by Behzad Jeddi and Guilherme DePaula
Producers across the Corn Belt increasingly face seasons when heat accelerates insect development, tightens the effective control window, and raises the risk of costly losses. A new study published in Agricultural and Resource Economics Review (Jeddi and DePaula 2025) asks a practical question: Can farmer networks, where members share timely local observations, help producers act at the right moment and protect profits when excessive heat pushes pest pressure?
Why this matters
In practice, producers often respond to an increase in the frequency of damaging pest outbreaks by using higher pesticide dosages. However, relying on dosage is a costly substitute for timing. Many key corn pests can only be managed within a narrow timeframe, meaning that the main issue is informational—applying pesticides too early wastes money, while applying them too late can result in lost yield.
Area-wide coordination can help address this timing challenge. Since mobile pests do not respect farm boundaries, isolated responses are usually less effective. Coordinated information sharing improves decision-making and can slow down the development of pest resistance. Social learning, which includes guidance based on experience, on-farm scouting, and signals from peers, serves as a valuable means for adaptation. In addition to increasing profitability, better timing reduces unnecessary pesticide applications and their associated external costs to water, soil organisms, and pollinators.
What we studied
Jeddi and DePaula (2025) examine the economic advantages of engaging in farmer networks for pest management, with an emphasis on the timing of pesticide applications. They build upon the target-input framework developed by Foster and Rosenzweig (1995), adapting it to tackle the issue of optimal treatment timing. Utilizing Monte Carlo simulations, they project potential benefits under typical climatic conditions and underscore the enhanced value of networks during periods of heat stress, as indicated by increases in Growing Degree Days (GDD). Their case study focuses on the Smart Integrated Farm Network for Rural Agricultural Communities (SIRAC) pilot network in Iowa, which involves 121 soybean and corn producers and was initiated to assess telecommunications and pest-management strategies. They identify three distinct learning channels: background guidance; hands-on learning through scouting; and, peer information. The simulations are calibrated to the European corn borer; however, the same framework can be extended to other corn pests by reparametrizing the pest-population growth and phenology components for the target species.
Key findings
Figure 1 illustrates the distributions of differences in expected gains from a 10% increase in GDDs. The blue histograms represent the difference in farmers’ expected gains from scouting alone when comparing the +10% GDD scenario to the baseline climate. In contrast, the orange histograms depict the corresponding differences when farmers integrate scouting with network signals under the same conditions. The network’s adaptation value under the +10% GDD scenario is indicated by the gap between the orange and blue distributions, highlighting the additional benefits of network participation beyond scouting alone. Dashed vertical lines mark the medians of each distribution, with each panel varying the precision of scouting information and network signals.
When both scouting and network signals are relatively imprecise (Graph A), network participation still delivers economically meaningful protection in hot years. In the low–low precision panel, the median gap between the orange and blue distributions implies an adaptation value of about $52 per acre, reflecting fewer late sprays and fewer “insurance” applications when neighbors upstream in the wave provide timely cues. Holding farm scouting precision fixed but improving the precision of network signals (low scouting, high network precision, Graph B raises the median gap to about $62 per acre. We can quantify the benefits of enhancing network signal precision across all farms by comparing the outcomes presented in Graphs A and B. The difference, averaged across farms, represents an expected gain of approximately $35 per acre, which means the value derived from investing in improving network signal precision.
Graphs C and D illustrate the results of simulations in which scouting technology precision is uniformly high across all farms within the network. While it is unlikely that every farm would have access to such high-precision scouting in reality, analyzing this scenario helps to establish a lower bound for the network's adaptation value.
When farms possess advanced internal capabilities for monitoring pest populations, the added benefit of external information received from network peers naturally diminishes. The simulation results shown in Graph C indicate that the expected adaptation value of the expanded network, given high precision in scouting technology but low precision in network signals, is relatively modest, around $10 per acre. When we improve the accuracy of the network signal, the expected adaptation value increases only slightly to approximately $25 per acre, as demonstrated in Graph D. These findings emphasize that even in a more conservative scenario where all farms utilize high scouting technology, there remains a noticeable, albeit marginal, adaptation value in learning from network peers.
Large variation in the network value across farms
Our analysis reveals substantial variation in adaptation values across quantiles for each network simulation and climate scenario. For the SIRAC network, adaptation values range from $16–$38 per acre at lower quantiles but rise dramatically at the upper end, reaching $108 per acre with a 10% GDD increase and $126 per acre under more severe climate change (Jeddi and DePaula 2025). This variation primarily stems from differences in initial pest populations and extreme GDD magnitude, as other parameters remain constant across quantiles.
Results for an expanded version of the SIRAC network show only marginal improvements over the original network, indicating that simply increasing network size yields limited benefits without addressing information precision variability among members.
However, implementing a signal selection mechanism based on geographical proximity significantly enhances adaptation capabilities. Under a 20% GDD increase scenario, the network with signal selection achieves an adaptation value of $255 per acre in the upper quantiles of the distribution of network value. These findings highlight two insights: farmer networks can provide significant adaptive benefits under heightened climate stress and pest infestation risks, and targeted signal selection strategies prove far more effective than mere network expansion for improving climate adaptation outcomes.
References
Jeddi, B., and G. DePaula. 2025. "The Economic Value of a Farmer Network: An Application to Pest Management in Iowa." Agricultural and Resource Economics Review 54(2):440-471.https://doi.org/10.1017/age.2025.10007
Foster, A.D., and M.R. Rosenzweig. 1995. "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture." Journal of Political Economy 103(6):1176-1209. https://www.jstor.org/stable/2138708
Suggested citation
Behzad, J., and G. DePaula. 2025. "The Value of Farmer Networks When Heat Drives Pest Pressure." Agricultural Policy Review Fall 2025. Center for Agricultural and Rural Development, Iowa State University. https://agpolicyreview.card.iastate.edu/fall-2025/value-farmer-networks-when-heat-drives-pest-pressure/