How do forage availability and climate control sheep reproductive performance?. An analysis based on artificial neural networks and remotely sensed data.

TEXEIRA, M.; PARUELO J. M.; JOBBAGY, E.G.

ECOLOGICAL MODELLING

Elsevier

Año: 2008 vol. 217 p. 197 - 206

0304-3800

Environmental variability affects life history and fitness of both animal and plant species. Forherbivores in particular, climate can have strong direct and indirect effects on demography,which tend to exacerbate in arid and semiarid environments with highly seasonal weather.We studied the joint effect of forage conditions, plant phenology, and climate on the reproductiveperformance of a “model” population: domestic sheep in the Patagonian steppe ofArgentina. In this region sheep behave as semi-natural populations and relatively good populationrecords are available. Using linear models and artificial neural networks trained bysecond order back-propagation methods, we demonstrated that reproductive performance,characterized by the marking rate (number of lambs per ewe), was associated to the timingof growing season start and to the primary production (as estimated from remotely senseddata) at mating. An ANN model including these variables explained 73% of the variabilityof normalized marking rate, and predicted observed marking rates with an accuracyof 63%. Our results highlight the importance of forage availability as opposed to weatherregulating the reproductive performance of sheep at Patagonia, suggesting that bottom-upcontrols are of dominant importance for these populations. Using artificial neural networks,satellite imagery, and historical productive and climatic records, we disentangled the controlsof sheep reproductive performance in a region characterized by weak but consistentrelationships between environment and sheep dynamics.Ourwork aims to the development of quantitative tools for the management and planningof sheep herd structure from ranches to regions, especially for those situations in whichclassical methods (i.e. linear methods) fail.