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Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve.

Anaerobic digestion is a commercial reality for several kinds of waste. Nonetheless, anaerobic digestion of single substrates presents some drawbacks linked to substrate characteristics. Anaerobic co-digestion, the simultaneous digestion of two or more substrates, is a feasible option to overcome the drawbacks of mono-digestion and to improve plants economic feasibility. At present, since 50% of the publication has been published in the last two years, anaerobic co-digestion can be considered the most relevant topic within anaerobic digestion research. The aim of this paper is to present a review of the achievements and perspectives of anaerobic co-digestion within the period 2010-2013, which represents a continuation of the previous review made by the authors [3]. In the present review, the publications have been classified as for the main substrate, i.e., animal manures, sewage sludge and biowaste. Animal manures stand as the most reported substrate, agro-industrial waste and the organic fraction of the municipal solid waste being the most reported co-substrate. Special emphasis has been made to the effect of the co-digestion over digestate quality, since land application seems to be the best option for digestate recycling. Traditionally, anaerobic co-digestion between sewage sludge and the organic fraction of the municipal solid waste has been the most reported co-digestion mixture. However, between 2010 and 2013 the publications dealing with fats, oils and greases and algae as sludge co-substrate have increased. This is because both co-substrates can be obtained at the same wastewater treatment plant. In contrast, biowaste as a main substrate has not been as studied as manures or sewage sludge. Finally, three interdisciplinary sections have been written for addressing novelty aspects in anaerobic co-digestion, i.e., pre-treatments, microbial dynamics and modeling. However, much effort needs to be done in these later aspects to better understand and predict anaerobic co-digestion.

This paper describes a process-based metapopulation dynamics and phenology model of prickly acacia, Acacia nilotica, an invasive alien species in Australia. The model, SPAnDX, describes the interactions between riparian and upland sub-populations of A. nilotica within livestock paddocks, including the effects of extrinsic factors such as temperature, soil moisture availability and atmospheric concentrations of carbon dioxide. The model includes the effects of management events such as changing the livestock species or stocking rate, applying fire, and herbicide application. The predicted population behaviour of A. nilotica was sensitive to climate. Using 35 years daily weather datasets for five representative sites spanning the range of conditions that A. nilotica is found in Australia, the model predicted biomass levels that closely accord with expected values at each site. SPAnDX can be used as a decision-support tool in integrated weed management, and to explore the sensitivity of cultural management practices to climate change throughout the range of A. nilotica. The cohort-based DYMEX modelling package used to build and run SPAnDX provided several advantages over more traditional population modelling approaches (e.g. an appropriate specific formalism (discrete time, cohort-based, process-oriented), user-friendly graphical environment, extensible library of reusable components, and useful and flexible input/output support framework).

AbstractTechnology developments have made significant impacts on both humans and the environment in which they live. However, there is limited whole-of-system understanding of ancient technology development. This paper aims to uncover the evolutionary pattern of the ancient Chinese agricultural technology system that focused on land and water mobilisations from 8000 BC to 1911 AD. Our findings show that agricultural technology in China transitioned through an extremely slow, S-shaped pathway, increasing only ten fold in over 8000 years. The technology system was initially driven by tangible tools (40% of growth), then by technological theories and practices that contributed more than 50% of growth. Its development was spatially inclined to the Yellow River then to the Yangtze River region, where over 45% of technologies were developed. This study provides an empirical baseline for comparative studies between pre-industrial and industrial technologies. Greater understanding of the mechanisms of technology development will be required to reorientate technology development for present and future generations.

Tanzania's dairy sector is poorly developed, creating reliance on imports for processed, value-added dairy products and threatening food security, particularly when supply chains are disrupted due to market volatility or armed conflicts. The Tanzanian Dairy Development Roadmap is a domestic development initiative that aims to achieve dairy self-sufficiency by 2030. Here, we model different outcomes of the roadmap, finding that adoption of high-yield cattle breeds is essential for reducing dairy import dependency. Avoided land use change resulting from fewer, higher yielding dairy cattle would lead to lower greenhouse gas emissions. Dairy producers' average incomes could increase despite capital expenditure and land allocation required for the adoption of high-yield breeds. Our findings demonstrate the importance of bottom-up development policies for sustainable food system transformations, which also support food sovereignty, increase incomes for smallholder farmers and contribute towards Tanzania's commitments to reduce greenhouse gas emissions.

The objective of this study was to assess the impacts of climate change and salinity on the economic viability of rice-based cropping systems under farmers' current management across current and future climate and salinity scenarios in south-west coastal Bangladesh. Detailed case studies were conducted in two contrasting coastal villages in Dacope Sub-district, Khulna District. Enterprise budgets were developed using APSIM-simulated and extrapolated yields together with crop management, cost, and price data obtained from the villages and estimated from various sources. The projected impact of climate change and salinization on the economic viability (profitability and riskiness) of most cropping systems was not pronounced. Thus rice-based cropping systems are likely to remain viable in both optimistic and pessimistic climate scenarios in coming decades, even allowing for salinization, because some of the positive effects of climate change were projected to offset the sizeable losses due to salinity. Moreover, where small yield declines were projected these were often offset by higher future prices. Sustainably-managed rice/shrimp cropping systems are likely to remain the most profitable option in locations with access to tidal saline water. In other sites, given adequate freshwater for irrigation in the dry season, rice/non-rice cropping systems were projected to be the most viable options, especially incorporating newer crops such as sunflower and maize. Dry-season rice and wheat were not projected to be viable options.

Cocoa beans (Theobroma cacao L.) are the raw material for chocolate production. Fermentation of cocoa pulp by microorganisms is crucial for developing chocolate flavor precursors. Yeasts conduct an alcoholic fermentation within the bean pulp that is essential for the production of good quality beans, giving typical chocolate characters. However, the roles of bacteria such as lactic acid bacteria and acetic acid bacteria in contributing to the quality of cocoa bean and chocolate are not fully understood. Using controlled laboratory fermentations, this study investigated the contribution of lactic acid bacteria to cocoa bean fermentation. Cocoa beans were fermented under conditions where the growth of lactic acid bacteria was restricted by the use of nisin and lysozyme. The resultant microbial ecology, chemistry and chocolate quality of beans from these fermentations were compared with those of indigenous (control) fermentations. The yeasts Hanseniaspora guilliermondii, Pichia kudriavzevii, Kluyveromyces marxianus and Saccharomyces cerevisiae, the lactic acid bacteria Lactobacillus plantarum, Lactobacillus pentosus and Lactobacillus fermentum and the acetic acid bacteria Acetobacter pasteurianus and Gluconobacter frateurii were the major species found in control fermentations. In fermentations with the presence of nisin and lysozyme, the same species of yeasts and acetic acid bacteria grew but the growth of lactic acid bacteria was prevented or restricted. These beans underwent characteristic alcoholic fermentation where the utilization of sugars and the production of ethanol, organic acids and volatile compounds in the bean pulp and nibs were similar for beans fermented in the presence of lactic acid bacteria. Lactic acid was produced during both fermentations but more so when lactic acid bacteria grew. Beans fermented in the presence or absence of lactic acid bacteria were fully fermented, had similar shell weights and gave acceptable chocolates with no differences in sensory rankings. It was concluded that lactic acid bacteria may not be necessary for successful cocoa fermentation.