Ian Macadam

Adapting agriculture to climate change: a review

The agricultural sector is highly vulnerable to future climate changes and climate variability, including increases in the incidence of extreme climate events. Changes in temperature and precipitation will result in changes in land and water regimes that will subsequently affect agricultural productivity. Given the gradual change of climate in the past, historically, farmers have adapted in an autonomous manner. However, with large and discrete climate change anticipated by the end of this century, planned and transformational changes will be needed. In light of these, the focus of this review is on farm-level and farmers responses to the challenges of climate change both spatially and over time. In this review of adapting agriculture to climate change, the nature, extent, and causes of climate change are analyzed and assessed. These provide the context for adapting agriculture to climate change. The review identifies the binding constraints to adaptation at the farm level. Four major priority areas are identified to relax these constraints, where new initiatives would be required, i.e., information generation and dissemination to enhance farm-level awareness, research and development (R&D) in agricultural technology, policy formulation that facilitates appropriate adaptation at the farm level, and strengthening partnerships among the relevant stakeholders. Forging partnerships among R&D providers, policy makers, extension agencies, and farmers would be at the heart of transformational adaptation to climate change at the farm level. In effecting this transformational change, sustained efforts would be needed for the attendant requirements of climate and weather forecasting and innovation, farmer’s training, and further research to improve the quality of information, invention, and application in agriculture. The investment required for these would be highly significant. The review suggests a sequenced approach through grouping research initiatives into short-term, medium-term, and long-term initiatives, with each initiative in one stage contributing to initiatives in a subsequent stage. The learning by doing inherent in such a process-oriented approach is a requirement owing to the many uncertainties associated with climate change.

Multi-model ensemble projections of future extreme temperature change using a statistical downscaling method in south eastern Australia

Projections of changes in temperature extremes are critical to assess the potential impacts of climate change on agricultural and ecological systems. Statistical downscaling can be used to efficiently downscale output from a large number of general circulation models (GCMs) to a fine temporal and spatial scale, providing the opportunity for future projections of extreme temperature events. This paper presents an analysis of extreme temperature data downscaled from 7 GCMs selected from the Coupled Model Intercomparison Project phase 5 (CMIP5) using a skill score based on spatial patterns of climatological means of daily maximum and minimum temperature. Data for scenarios RCP4.5 and RCP8.5 for the New South Wales (NSW) wheat belt, south eastern Australia, have been analysed. The results show that downscaled data from most of the GCMs reproduces the correct sign of recent trends in all the extreme temperature indices (except the index for cold days) for 1961–2000. An independence weighted mean method is used to calculate uncertainty estimates, which shows that multi-model ensemble projections produce a consistent trend compared to the observations in most extreme indices. Great warming occurs in the east and northeast of the NSW wheat belt by 2061–2100 and increases the risk of exposure to hot days around wheat flowering date, which might result in farmers needing to reconsider wheat varieties suited to maintain yield. This analysis provides a first overview of projected changes in climate extremes from an ensemble of 7 CMIP5 GCM models with statistical downscaling data in the NSW wheat belt.

Australian wheat production expected to decrease by the late 21st century

Climate change threatens global wheat production and food security, including the wheat industry in Australia. Many studies have examined the impacts of changes in local climate on wheat yield per hectare, but there has been no assessment of changes in land area available for production due to changing climate. It is also unclear how total wheat production would change under future climate when autonomous adaptation options are adopted. We applied species distribution models to investigate future changes in areas climatically suitable for growing wheat in Australia. A crop model was used to assess wheat yield per hectare in these areas. Our results show that there is an overall tendency for a decrease in the areas suitable for growing wheat and a decline in the yield of the northeast Australian wheat belt. This results in reduced national wheat production although future climate change may benefit South Australia and Victoria. These projected outcomes infer that similar wheat-growing regions of the globe might also experience decreases in wheat production. Some cropping adaptation measures increase wheat yield per hectare and provide significant mitigation of the negative effects of climate change on national wheat production by 2041-2060. However, any positive effects will be insufficient to prevent a likely decline in production under a high CO2 emission scenario by 2081-2100 due to increasing losses in suitable wheat-growing areas. Therefore, additional adaptation strategies along with investment in wheat production are needed to maintain Australian agricultural production and enhance global food security. This scenario analysis provides a foundation towards understanding changes in Australias wheat cropping systems, which will assist in developing adaptation strategies to mitigate climate change impacts on global wheat production.

High-resolution regional climate model projections of future tropical cyclone activity in the Philippines

Funding information UK Department for International Development The Philippines is one of the most exposed countries in the world to tropical cyclones. In order to provide information to help the country build resilience and plan for a future under a warmer climate, we build on previous research to investigate implications of future climate change on tropical cyclone activity in the Philippines. Experiments were conducted using three regional climate models with horizontal resolutions of approximately 12 km (HadGEM3-RA) and 25 km (HadRM3P and RegCM4). The simulations are driven by boundary data from a subset of global climate model simulations from the CMIP5 ensemble. Here we present the experimental design, the methodology for selecting CMIP5 models, the results of the model validation, and future projections of changes to tropical cyclone frequency and intensity by the mid-21st century. The models used are shown to represent the key climatological features of tropical cyclones across the domain, including the seasonality and general distribution of intensities, but issues remain in resolving very intense tropical cyclones and simulating realistic trajectories across their life-cycles. Acknowledging model inadequacies and uncertainties associated with future climate model projections, the results show a range of plausible changes with a tendency for fewer but slightly more intense tropical cyclones. These results are consistent with the basin-wide results reported in the IPCC AR5 and provide clear evidence that the findings from these previous studies are applicable in the Philippines region.

Impacts of rainfall extremes on wheat yield in semi-arid cropping systems in eastern Australia

Investigating the relationships between climate extremes and crop yield can help us understand how unfavourable climatic conditions affect crop production. In this study, two statistical models, multiple linear regression and random forest, were used to identify rainfall extremes indices affecting wheat yield in three different regions of the New South Wales wheat belt. The results show that the random forest model explained 41–67% of the year-to-year yield variation, whereas the multiple linear regression model explained 34–58%. In the two models, 3-month timescale standardized precipitation index of Jun.–Aug. (SPIJJA), Sep.–Nov. (SPISON), and consecutive dry days (CDDs) were identified as the three most important indices which can explain yield variability for most of the wheat belt. Our results indicated that the inter-annual variability of rainfall in winter and spring was largely responsible for wheat yield variation, and pre-growing season rainfall played a secondary role. Frequent shortages of rainfall posed a greater threat to crop growth than excessive rainfall in eastern Australia. We concluded that the comparison between multiple linear regression and machine learning algorithm proposed in the present study would be useful to provide robust prediction of yields and new insights of the effects of various rainfall extremes, when suitable climate and yield datasets are available.