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Farming technologies and food yields in Pakistan

Table 5. ARDL estimates
Dependent variable: CCY
Variables Coefficient Standard error t‑Statistic Probability
∆(AGRI) −0.014 0.002 −5.801 0.000
∆(TEMP) −180.128 198.039 −0.909 0.367
∆(DAML) 3971.774 1568.494 2.532 0.014
∆(FAR) 24.330 11.244 2.163 0.034
∆(CH ) 50.567 23.435 2.145 0.045
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∆(N O) 48.356 37.546 2.457 0.053
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∆(PAT) 0.048 0.012 3.840 0.000
CointEq(−1) −0.362 0.148 −2.443 0.017
Long run coefficients
Variables Coefficient Standard error t‑Statistic Probability
AGRI −0.028 0.010 2.801 0.035
TEMP −497.536 499.057 −0.996 0.323
DAML 3163.687 2299.048 1.376 0.174
FAR 67.203 26.846 2.5031 0.015
CH 4 58.562 28.096 2.573 0.013
N O 56.203 30.326 2.952 0.016
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PAT 0.147 0.052 2.826 0.035
Constant 21865.514 10810.553 2.022 0.048
Source: Author’s estimate.
Abbreviations: AGRI: Agriculture value-added; CH : Methane emissions; CCY: Cereal crop yield; DAML: Data analytics and machine
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learning; FAR: Farmer’s adoption rate; N O: Nitrous oxide emissions; PAT: Precision agriculture technology; TEMP: Temperature.
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importance of policy frameworks, extension services, emissions are indicative of more nutrient-rich soil, which
and institutional capacity in technology transmission is crucial for increasing agricultural production. This
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and farmer training. 57 relationship emphasizes the necessity for data-driven
The ARDL results indicate a positive relationship solutions and precision agriculture to strike a balance
between CH and N O emissions and CCYs in the short between crop production and greenhouse gas emissions.
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and long run. This shows that intensive agricultural Precise agriculture technology can aid in the rapid
practices increase emissions and food output. However, enhancement of CCYs over time, boosting agricultural
this association highlights an essential cost-benefit efficiency and sustainability. Data analytics, GPS-
analysis: agricultural intensification improves yields and guided devices, and remote sensing provide accurate
greenhouse gas emissions, threatening environmental resource management, site-specific interventions, and
sustainability. These findings demonstrate the necessity real-time monitoring. These technologies provide
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to include precision farming technology that maximizes personalized input applications for crop and soil
input use and reduces environmental impacts into conditions, improving yields with minimal waste and
productivity gains to reduce emissions. Further, the environmental impact. It has been demonstrated that
findings reveal that productivity advantages and long-term output and resilience can be increased through
environmental externalities are traded off since more the implementation of strategies informed by data
emissions are generated in exchange for higher yields, collected from multiple growing seasons. In addition,
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which are not achieved in a more sustainable manner. precision agriculture may strengthen agricultural
Conversely, CH and N O emissions may enhance plant systems, protect biodiversity, and improve soil health,
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growth and soil fertility in some situations. These enabling sustainability in agriculture.
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gases enhance plant growth and output and are usually Table 6 shows that diagnostic testing confirmed
produced by microbial activities in the soil. These the ARDL model’s robustness. The Breusch–Godfrey
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Volume 22 Issue 3 (2025) 113 doi: 10.36922/AJWEP025130096