Climate Information Services Integrating Crop Modeling for Decision-Making in Corn Farming

Climate products (i.e., information such as seasonal forecasts, climate projections, and rainfall advisories) are useful only when they respond to the needs of users. However, the interaction between climate information providers and users is often limited. A major concern is how to convert climate information into sectoral impacts and practical response options that end users can apply. In the Philippines, progress has been made in producing climate information products, but user needs and the fields of application of Climate Information Services (CIS) vary greatly, consequently requiring a broader range of knowledge products.

This monograph draws on the author’s accumulated research contributions in the field and enhances them further by presenting a solution for corn farming in the Philippines to cope with climate variability through an information and communication technology (ICT)-based CIS. It aimed to: (1) develop a localized corn CIS model; (2) assess future corn production under various climate change scenarios; and (3) develop a decision support system for corn production. The methods employed were scientific tools, such as simulation and climate modeling, integration of Automated Weather Stations (AWS) for real-time weather data inputs, and short message service (SMS) as decision support tools for government workers and farmers. The model developed was able to predict the observed data on yield and the timing of phenological events from the experiments and actual farmers’ fields with a high goodness of fit, ranging from 91 to 98 percent for the calibration and 86 to 97 percent for the validation process. Moreover, applications of the model for climate change assessments indicated that corn yield in northern Philippines could be reduced by up to 35 percent by 2050 due to changes in rainfall amounts and a rise in temperature, which are indicators of climate change. The model was automated to provide decision support for farmers’ operational decision-making, as well as crop and weather advisories. The pilot test, in which representative farmers from various agroecozones participated, indicated an average yield increase of 24.41 percent over the baseline average yield. Hence, an immediate rollout of the CIS innovation from this proof of concept is highly feasible and implies significant socioeconomic impact benefiting corn farmers and the local economy.