Regional analysis of maize-based land use systems for early warning applications

Conventional analytical crop growth models cannot handle actual Land Use Systems because of massive data needs, algorithm complexity and prohibitive error propagation. It is possible however to describe rigidly simplified 'Production Situations' representing Land Use Systems with annual row crops and minimal environmental constraints. The simplest Production Situation imaginable is a Land Use System in which all constraints that can be eliminated by a farmer are indeed (assumed to be) eliminated. Crop growth and yield are then entirely conditioned by crop physiology and weather conditions, notably by the temperature and radiation during the crop cycle. The calculated production level is not the actual production but the production potential.

In many countries, water availability to the crop is the main constraint to crop production. The biophysical production potential model has therefore been extended with a water budget routine that matches actual water use with the crop's requirement in order to calculate the "water-limited production potential". In this configuration, crop physiology, temperature, radiation and water availability condition the calculated level of crop (potential) production. This thesis discusses the use of satellite-derived rainfall data for regional analysis of water-limited yield potentials.

Monitoring and crop yield forecasting for early warning applications require insight in farmers' reality. Often, a score of environmental and socio-economic constraints reduce on-farm production to a level that lags far behind the theoretical production potential. This thesis explores farmers' insights, in an attempt to identify the causes and structure of the "yield gap" between potential (reference) production levels and production levels realized on-farm.

So far, actual production could only be established through field measurements. This thesis presents a methodology for estimating regional levels of actual crop production. The difference between remotely sensed canopy temperature and ambient temperature is used to estimate the degree of stomata closure of the crop. Introducing this Remote Sensing based degree of stomata closure in calculations of assimilatory activity permits to calculate the actual rate of crop growth over regions.

Repeated measurements during the crop cycle allow monitoring of the sufficiency of actual management practices. Introducing estimated or forecast weather data in crop growth calculations for the remainder of the crop cycle permits to make repeated estimates of anticipated crop production and to timely signal a need for remedial action.