The R package gdistance provides classes and functions to calculate various distance measures and routes in heterogeneous geographic spaces represented as grids. Least-cost distances as well as more complex distances based on (constrained) random walks can be calculated. Also the corresponding routes or probabilities of passing each cell can be determined. The package implements classes to store the data about the probability or cost of transitioning from one cell to another on a grid in a memory-efficient sparse format. These classes make it possible to manipulate the values of cell-to-cell movement directly, which offers flexibility and the possibility to use asymmetric values. The novel distances implemented in the package are used in geographical genetics (applying circuit theory), but also have applications in other fields of geospatial analysis.

Major leaps forward in understanding rice both in genetics and archaeology have taken place in the past decade or so-with the publication of full draft genomes for indica and japonica rice, on the one hand, and with the spread of systematic flotation and increased recovery of archaeological spikelet bases and other rice remains on early sites in China, India and Southeast Asia. This paper will sketch a framework that coherently integrates the evidence from these burgeoning fields. This framework implies a reticulate framework in the phylogeny of early cultivated rice, with multiple starts of cultivation (two is perhaps not enough) but with the key consolidations of adaptations that must have been spread through hybridisation and therefore long-distance cultural contacts.Archaeobotanical evidence allows us to document the gradual evolutionary process of domestication through rice spikelet bases and grain size change. Separate trends in grain size change can be identified in India and China. The earliest centre of rice domestication was in the Yangtze basin of China, but a largely separate trajectory into rice cultivation can be traced in the Ganges plains of India. Intriguingly, contact-induced hybridisation is indicated for the early development of indica in northern India, ca. 2000 BC. An updated synthesis of the interwoven patterns of the spread of various rice varieties throughout Asia and to Madagascar can be suggested in which rice reached most of its historical range of important cultivation by the Iron Age. The distribution of wild rice and genetic diversity in domesticated ricesRice is a highly diversified crop, being grown from the equator to over 40°N, from sea-level ca. 2,700 m in parts of the Himalayas and in a wide ecological range of cultivation systems. Although there is much less ecological variation found within its wild progenitor complex (Oryza rufipogon and Oryza nivara), these are nonetheless distributed over a wide geographical range and a spectrum of ecological niches from permanent to seasonal wetlands. As the origins of cultivation must have developed in places where hunter-gatherers were utilising wild populations, the distribution of the wild progenitor, in the past when cultivation began, is a key element in identifying the origins of rice. There are three lines of approach to inferring this without archaeological evidence, including (1) the Electronic supplementary material The online version of this article (

Addressing the global challenges of climate change, food security, and poverty alleviation requires enhancing the adaptive capacity and mitigation potential of agricultural landscapes across the tropics. However, adaptation and mitigation activities tend to be approached separately due to a variety of technical, political, financial, and socioeconomic constraints. Here, we demonstrate that many tropical agricultural systems can provide both mitigation and adaptation benefits if they are designed and managed appropriately and if the larger landscape context is considered. Many of the activities needed for adaptation and mitigation in tropical agricultural landscapes are the same needed for sustainable agriculture more generally, but thinking at the landscape scale opens a new dimension for achieving synergies. Intentional integration of adaptation and mitigation activities in agricultural landscapes offers significant benefits that go beyond the scope of climate change to food security, biodiversity conservation, and poverty alleviation. However, achieving these objectives will require transformative changes in current policies, institutional arrangements, and funding mechanisms to foster broad-scale adoption of climate-smart approaches in agricultural landscapes.

We review the origins and dispersal of rice in Asia based on a data base of 443 archaeobotanical reports. Evidence is considered in terms of quality, and especially whether there are data indicating the mode of cultivation, in flooded ('paddy' or 'wet') or non-flooded ('dry') fields. At present it appears that early rice cultivation in the Yangtze region and southern China was based on wet, paddy-field systems from early on, before 4000 bc, whereas early rice in northern India and Thailand was predominantly dry rice at 2000 bc, with a transition to flooded rice documented for India at c. 1000 bc. On the basis of these data we have developed a GIS spatial model of the spread of rice and the growth of land area under paddy rice. This is then compared with a review of the spread of ungulate livestock (cattle, water buffalo, sheep, goat) throughout the Old World. After the initial dispersal through Europe and around the Mediterranean (7000-4000 bc), the major period of livestock expansion is after 3000 bc, into the Sub-Saharan savannas, through monsoonal India and into central China. Further expansion, to southern Africa and Southeast Asia dates mostly after 1000 bc. Based on these two data sets we provide a quantitative model of the land area under irrigated rice, and its likely methane output, through the mid to late Holocene, for comparison to a more preliminary estimate of the expansion of methane-producing livestock. Both data sets are congruent with an anthropogenic source of later Holocene methane after 3000 bc, although it may be that increase in methane input from livestock was most significant in the 3000-1000 bc period, whereas rice paddies become an increasingly significant source especially after 2000 bc.

Cacao (Theobroma cacao L.) is indigenous to the Amazon basin, but is generally believed to have been domesticated in Mesoamerica for the production of chocolate beverage. However, cacao’s distribution of genetic diversity in South America is also likely to reflect pre-Columbian human influences that were superimposed on natural processes of genetic differentiation. Here we present the results of a spatial analysis of the intra-specific diversity of cacao in Latin America, drawing on a dataset of 939 cacao trees genotypically characterized by means of 96 SSR markers. To assess continental diversity patterns we performed grid-based calculations of allelic richness, Shannon diversity and Nei gene diversity, and distinguished different spatially coherent genetic groups by means of cluster analysis. The highest levels of genetic diversity were observed in the Upper Amazon areas from southern Peru to the Ecuadorian Amazon and the border areas between Colombia, Peru and Brazil. On the assumption that the last glaciation (22,000–13,000 BP) had the greatest pre-human impact on the current distribution and diversity of cacao, we modeled the species’ Pleistocene niche suitability and overlaid this with present-day diversity maps. The results suggest that cacao was already widely distributed in the Western Amazon before the onset of glaciation. During glaciations, cacao populations were likely to have been restricted to several refugia where they probably underwent genetic differentiation, resulting in a number of genetic clusters which are representative for, or closest related to, the original wild cacao populations. The analyses also suggested that genetic differentiation and geographical distribution of a number of other clusters seem to have been significantly affected by processes of human management and accompanying genetic bottlenecks. We discuss the implications of these results for future germplasm collection and in situ, on farm and ex situ conservation of cacao.

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Achieving climate smart agriculture depends on understanding the links between farming and livelihood practices, other possible adaptation options, and the effects on farm performance, which is conceptualised by farmers as wider than yields. Reliable indicators of farm performance are needed in order to model these links, and to therefore be able to design interventions which meet the differing needs of specific user groups. However, the lack of standardization of performance indicators has led to a wide array of tools and ad-hoc indicators which limit our ability to compare across studies and to draw general conclusions on relationships and trade-offs whereby performance indicators are shaped by farm management and the wider socialenvironmental context. RHoMIS is a household survey tool designed to rapidly characterise a series of standardised indicators across the spectrum of agricultural production and market integration, nutrition, food security, poverty and GHG emissions. The survey tool takes 40-60 minutes to administer per household using a digital implementation platform. This is linked to a set of automated analysis procedures that enable immediate cross-site benchmarking and intra-site characterisation. We trialled the survey in two contrasting agro-ecosystems, in Lushoto district of Tanzania (n=151) and in the Trifinio border region of Guatemala, El Salvador and Honduras (n=285). The tool rapidly characterised variability between farming systems at landscape scales in both locations identifying key differences across the population of farm households that would be critical for targeting CSA interventions. Our results suggest that at both sites the climate smartness of different farm strategies is clearly determined by an interaction between the characteristics of the farm household and the farm strategy. In general strategies that enabled production intensification contributed more towards the goals of climate smart agriculture on smaller farms, whereas increased market orientation was more successful on larger farms. On small farms off-farm income needs to be in place before interventions can be promoted successfully, whereas on the larger farms a choice is made between investing labour in off-farm incomes, or investing that the labour into the farm, resulting in a negative association between off-farm labour and intensification, market orientation and crop diversity on the larger farms, which is in complete opposition to the associations found for the smaller farms. The balance of indicators selected gave an adequate snap shot picture of the two sites, and allowed us to appraise the 'CSA-ness' of different existing farm strategies, within the context of other major development objectives.

Crop adaptation to climate change requires accelerated crop variety introduction accompanied by recommendations to help farmers match the best variety with their field contexts. Existing approaches to generate these recommendations lack scalability and predictivity in marginal production environments. We tested if crowdsourced citizen science can address this challenge, producing empirical data across geographic space that, in aggregate, can characterize varietal climatic responses. We present the results of 12,409 farmer-managed experimental plots of common bean (Phaseolus vulgarisL.) in Nicaragua, durum wheat (Triticum durumDesf.) in Ethiopia, and bread wheat (Triticum aestivumL.) in India. Farmers collaborated as citizen scientists, each ranking the performance of three varieties randomly assigned from a larger set. We show that the approach can register known specific effects of climate variation on varietal performance. The prediction of variety performance from seasonal climatic variables was generalizable across growing seasons. We show that these analyses can improve variety recommendations in four aspects: reduction of climate bias, incorporation of seasonal climate forecasts, risk analysis, and geographic extrapolation. Variety recommendations derived from the citizen science trials led to important differences with previous recommendations.