higher yield of sugarcane crop in the prior year corresponds to an expanded area in the subsequent year. It ensues because an even greater yield boosts the level of competitiveness of the crop by generating more revenue from the harvest. Regression coefficients for lagged year price and lagged year yield of paddy were observed to be negative, indicating that a rise in the price and yield of paddy in last year would have the detrimental implications for the current acreage of sugarcane. Thus, it is clear from the table that these variables served as an important factor in acreage determination. The value of R2 shows that the entire set of variables is very capable of explaining the variance. The value of the standard error also remained low, indicating a lower chance of inadequacy.

The results of the Nerlovian adjustment/lag model for the sugarcane crop during the second period show that the yield of alternative crop paddy had an adverse impact on the total acre under the sugarcane crop. Addressing the individual impact of the variables incorporated in the model, sugarcane harvest area lagged by one year, prices lagged by one year, and yield lagged by one year had been positive. Thus, it can be inferred that the current area allocation of sugarcane crop is positively influenced by one year lagged area, harvest prices, and yield of sugarcane crop, and prices of the relative crop also have a positive impact on the area of sugarcane crop. It is abundantly apparent from the measured amount of R2 that the specified factors employed to determine the variance in the current proportion of area under sugarcane are deemed sufficient to explain the variance in it.

During the third period of the study, it was revealed that the coefficients obtained to show the impacts on the sugarcane area with respect to its area, farm harvest prices, and yield in the last year, used in the function imply that all other things being equal, farmers ought to anticipate inflating the current area under sugarcane by 1.39% as a result of a 1% rise in the area lagged by one year. The coefficient of the lagged price of the same crop was determined to be positive indicating that the climb of the last year’s price by 1% enlarged the present year’s area under the sugarcane crop by 1.10%. In line with the value of the estimated coefficient for lagged yield, it appeared to result in additional area under the crop during the current year. Therefore, farmers were incentivized to broaden their acreage. As indicated by the projected results, a 1.15% and 1.03% decline was caused by a 1% increase in the lagged price and lagged yield of rival crop, respectively.

The results related to the relative profitability of the sugarcane crop during the fourth period of the study show that the calculated coefficients of lagged price and lagged yield of the sugarcane crop were found to be positive but estimated to be negative in the case of area. As per the estimated results, ceteris paribus, a 1% surge in the lagged area of the sugarcane crop had resulted in a 0.46% shrink in current area of sugarcane. Nevertheless, it came to light that price and yield lagged by one year for the sugarcane crop, which had counterproductive results for the current acreage under the crop. As a consequence, when the discussion turns to the area allocation under the sugarcane crop, agricultural producers are more inclined towards a more lucrative crop.

Regarding the findings related to the impact analysis of the lagged variables of sugarcane and competing crop on the acreage of the sugarcane during the overall study period, coefficients for lagged area, price, and yield were found to be quite positive. Thus, the evidence suggests that technological advances, management and supervision, also knowledge and specialization are essential components that significantly influenced the area and cultivation of sugarcane crop. The frequency and speed at which farmers adapt and rearrange the amount of land, on which sugarcane is planted in response to variables like area, price, and yield were determined to be 0.90, 0.32, and 0.71% as a result of a 1% expansion in area lagged by one year, lagged year price, and lagged year yield. The regression coefficients for the price and yield of the competing crop exhibited a negative influence on the current area allocation of the crop. The magnitudes of the coefficients of relative crop were estimated at 0.46 and 0.59, respectively. Thus, more preference has been given to paddy in comparison to sugarcane by the farmers of the state due to the increasing price and per hectare productivity of the relative crop.