Antonio Trabucco

Towards domestication of Jatropha curcas

Jatropha curcas L. attracts a lot of interest as a biofuel crop, triggering large investments and rapid expansion of cultivation areas, and yet, it should still be considered as a (semi-)wild, undomesticated plant. To use the full potential of Jatropha and to support further expansion and systematic selection, breeding and domestication are a prerequisite. This review reveals and identifies gaps in knowledge that still impede domestication of Jatropha. Prebreeding knowledge is limited. In particular, the regeneration ecology and the degree of genetic diversity among and within natural populations in and outside the center of origin are poorly studied. There is only a limited understanding of the Jatropha breeding system and the effect of inbreeding and outbreeding. This review presents all currently available and relevant information on the species distribution, site requirements, regeneration ecology, genetic diversity, advances in selection, development of varieties and hybridization. It also describes possible routes to a better Jatropha germplasm, gives recommendations for tackling current problems and provides guidance for future research. We also discuss the participatory domestication strategy of Jatropha integration in agroforestry.

Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing.

Recent advances in remote sensing provide opportunities to map plant species and vegetation within wetlands at management relevant scales and resolutions. Hyperspectral imagers, currently available on airborne platforms, provide increased spectral resolution over existing space-based sensors that can document detailed information on the distribution of vegetation community types, and sometimes species. Development of spectral libraries of wetland species is a key component needed to facilitate advanced analytical techniques to monitor wetlands. Canopy and leaf spectra at five sites in California, Texas, and Mississippi were sampled to create a common spectral library for mapping wetlands from remotely sensed data. An extensive library of spectra (n=1336) for coastal wetland communities, across a range of bioclimatic, edaphic, and disturbance conditions were measured. The wetland spectral libraries were used to classify and delineate vegetation at a separate location, the Pacheco Creek wetland in the Sacramento Delta, California, using a PROBE-1 airborne hyperspectral data set (5m pixel resolution, 128 bands). This study discusses sampling and collection methodologies for building libraries, and illustrates the potential of advanced sensors to map wetland composition. The importance of developing comprehensive wetland spectral libraries, across diverse ecosystems is highlighted. In tandem with improved analytical tools these libraries provide a physical basis for interpretation that is less subject to conditions of specific data sets. To facilitate a global approach to the application of hyperspectral imagers to mapping wetlands, we suggest that criteria for and compilation of wetland spectral libraries should proceed today in anticipation of the wider availability and eventual space-based deployment of advanced hyperspectral high spatial resolution sensors.

Global mapping of Jatropha curcas yield based on response of fitness to present and future climate.

Although acclaimed as a biofuel crop with high potential to sustainably replace fossil fuels, Jatropha curcas L. remains a poorly studied plant. Reliable yield assessments with conventional methods require agroclimatic and physiological knowledge, which is not yet available for Jatropha. To fill this gap, we tested a novel two‐step approach integrating knowledge from biogeography and population biology with available Jatropha field data. In the first step, using MaxEnt, a widely implemented model in biogeography, we predicted Jatropha fitness in response to climate by relating natural occurrence recorded in herbaria with bioclimatic geodatasets. In the second step, we relied on population biology principles supported by seed mass addition experiments to relate fitness to reproductive potential, hence seed yield. Jatropha seed yield in response to climate was mapped worldwide for actual (1950–2000 average) and future (2020) climate conditions. The modelled Jatropha seed yield was validated against a set of on‐field yield assessments (R2=0.67, P<0.001). The discrepancies between estimated and measured yields were partially explained by model uncertainties, as quantified by the sensitivity analysis of our modelling (R2=0.57, P=0.001). Jatropha has a pan‐tropical distribution, plus specific adaptability to hot temperate areas. Climate variables most significantly affecting modelled yield response were annual average temperature, minimum temperature, annual precipitation and precipitation seasonality.

Global tree cover and biomass carbon on agricultural land: The contribution of agroforestry to global and national carbon budgets

Agroforestry systems and tree cover on agricultural land make an important contribution to climate change mitigation, but are not systematically accounted for in either global carbon budgets or national carbon accounting. This paper assesses the role of trees on agricultural land and their significance for carbon sequestration at a global level, along with recent change trends. Remote sensing data show that in 2010, 43% of all agricultural land globally had at least 10% tree cover and that this has increased by 2% over the previous ten years. Combining geographically and bioclimatically stratified Intergovernmental Panel on Climate Change (IPCC) Tier 1 default estimates of carbon storage with this tree cover analysis, we estimated 45.3 PgC on agricultural land globally, with trees contributing >75%. Between 2000 and 2010 tree cover increased by 3.7%, resulting in an increase of >2 PgC (or 4.6%) of biomass carbon. On average, globally, biomass carbon increased from 20.4 to 21.4 tC ha−1. Regional and country-level variation in stocks and trends were mapped and tabulated globally, and for all countries. Brazil, Indonesia, China and India had the largest increases in biomass carbon stored on agricultural land, while Argentina, Myanmar, and Sierra Leone had the largest decreases.

Global greenhouse gas implications of land conversion to biofuel crop cultivation in arid and semi-arid lands: lessons learned from Jatropha

Biofuels are considered as a climate-friendly energy alternative. However, their environmental sustainability is increasingly debated because of land competition with food production, negative carbon balances and impacts on biodiversity. Arid and semi-arid lands have been proposed as a more sustainable alternative without such impacts. In that context this paper evaluates the carbon balance of potential land conversion to Jatropha cultivation, biofuel production and use in arid and semi-arid areas. This evaluation includes the calculation of carbon debt created by these land conversions and calculation of the minimum Jatropha yield necessary to repay the respective carbon debts within 15 or 30 years. The carbon debts caused by conversion of arid and semi-arid lands to Jatropha vary largely as a function of the biomass carbon stocks of the land use types in these regions. Based on global ecosystem carbon mapping, cultivated lands and marginal areas (sparse shrubs, herbaceous and bare areas) show to have similar biomass carbon stocks (on average 4e 8tCh a � 1 ) and together cover a total of 1.79 billion ha.

Pan-Tropical Analysis of Climate Effects on Seasonal Tree Growth

Climate models predict a range of changes in tropical forest regions, including increased average temperatures, decreased total precipitation, reduced soil moisture and alterations in seasonal climate variations. These changes are directly related to the increase in anthropogenic greenhouse gas concentrations, primarily CO2. Assessing seasonal forest growth responses to climate is of utmost importance because woody tissues, produced by photosynthesis from atmospheric CO2, water and light, constitute the main component of carbon sequestration in the forest ecosystem. In this paper, we combine intra-annual tree growth measurements from published tree growth data and the corresponding monthly climate data for 25 pan-tropical forest sites. This meta-analysis is designed to find the shared climate drivers of tree growth and their relative importance across pan-tropical forests in order to improve carbon uptake models in a global change context. Tree growth reveals significant intra-annual seasonality at seasonally dry sites or in wet tropical forests. Of the overall variation in tree growth, 28.7% was explained by the site effect, i.e. the tree growth average per site. The best predictive model included four climate variables: precipitation, solar radiation (estimated with extrasolar radiation reaching the atmosphere), temperature amplitude and relative soil water content. This model explained more than 50% of the tree growth variations across tropical forests. Precipitation and solar radiation are the main seasonal drivers of tree growth, causing 19.8% and 16.3% of the tree growth variations. Both have a significant positive association with tree growth. These findings suggest that forest productivity due to tropical tree growth will be reduced in the future if climate extremes, such as droughts, become more frequent.

The future distribution of the savannah biome: model-based and biogeographic contingency.

The extent of the savannah biome is expected to be profoundly altered by climatic change and increasing atmospheric CO2 concentrations. Contrasting projections are given when using different modelling approaches to estimate future distributions. Furthermore, biogeographic variation within savannahs in plant function and structure is expected to lead to divergent responses to global change. Hence the use of a single model with a single savannah tree type will likely lead to biased projections. Here we compare and contrast projections of South American, African and Australian savannah distributions from the physiologically based Thornley transport resistance statistical distribution model (TTR-SDM)—and three versions of a dynamic vegetation model (DVM) designed and parametrized separately for specific continents. We show that attempting to extrapolate any continent-specific model globally biases projections. By 2070, all DVMs generally project a decrease in the extent of savannahs at their boundary with forests, whereas the TTR-SDM projects a decrease in savannahs at their boundary with aridlands and grasslands. This difference is driven by forest and woodland expansion in response to rising atmospheric CO2 concentrations in DVMs, unaccounted for by the TTR-SDM. We suggest that the most suitable models of the savannah biome for future development are individual-based dynamic vegetation models designed for specific biogeographic regions. This article is part of the themed issue ‘Tropical grassy biomes: linking ecology, human use and conservation’.

Evaluating the applicability of using daily forecasts from seasonal prediction systems (SPSs) for agriculture: a case study of Nepal’s Terai with the NCEP CFSv2

Ensemble forecasts from dynamic seasonal prediction systems (SPSs) have the potential to improve decision-making for crop management to help cope with interannual weather variability. Because the reliability of crop yield predictions based on seasonal weather forecasts depends on the quality of the forecasts, it is essential to evaluate forecasts prior to agricultural applications. This study analyses the potential of Climate Forecast System version 2 (CFSv2) in predicting the Indian summer monsoon (ISM) for producing meteorological variables relevant to crop modeling. The focus area was Nepal’s Terai region, and the local hindcasts were compared with weather station and reanalysis data. The results showed that the CFSv2 model accurately predicts monthly anomalies of daily maximum and minimum air temperature (Tmax and Tmin) as well as incoming total surface solar radiation (Srad). However, the daily climatologies of the respective CFSv2 hindcasts exhibit significant systematic biases compared to weather station data. The CFSv2 is less capable of predicting monthly precipitation anomalies and simulating the respective intra-seasonal variability over the growing season. Nevertheless, the observed daily climatologies of precipitation fall within the ensemble spread of the respective daily climatologies of CFSv2 hindcasts. These limitations in the CFSv2 seasonal forecasts, primarily in precipitation, restrict the potential application for predicting the interannual variability of crop yield associated with weather variability. Despite these limitations, ensemble averaging of the simulated yield using all CFSv2 members after applying bias correction may lead to satisfactory yield predictions.

Anticipating Climatic Variability: The Potential of Ecological Calendars

Indigenous and rural societies who have contributed least to anthropogenic climate change are facing its harshest consequences. One of the greatest challenges of climate change is lack of predictability, especially at the local scale. An estimated 70-80% of the world’s food is produced by smallholders with less than two hectares of land (FAO 2014; Lowder et al. 2016). These small-scale farmers and herders face an ever-shifting ‘new normal’ climate, increasing inconsistency in the seasonality of temperature and precipitation, and higher frequency of what were once considered extreme weather events (Jolly et al. 2002; Thornton et al. 2014). Climate variability is disrupting food systems and generating a debilitating anxiety (Carroll et al. 2009; Kassam 2009a,b; Coyle and Susteren 2011; UN Human Rights Council 2016). Anticipatory capacity – the ability to envision possible futures and develop a plan of action to deal with uncertainties – is needed urgently (Tschakert and Dietrich 2010). Communities and researchers must create innovative systems to recognize and respond to climate trends and prepare for a greater range of possible scenarios (Reid et al. 2014; Cuerrier et al. 2015). To build anticipatory capacity for climate change, communities need systems that are effective at the scale of the village and valley (Berkes and Jolly 2001; Downing and Cuerrier 2011). While climate scientists have increased model capabilities to make more accurate projections of global climate conditions, the uncertainties of global climate modeling together with those of downscaling methods means that these models are not always reliable at regional and local scales (Salick and Ross 2009). Synergy between indigenous ecological knowledge and climate science has already benefitted many local communities, as well as international understanding of climate change drivers and impacts (Jolly et al. 2002; Nickels et al. 2005; Nyong et al. 2007; Kassam 2009a; Alexander et al. 2011; Boillat and Berkes 2013; Rapinski et al. 2017;). Similarly, ground-truthing climate models with indigenous ecological knowledge can be used to refine downscaling methods and to inform planning and policies at local, regional, and national levels. Projections of climate models are least accurate within mountainous regions, where weather stations are scarce and rugged topographies dramatically alter climate patterns (Hall 2014). In addition, significant environmental degradation in many mountain regions, such as reduction of vegetation cover due to overgrazing or hydrological transformations resulting from road and dam construction, are obscuring the entangled effects of climate change. Nevertheless, food producers in these remote regions require the ability to anticipate patterns of temperature, precipitation, and runoff from glaciers and snowfields. Many indigenous and rural societies have developed unique systems to recognize and respond to climatic trends and variability. Over the course of multiple generations living in particular landscapes, indigenous people have accumulated knowledge of the relative timing of celestial, meteorological, and ecological phenomena. Understanding these relationships has enabled these communities to anticipate weather and other seasonal processes, and thereby coordinate their livelihood activities (Acharya 2011; Turner and Singh 2011). However, indigenous knowledge systems have suffered centuries of disruption and destruction as a result of colonialism, violent conflicts, and loss of land. Global climate change introduces unprecedented rates and magnitudes of change, exacerbating existing inequities (Turner and Clifton 2009). Although * Karim-Aly S. Kassam ksk28@cornell.edu

Coexistence trend contingent to Mediterranean oaks with different leaf habits

Abstract In a previous work we developed a mathematical model to explain the co‐occurrence of evergreen and deciduous oak groups in the Mediterranean region, regarded as one of the distinctive features of Mediterranean biodiversity. The mathematical analysis showed that a stabilizing mechanism resulting from niche difference (i.e. different water use and water stress tolerance) between groups allows their coexistence at intermediate values of suitable soil water content. A simple formal derivation of the model expresses this hypothesis in a testable form linked uniquely to the actual evapotranspiration of forests community. In the present work we ascertain whether this simplified conclusion possesses some degree of explanatory power by comparing available data on oaks distributions and remotely sensed evapotranspiration (MODIS product) in a large‐scale survey embracing the western Mediterranean area. Our findings confirmed the basic assumptions of model addressed on large scale, but also revealed asymmetric responses to water use and water stress tolerance between evergreen and deciduous oaks that should be taken into account to increase the understating of species interactions and, ultimately, improve the modeling capacity to explain co‐occurrence.