The researchers, writing in Frontiers in Sustainable Food Systems, combined crop-specific DEA models with a pooled analysis, Tobit regression, fractional-logit robustness tests and non-parametric comparisons across the valley’s three geographical zones. Apple, pear and cherry production were analyzed separately because their underlying technologies may differ. The pooled model placed all 418 farms against a common frontier and generated the efficiency scores used in the second-stage analysis of household, institutional and regional factors. This design also allowed the team to distinguish overall technical efficiency from pure technical efficiency, which measures performance independently of production scale, and scale efficiency, which measures how closely a farm approaches the most productive scale.

DEA is a non-parametric method that constructs an empirical production frontier from observed combinations of inputs and outputs. Farms closer to that frontier are considered more efficient relative to their peers. The study used total horticultural output, measured in kilograms, as the single output. Eight inputs entered the models: landholding in marlas; urea, diammonium phosphate and potassium fertilizer in kilograms; vermicompost in kilograms; family and hired labour in man-days; plant-protection sprays in litres; and the number of horticultural assets. The input-oriented specification was chosen because growers have more discretion over the quantities and combinations of inputs they employ than over output, which is also affected by weather, biological conditions and markets.

Under the constant-returns-to-scale model, the mean score was highest for apple growers, followed by pear and cherry growers. More than half of apple farms, 51.08%, scored at least 0.80, compared with 28.07% of pear farms and 28.82% of cherry farms. Fully efficient farms—those scoring 1—accounted for 8.19% of apple growers, 6.38% of pear growers and 5.90% of cherry growers. Cherry production had the lowest average score and the widest dispersion, indicating especially uneven performance among its farms. These figures describe relative efficiency within the sample, not absolute productivity or the maximum output physically possible on each orchard.

The variable-returns-to-scale results were markedly higher: 92.05% of apple growers, 92.60% of pear growers and 92.01% of cherry growers were classified as efficient when farms were benchmarked against others of comparable scale. Scale-efficiency averages were substantially lower, at 0.8181 for apple, 0.7218 for pear and 0.6771 for cherry, and only 8.19%, 6.38% and 5.90%, respectively, reached full scale efficiency. In other words, most growers appeared to manage their available inputs effectively within their own operating size, yet many operated below or beyond the scale that would have produced the strongest frontier performance. The findings do not mean that every farm should be enlarged: scale efficiency identifies a distance from the sample’s productive scale, not the crop or size that would be optimal for an individual farm.

Farm size and production also differed sharply among crops. Apple growers reported average output of 8,488.63 kg from an average holding of 136.17 marlas, while pear growers averaged 1,643.44 kg from 36.63 marlas and cherry growers 206.15 kg from 29.27 marlas. Large standard deviations in output and input use showed substantial heterogeneity among farms. Some households cultivated more than one fruit, so the crop-specific samples included 415 apple growers, 392 pear growers and 288 cherry growers even though the household survey covered 418 farms. Sampling was multi-stage: all 10 districts were placed into North, Central and South Kashmir; two horticulture-intensive blocks were selected in each district; and commercially active households with relevant production experience were then purposively selected.

In the pooled constant-returns model, the mean farm-level score was 0.5823, with a standard deviation of 0.1623 and a range from 0.3050 to 1.0000. The lower pooled mean reflects the greater heterogeneity of a common frontier encompassing different crops, farm sizes and production systems. Tobit regression, which accommodates efficiency scores bounded between zero and one, showed that the explanatory variables were jointly significant. Family size had a positive coefficient of 0.0422, education of the household head 0.2196, artificial irrigation 0.1504 and access to institutional credit 0.0727, all with p-values below 0.01. Cold-storage availability was also positive, with a coefficient of 0.1041, but was significant only at the 10% level.

Regional location was associated with the pooled efficiency measure even after the regression accounted for household and institutional variables. Relative to Central Kashmir, coefficients were 0.1734 for North Kashmir and 0.2197 for South Kashmir, both significant at p<0.01. A fractional-logit model broadly reproduced the direction of the main relationships: family size, education, irrigation, credit and regional location remained positive, while cold storage became significant at the 5% level. Economic status was not significant in the Tobit model but negative and significant at p=0.008 in the fractional-logit specification. Market accessibility was negative in both models, significant at 10% in Tobit and 5% in fractional logit. These are conditional associations rather than evidence that any factor caused changes in efficiency.

Non-parametric tests supported the presence of geographic variation. A Kruskal–Wallis test rejected the null hypothesis that efficiency distributions were identical across the three zones at p=0.015. Pairwise Mann–Whitney comparisons with adjusted p-values found a significant difference between South and North Kashmir at adjusted p=0.018. The adjusted Central–North comparison was not significant at the conventional 5% threshold, while South–Central had an adjusted p-value of 1.000. Together with the regression results, this points to location-specific production conditions, infrastructure or services as possible explanations, although the cross-sectional design cannot isolate which underlying features produced the differences.

The study offers evidence that efficient horticulture in the Kashmir Valley may depend as much on production scale and supporting institutions as on better allocation of fertilizer, labour and other inputs. Its results associate technical efficiency with larger families, literacy among household heads, irrigation, credit, cold storage and location, while also showing that proximity to a market alone does not correspond to higher measured efficiency. Several limitations constrain generalization: the data were cross-sectional, so they do not track changes over time; they came from one valley and three fruit crops; and purposively selected commercial growers may differ from households outside the sample. The authors also note that future work could add environmental indicators, panel data and comparisons with other horticultural regions. The present results identify measurable associations and the relative importance of scale, but do not demonstrate that a specific intervention would raise output, farm income or environmental performance.

Subject of Research: Farm-level technical efficiency among apple, pear and cherry growers in Kashmir Valley

Article Title: Farm-level technical efficiency in temperate horticulture: evidence from apple, pear, and cherry growers

Article References: Sajjad, S., Dar, K. H., Ganie, A. U. H., Yousif, G. M., Salim, M., & Othman, M. S. (2026). Farm-level technical efficiency in temperate horticulture: evidence from apple, pear, and cherry growers. Frontiers in Sustainable Food Systems, 10, Article 1928386. https://doi.org/10.3389/fsufs.2026.1928386

Image Credits: AI Generated

DOI: 10.3389/fsufs.2026.1928386

Keywords: technical efficiency, Data Envelopment Analysis, Kashmir Valley, apple cultivation, pear cultivation, cherry cultivation, scale efficiency, rural credit, irrigation, sustainable horticulture, Farm-level, technical