AI diffusion links with perceived farm performance in Punjab

A cross-sectional survey of 101 farmers in Punjab, Pakistan, found that more extensive use and integration of artificial intelligence-enabled agricultural technologies was associated with higher perceived farm productivity and profitability. The study, published in Frontiers in Artificial Intelligence, reports a moderate overall level of AI diffusion, with a mean composite score of 18.27 on a scale extending to 25. AI diffusion correlated with perceived productivity at r = 0.617 and with perceived profitability at r = 0.607, with both relationships statistically significant at p < 0.001. These results do not show that AI caused better farm outcomes: they identify associations among farmers’ reported adoption and their assessments of performance in a small, non-probability sample.

The researchers defined AI diffusion as farmers’ awareness of AI-enabled agricultural tools, their current use of those tools and their integration into routine activities. Examples included precision-farming applications, predictive analytics for yields or pests, and sensor-based irrigation or monitoring systems. The composite also incorporated expectations about AI, including its possible role in crop-management decisions and plans for future expansion. Farm productivity represented perceived improvements in production efficiency, crop yield and resource use, while profitability represented perceived improvements in cost efficiency, income stability and financial returns. Every construct was measured through five-item, five-point Likert scales, from 1 for strong disagreement to 5 for strong agreement.

Because audited yield and profit data were rarely available for the farms represented in the study, the investigators relied on farmers’ perceptions rather than objective performance records. The questionnaire was administered in person and through local agricultural networks, and participants consented to the study under ethics approval granted by Ulster University on 16 February 2026. Reliability was acceptable for all three measures: Cronbach’s alpha was 0.779 for AI diffusion, 0.830 for productivity and 0.784 for profitability. A Kaiser–Meyer–Olkin value of 0.887 and a significant Bartlett test of sphericity indicated that the questionnaire items were suitable for exploratory factor analysis, although no formal pilot study was conducted before full data collection.

The respondents’ attitudes were generally favorable, although reported use trailed belief in AI’s potential. A total of 53.5% agreed or strongly agreed that their farm used tools such as sensors, drones or smart irrigation, while 71.3% agreed or strongly agreed that AI improves crop-management decisions. Even stronger optimism appeared around monitoring and financial applications: 75.3% agreed or strongly agreed that AI could improve monitoring of farm conditions, and 81.2% agreed or strongly agreed that it could identify profitable crop-management strategies. Across the composite measures, perceived productivity averaged 19.08 with a standard deviation of 3.66, while perceived profitability averaged 19.64 with a standard deviation of 3.20.

Linear regression supported all three relationships specified by the study’s conceptual model. In the model predicting productivity from AI diffusion, the unstandardized coefficient was 0.608, with t = 7.800 and p < 0.001; the model accounted for 38.1% of the variation in perceived productivity. When AI diffusion predicted profitability, the coefficient was 0.525, with t = 7.601 and p < 0.001, and the model explained 36.8% of the variation in perceived profitability. The strongest relationship was between productivity and profitability: the productivity coefficient was 0.656, t was 11.247, p < 0.001, and R² was 0.561. These are substantial statistical associations in this dataset, but they are not estimates of causal gains in yield, income or profit.

The study combined three theoretical perspectives to interpret the results. Diffusion of Innovations theory emphasizes farmers’ perceptions of an innovation’s relative advantage, compatibility with existing practices, complexity, trialability and observability. The Resource-Based View focuses on complementary internal capabilities, including technical knowledge, finance and organizational skills, that may enable farmers to turn access to technology into measurable value. The Technology–Organization–Environment framework adds external conditions such as infrastructure, institutional support, regulation and markets. Together, these perspectives suggest that awareness and perceived usefulness alone cannot explain adoption or performance; technology operates within a wider system of human skills, farm resources and local conditions.

The descriptive results point to a perception–adoption gap. Farmers generally believed that AI could improve decisions, monitor conditions, manage water and fertilizer, reduce losses and strengthen financial returns, but fewer reported that their farms currently used AI tools. The study interprets this as a transitional stage in which awareness and favorable attitudes are more developed than implementation. The authors caution, however, that they did not directly measure investment costs, connectivity, electricity reliability, technical expertise, credit access or extension support in the surveyed sample. These barriers are therefore plausible, literature-informed explanations for the gap rather than causal determinants demonstrated by the analysis.

Productivity and profitability were treated as related but distinct outcomes. Greater technical efficiency can, in principle, support financial performance by reducing input waste or improving operational decisions, but higher output does not automatically ensure higher profit because prices, production costs, market conditions and the expense of acquiring and maintaining technology can alter the final result. The study did not measure these external economic factors directly. Its significant productivity–profitability association may therefore be compatible with several mechanisms, including genuine efficiency gains, farmers’ confidence in technologies they have adopted, or optimism about tools they expect to use more extensively in future.

The sample included small farms of less than 12 acres, medium farms of 12–25 acres and large farms above 25 acres, with medium-sized operations comprising 55.4% of respondents. Wheat was the most commonly reported main crop, followed by rice, cotton and sugarcane. Nevertheless, convenience sampling, the absence of a comprehensive sampling frame and the failure to record district and village identifiers systematically limit geographic specificity. The modest sample also reduces power to detect smaller associations and increases the risk of a type II error. No residual diagnostics for normality or homoscedasticity were undertaken, and the instrument was not validated through exploratory and confirmatory factor analysis in independent samples.

The results offer context-specific evidence that AI diffusion in Punjab’s agricultural sector is neither absent nor mature, but uneven and incomplete. Strong perceptions coexist with moderate reported use, while respondents who report greater AI integration also tend to describe their farms as more productive and profitable. The evidence does not justify claims that AI has already raised incomes or yields across Punjab, nor does it identify the mechanisms responsible for the relationships. Longer-term research using probability-based sampling, objective yield and financial records, repeated observations and methods capable of addressing endogeneity would be needed to determine whether AI causes measurable gains, for which farm types and under what infrastructure, cost and capability conditions.

Subject of Research: Associations between AI adoption and perceived productivity and profitability among 101 farmers in Punjab, Pakistan

Article Title: Improving farm productivity and profitability through AI diffusion: evidence from Punjab, Pakistan

Article References: Khalid, A., Akhurst, C., Gilani, S. A. M., Armosh, F., Zanib, S. A., Boath, J., Peel, R. S., & Jinadasa, Y. (2026). Improving farm productivity and profitability through AI diffusion: evidence from Punjab, Pakistan. Frontiers in Artificial Intelligence, 9, Article 1890862. https://doi.org/10.3389/frai.2026.1890862

Image Credits: AI Generated

DOI: 10.3389/frai.2026.1890862

Keywords: artificial intelligence, digital agriculture, farm productivity, farm profitability, technology adoption, Punjab, Pakistan, smallholder farming, survey research, Improving, farm, productivity