Climate change and variability impacts on grazing herds: Insights from a system dynamics approach for semi-arid Australian rangelands.

Grazing livestock are an important source of food and income for millions of people worldwide. Changes in mean climate and increasing climate variability are affecting grasslands' carrying capacity, thus threatening the livelihood of millions of people as well as the health of grassland ecosystems. Compared with cropping systems, relatively little is known about the impact of such climatic changes on grasslands and livestock productivity and the adaptation responses available to farmers. In this study, we analysed the relationship between changes in mean precipitation, precipitation variability, farming practices and grazing cattle using a system dynamics approach for a semi-arid Australian rangeland system. We found that forage production and animal stocking rates were significantly affected by drought intensities and durations as well as by long-term climate trends. After a drought event, herd size recovery times ranged from years to decades in the absence of proactive restocking through animal purchases. Decreases in the annual precipitation means or increases in the interannual (year-to-year) and intra-annual (month-to-month) precipitation variability, all reduced herd sizes. The contribution of farming practices versus climate effect on herd dynamics varied depending on the herd characteristics considered. Climate contributed the most to the variance in stocking rates, followed by forage productivity levels and feeding supplementation practices (with or without urea and molasses). While intensification strategies and favourable climates increased long-term herd sizes, they also resulted in larger reductions in animal numbers during droughts and raised total enteric methane emissions. In the face of future climate trends, the grazing sector will need to increase its adaptability. Understanding which farming strategies can be beneficial, where, and when, as well as the enabling mechanisms required to implement them, will be critical for effectively improving rangelands and the livelihoods of pastoralists worldwide.

Yield gap analyses to estimate attainable bovine milk yields and evaluate options to increase production in Ethiopia and India.

Livestock provides an important source of income and nourishment for around one billion rural households worldwide. Demand for livestock food products is increasing, especially in developing countries, and there are opportunities to increase production to meet local demand and increase farm incomes. Estimating the scale of livestock yield gaps and better understanding factors limiting current production will help to define the technological and investment needs in each livestock sector. The aim of this paper is to quantify livestock yield gaps and evaluate opportunities to increase dairy production in Sub-Saharan Africa and South Asia, using case studies from Ethiopia and India. We combined three different methods in our approach. Benchmarking and a frontier analysis were used to estimate attainable milk yields based on survey data. Household modelling was then used to simulate the effects of various interventions on dairy production and income. We tested interventions based on improved livestock nutrition and genetics in the extensive lowland grazing zone and highland mixed crop-livestock zones of Ethiopia, and the intensive irrigated and rainfed zones of India. Our analyses indicate that there are considerable yield gaps for dairy production in both countries, and opportunities to increase production using the interventions tested. In some cases, combined interventions could increase production past currently attainable livestock yields.

Closing yield gaps in smallholder goat production systems in Ethiopia and India.

Small ruminants such as goats are an important source of income for smallholder farmers in South Asia and Sub Saharan Africa: they may be kept as a stepping stone to owning larger and higher-value animals such as cattle or buffalo, or provide a more-profitable and less-risky alternative in marginal or densely populated areas where access to feed resources are limited. However, smallholder goat production in these areas is often low due to low growth and reproduction rates and high animal mortality. The aim of this study was to investigate the potential for different intervention packages to increase yields and profitability of goat meat production in Ethiopia and India. Packages were based on improved nutrition, reduced flock mortality from improved control of health and diseases, and replacing indigenous livestock with improved goat breeds. Household modelling was used to simulate the effects of interventions on goat production and household income in the extensive lowland grazing zone and highland mixed crop-livestock zones of Ethiopia, and the extensive arid zone of India. Our analysis showed that there are opportunities to increase goat meat production in both countries. Reproduction, liveweight gain and survival rates can be increased through better nutrition, genetics and healthcare, but the biggest increase in production and profits occurred when multiple interventions were combined. Importantly, interventions resulting in the biggest increases in goat meat production or number of animals sold did not always give the highest profits.

Assessing the sustainable development and intensification potential of beef cattle production in Sumbawa, Indonesia, using a system dynamics approach.

The intensification of beef cattle production in dryland areas of East Indonesia has the potential to substantially raise the incomes of smallholder farmers that dominate the sector. In this study we assess the potential for intensifying beef production on Sumbawa Island, by introducing a household feedlot production system (2-20 animals) based on the Leucaena leucocephala (leucanea) tree legume as an improved source of feed. We used a system dynamics approach to model the entire value chain, accounting for herd dynamics, demand dynamics and seasonality. Our findings complement the growing body of biophysical evidence about the potential success of this intervention, by simulating improvements in the annual profitability for beef farmers in the project area of up to 415% by 2023. Increases in farm profit were shown to depend near equally on the higher productivity of the leucaena feeding system and an associated price premium, demonstrating the importance of supporting improved agricultural production with better marketing practices. The intervention was also shown to generate positive or neutral benefits for the main post-farm value chain actors. Importantly, it also reduced the GHG emission intensity of outputs from the beef herd by 16% by 2020. We explored number of scale-out pathways, including a relatively moderate pace of autonomous adoption for our main analysis, resulting in the accumulation of 3,444 hectares of leucaena 20-years after the initial project phase, which could sustain the fattening of 37,124 male cattle per year. More ambitious rates of scale-out were found to be possible without exceeding the animal and land resources of the island.

GTfold: enabling parallel RNA secondary structure prediction on multi-core desktops.

BACKGROUND: Accurate and efficient RNA secondary structure prediction remains an important open problem in computational molecular biology. Historically, advances in computing technology have enabled faster and more accurate RNA secondary structure predictions. Previous parallelized prediction programs achieved significant improvements in runtime, but their implementations were not portable from niche high-performance computers or easily accessible to most RNA researchers. With the increasing prevalence of multi-core desktop machines, a new parallel prediction program is needed to take full advantage of today's computing technology. FINDINGS: We present here the first implementation of RNA secondary structure prediction by thermodynamic optimization for modern multi-core computers. We show that GTfold predicts secondary structure in less time than UNAfold and RNAfold, without sacrificing accuracy, on machines with four or more cores. CONCLUSIONS: GTfold supports advances in RNA structural biology by reducing the timescales for secondary structure prediction. The difference will be particularly valuable to researchers working with lengthy RNA sequences, such as RNA viral genomes.