Linking Demand with Supply for Tropical Forage Genetic Resources to Reach Impact at Scale

Over the last decades a wealth of information on tropical forage genetic resources has been collated and is accessible to users ranging from farmers, development practitioners and researchers to decision makers and academia, e.g. through online tools such as Tropical Forages: An interactive selection tool (www.tropicalforages.info). However, while genetic diversity is being conserved by international gene banks, adoption of improved tropical forages is still far from reaching its full potential. Major bottlenecks in our view include lack of awareness of available forage genetic resources, confirmation of adaptation in a wide range of bio-physical, socio-economic, political and cultural contexts and functionality of financing, extension and seed supply systems. This paper discusses the potential for adoption of tropical forages in the context of new opportunities by market driven innovation, and presents early successes using as examples improved Urochloa spp., Megathyrsus maximus and other grass and legume germplasm, while describing possible pathways to go to scale with small and medium size livestock producers. We use examples of approaches from the tropical Americas, tropical Africa and tropical Asia, including partnerships with the private sector in diverse market environments (e.g. Africa and Latin America) and network approaches (Asia)

Farmers' Perceptions of Dairy Cattle Breeds, Breeding and Feeding Strategies: A Case of Smallholder Dairy Farmers in Western Kenya

To understand farmers' preference and perceptions of breed attributes, breeding and feeding practices, 419 households in western Kenya were interviewed in a cross-sectional survey. Respondents scored their preference for cattle breeds, traits and breeding methods on a scale of 1 (most preferred) to 5 (least preferred). Preferences were compared using multinomial logistic regression models on weighted scores. The Ayrshire breed was most preferred followed by the Friesian. Using hardship tolerance as a reference trait, the Friesian was preferred 4.86 times more for high milk production and Ayrshire, Jersey and Guernsey breeds 4.61, 4.60 and 4.18 times (p p p 68%) of respondents used natural mating, because it was readily available and cheaper. The current study highlights the importance of taking into account farmers' objectives and the production environment when designing breed improvement programmes and recommends packaging of breeding together with feeding interventions

Use of body linear measurements to estimate liveweight of crossbred dairy cattle in smallholder farms in Kenya

Body linear measurements, and specifically heart girth (HG), have been shown to be useful predictors of cattle liveweight. To test the accuracy of body linear measurements for predicting liveweight, crossbred dairy cattle of different genotypes were measured and weighed. A total of 352 mature cows and 100 heifers were weighed using an electronic weighing scale and measurements of HG, body length, height at withers were taken using an ordinary measuring tape and body condition scored (BCS) using a five-point scale. The animals were grouped according to genotype and age. Genotype classification was undertaken from farmer recall and by visual appraisal as 40–60, 61–80 or 81–100 % exotic (non-indigenous). Age classification was simply as mature cows or heifers. Liveweight of the animals ranged from 102 to 433 kg. Liveweight was strongly correlated with HG (r = 0.84) and body condition scores (r = 0.70) and moderately correlated with body length (r = 0.64) and height at withers (0.61). Regressing LW on HG measurements gave statistically significant (P 2 ranging from of 0.53 to 0.78 and residual standard deviation ranging from 18.11 to 40.50 kg. The overall model developed (adjusted R2 = 0.71) had a prediction error of 26 kg (or 11 % of the mean) and predicted LW of over 95 % of crossbred dairy cattle in the range of 100–450 kg, regardless of age and breed group. Including BCS in the model slightly improved the model fit but not the prediction error. It was concluded that the model can be useful in making general management decisions in smallholder farms