There is an urgent need to mitigate climate change-induced heat stress in livestock and poultry in the Caribbean, given the deleterious effects it has on food and nutrition security. The temperature humidity index (THI) was used to assess the potential for heat stress on four types of livestock and poultry (broiler and layer chickens, pigs and ruminants) for three different agroecological locations in Jamaica. The THI was formulated specifically to each livestock type and was examined for 2001-2012 for seasonal and annual patterns of variability. Differences in THI were observed between summer (July to September) and winter (December to February) with some moderation due to agro-ecological location. Our results suggest that animals in ambient field conditions in Jamaica may already be experiencing considerable periods of heat stress even during the relatively cooler northern hemisphere winter months. Future patterns of heat stress relative to a 1961-1990 baseline were derived from a regional climate model when mean global surface air temperature is 1.5, 2.0 and 2.5°C above pre-industrial levels. At 1.5°C, marked increases were noted in THI and almost persistent year-round heat stress is projected for Caribbean livestock. Conditions will be exacerbated at the higher global warming states. Possible response strategies such as cooling technologies are discussed.

Abstract:The use of automated methods to estimate fractional vegetation cover (FVC) from digital photographs has increased in recent years given its potential to produce accurate, fast and inexpensive FVC measurements. Wide acceptance has been delayed because of the limitations in accuracy, speed, automation and generalization of these methods. This work introduces a novel technique, the Automated Canopy Estimator (ACE) that overcomes many of these challenges to produce accurate estimates of fractional vegetation cover using an unsupervised segmentation process. ACE is shown to outperform nine other segmentation algorithms, consisting of both threshold-based and machine learning approaches, in the segmentation of photographs of four different crops (oat, corn, rapeseed and flax) with an overall accuracy of 89.6%. ACE is similarly accurate (88.7%) when applied to remotely sensed corn, producing FVC estimates that are strongly correlated with ground truth values.

Crop production in the Caribbean is dominated by small open field holdings that are almost totally reliant on rainfall. Sweet potato (Ipomoea batatas L. Lam. [Convolvulaceae]) has been identified as an important commodity to attain food and nutrition security goals of the region, particularly in light of a changing climate. The crop has high nutritional value, innate drought‐tolerant properties, and can be grown with relatively low inputs. The routine use of crop models for yield optimization is largely absent in the Caribbean. In this study, an attempt was made to parameterize the FAO AquaCrop model for sweet potato for the first time. AquaCrop is a simulation model for crop water productivity, designed primarily for use in irrigation management. Parameters were developed using data from three sweet potato cultivars grown in two agroecological zones in Jamaica under rainfed and irrigated conditions. Digital photography was combined with an automated canopy estimator to track canopy development, and sample harvesting was done throughout the crop season. The overall simulation of biomass was good, with deviations of <28% for four out of six simulations, and season‐long performance of the model was commendable. The simulation of yield presented more challenges, especially given the nonlinear rate of tuber development. The results, however, indicate that AquaCrop could be a useful tool for Caribbean agriculture in predicting the productivity of sweet potato under varying water availability.

Cassava (Manihot esculenta Crantz) is an important food crop, especially in developing countries, because of its resilience and ability to grow in conditions generally inhospitable for other crops. However, tropical crops like cassava are not as frequently modeled compared with crops from temperate locations. The objective of this research was to calibrate the CSM-MANIHOT-Cassava model of the Decision Support System for Agrotechnology Transfer, DSSAT beta v4.8 and use the model to evaluate the potential benefits of irrigation on yield. We established two field trials with two water treatments (rainfed and irrigated) and four cultivars that had not been studied previously. We simulated in-season biomass and end-of-season yield, evaluating the model performance with different statistical measures. There was good agreement between simulated and measured values; the best results showed a deviation of 9.7%, normalized RMSE of 18%, and d-index of 0.98 for biomass, with corresponding values of 11, 24, and 0.98, respectively, for yield. Good simulations of yield correlated with accurate simulations for leaf area index and harvest index. The varieties showed differential responses to irrigation, suggesting that there are diverse levels of drought tolerance even within the same environmental conditions.