The predictive ability of phenological models, derived from glasshouse studies and driven by temperature and photoperiod, was investigated across a diverse range of cover crop genotypes and tropical hillside environments. The models were designed to predict the duration from germination to first flowering, and from first flowering to first pod maturity. Seeds of eleven legume species of cover and/or green manure crops collected from different hillside locations world-wide were sown in two groups of nurseries (tropical short-day plants in early summer and sub-tropical long-day plants in early winter) at Kabale and Namulonge in Uganda, Godavari and Lumle in Nepal, Cochabamba in Bolivia, Zamorano in Honduras, and Valenca in Brazil. Dates of sowing, first flowering and first pod maturity were taken and daily temperature data were recorded at each site. Similar observations for the same genotypes were available from independent experiments conducted at Islamabad, Pakistan, Hattiban, Nepal and at three locations in Cyprus. Model predictions were compared with field observations. The proportion of variation accounted for in the period from sowing to first pod maturity was 88% and 89% for the short-day and the long-day groups of genotypes, respectively. Likewise, the average difference from sowing to pod maturity between the model predictions and the field observations was 6.3% and 7.9% for the combined short-day species and the combined long-day species, respectively. It is clear that the model predictions, for this dataset at least, are sufficiently robust to serve as a filter for determining the environmental suitability of germplasm.
Qi, A.; Keatinge, J.D.H.; Wheeler, T.R. Validation of a photothermal phenology model for predicting dates of flowering and maturity in legume cover crops using field observations. Biological Agriculture and Horticulture (2000) 17 (4) 349-365. [DOI: 10.1080/01448765.2000.9754855]
Validation of a photothermal phenology model for predicting dates of flowering and maturity in legume cover crops using field observations.