Marker-free transgenic rice lines with a defensin gene are potentially active against phytopathogenic fungus Sarocladium oryzae

In this work it was developed marker-free transgenic indica rice plants (cv J-104) by biolistic co-transformation and segregation approach. We attempted to express the NmDef02 antifungal defensin. Primary transformants were regenerated from embryogenic callus on culture medium with 50 mg/L hygromycin. Screening of hpt-marker-free transgenic lines was made by PCR in T1 progeny lines, germinated on semisolid medium without hygromycin. Relative expression of NmDef02 mRNA was examined by quantitative RT-PCR in marker-free T1 plants. In vitro antifungal test was performed by disk diffusion assay against Sarocladium oryzae. PCR assay verified that 15.12% of T1 plants were marker-free (NmDef02+/hpt−). RT-PCR analysis indicated that NmDef02 gene was successfully transcribed and the transgenic lines displayed different expression levels of the NmDef02 cDNA. Protein extracts of marker-free lines with high relative expression of NmDef02 inhibited fungus mycelial growth around disks. In contrast, it was confirmed fungus proliferation on disks impregnated with protein extracts of non-transgenic plants. The results of the present work demonstrated that the expression of the NmDef02 defensin in transgenic rice plants is effective against the phytopathogenic fungus Sarocladium oryzae under in vitro conditions. Thus, NmDef02 defensin could be a useful tool for J-104 rice improvement

AgMIP Regional Activities in a Global Framework: The Brazil Experience

Climate variability and change are projected to increate the frequency of extreme high-temperature events, floods, and droughts, which can lead to subsequent changes in soil moister in many locations (Alexandrov and Hoogenboom, 2000). In Brazil, observations reveal a tendency for increasing frequency of extreme rainfall events particularly in south Brazil (Alexander et al., 2006; Carvalho et al., 2014; Groissman et al., 2005), as well as projections for increasing extremes in both maximum and minimum temperatures and high spatial variability for rainfall under the IPCC SRES A2 and B2 scenarios (Marengo et al., 2009)

Using AgMIP Regional Integrated Assessment Methods to Evaluate Vulnerability, Resilience and Adaptive Capacity for Climate Smart Agricultural Systems

The predicted effects of climate change call for a multi-dimensional method to assess the performance of various agricultural systems across economic, environmental and social dimensions. Climate smart agriculture (CSA) recognizes that the three goals of climate adaptation, mitigation and resilience must be integrated into the framework of a sustainable agricultural system. However, current methods to determine a systems’ ability to achieve CSA goals are lacking. This paper presents a new simulation-based method based on the Regional Integrated Assessment (RIA) methods developed by the Agricultural Model Inter-comparison and Improvement Project (AgMIP) for climate impact assessment. This method combines available data, field- and stakeholder-based surveys, biophysical and economic models, and future climate and socio-economic scenarios. It features an integrated farm and household approach and accounts for heterogeneity across biophysical and socioeconomic variables as well as temporal variability of climate indicators. This method allows for assessment of the technologies and practices of an agricultural system to achieve the three goals of CSA. The case study of a mixed crop livestock system in western Zimbabwe is highlighted as a typical smallholder agricultural systems in Africa

Balancing co-benefits and trade-offs between climate change mitigation and adaptation innovations under mixed crop-livestock systems in semi-arid Zimbabwe

Achieving Zimbabwe’s national and international commitments to food systems transformation and climate resilience building is of high priority. Integrated simulation-based research approaches developed under the Agricultural Model Intercomparison and Improvement Project (AgMIP) are important sources of evidence to guide policy decisions towards sustainable intensification. Through the identification of economically viable, socially inclusive and environmentally sustainable development pathways, the analysis in this study evaluates co-benefits and tradeoffs between climate change adaptation and mitigation interventions for vulnerable smallholder crop-livestock holdings in the semi-arid regions of Zimbabwe. We explore how climate effects disrupt the livelihoods and food security for diverse farm types, the extremely vulnerable and those better resource endowed but facing high risks. In an iterative process with experts and stakeholders, we co-developed context specific development pathways. They include market-oriented adaptation and mitigation interventions and social protection mechanisms that would support the transition towards more sustainable intensified, diversified and better integrated crop-livestock systems. We assess the trade-offs associated with adoption of climate-smart interventions aimed at improving incomes and food security but that may have consequences on GHG emissions for the different pathways and farm types. The approach and results inform the discussion on drivers that can bring about sustainable intensification, and the extent to which socio-economic benefits could enhance the uptake of emission reducing technologies thereof. Through this strategy we evaluate interventions that can result in win–win outcomes, that is, adaptation-mitigation co-benefits, and what this would imply for policies that aim at transforming agri-food systems

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

Sublittoral soft bottom communities and diversity of Mejillones Bay in northern Chile (Humboldt Current upwelling system)

The macrozoobenthos of Mejillones Bay (23°S; Humboldt Current) was quantitatively investigated over a 7-year period from austral summer 1995/1996 to winter 2002. About 78 van Veen grab samples taken at six stations (5, 10, 20 m depth) provided the basis for the analysis of the distribution of 60 species and 28 families of benthic invertebrates, as well as of their abundance and biomass. Mean abundance (2,119 individuals m-2) was in the same order compared to a previous investigation; mean biomass (966 g formalin wet mass m-2), however, exceeded prior estimations mainly due to the dominance of the bivalve Aulacomya ater. About 43% of the taxa inhabited the complete depth range. Mean taxonomic Shannon diversity (H', Log e) was 1.54 ± 0.58 with a maximum at 20 m (1.95 ± 0.33); evenness increased with depth. The fauna was numerically dominated by carnivorous gastropods, polychaetes and crustaceans (48%). About 15% of the species were suspensivorous, 13% sedimentivorous, 11% detritivorous, 7% omnivorous and 6% herbivorous. Cluster analyses showed a significant difference between the shallow and the deeper stations. Gammarid amphipods and the polychaete family Nephtyidae characterized the 5-mzone, the molluscs Aulacomya ater, Mitrella unifasciata and gammarids the intermediate zone, while the gastropod Nassarius gayi and the polychaete family Nereidae were most prominent at the deeper stations. The communities of the three depth zones did not appear to be limited by hypoxia during non-El Niño conditions. Therefore, no typical change in community structure occurred during El Niño 1997–1998, in contrast to what was observed for deeper faunal assemblages and hypoxic bays elsewhere in the coastal Humboldt Current system

NTFP harvesters as citizen scientists: Validating traditional and crowdsourced knowledge on seed production of Brazil nut trees in the Peruvian Amazon.

Understanding the factors that underlie the production of non-timber forest products (NTFPs), as well as regularly monitoring production levels, are key to allow sustainability assessments of NTFP extractive economies. Brazil nut (Bertholletia excelsa, Lecythidaceae) seed harvesting from natural forests is one of the cornerstone NTFP economies in Amazonia. In the Peruvian Amazon it is organized in a concession system. Drawing on seed production estimates of >135,000 individual Brazil nut trees from >400 concessions and ethno-ecological interviews with >80 concession holders, here we aimed to (i) assess the accuracy of seed production estimates by Brazil nut seed harvesters, and (ii) validate their traditional ecological knowledge (TEK) about the variables that influence Brazil nut production. We compared productivity estimates with actual field measurements carried out in the study area and found a positive correlation between them. Furthermore, we compared the relationships between seed production and a number of phenotypic, phytosanitary and environmental variables described in literature with those obtained for the seed production estimates and found high consistency between them, justifying the use of the dataset for validating TEK and innovative hypothesis testing. As expected, nearly all TEK on Brazil nut productivity was corroborated by our data. This is reassuring as Brazil nut concession holders, and NTFP harvesters at large, rely on their knowledge to guide the management of the trees upon which their extractive economies are based. Our findings suggest that productivity estimates of Brazil nut trees and possibly other NTFP-producing species could replace or complement actual measurements, which are very expensive and labour intensive, at least in areas where harvesters have a tradition of collecting NTFPs from the same trees over multiple years or decades. Productivity estimates might even be sourced from harvesters through registers on an annual basis, thus allowing a more cost-efficient and robust monitoring of productivity levels

Climate change impacts and adaptation for dryland farming systems in Zimbabwe: a stakeholder-driven integrated multi-model assessment

Decision makers need accurate information to address climate variability and change and accelerate transformation to sustainability. A stakeholder-driven, science-based multimodel approach has been developed and used by the Agricultural Model Intercomparison and Improvement Project (AgMIP) to generate actionable information for adaptation planning processes. For a range of mid-century climate projections—likely to be hotter, drier, and more variable—contrasting future socio-economic scenarios (Representative Agricultural Pathways, RAPs) were co-developed with stakeholders to portray a sustainable development scenario and a rapid economic growth pathway. The unique characteristic of this application is the integration of a multi-modeling approach with stakeholder engagement to co-develop scenarios and adaptation strategies. Distribution of outcomes were simulated with climate, crop, livestock, and economic impact assessment models for smallholder crop livestock farmers in a typical dryland agro-ecological zone in Zimbabwe, characterized by low and erratic rainfall and nutrient depleted soils. Results showed that in Nkayi District, Western Zimbabwe, climate change would threaten most of the farms, and, in particular, those with large cattle herds due to feed shortages. Adaptation strategies that showed the most promise included diversification using legume production, soil fertility improvement, and investment in conducive market environments. The switch to more legumes in the farming systems reduced the vulnerability of the very poor as well as the more resourced farmers. Overall, the sustainable development scenario consistently addressed institutional failures and motivated productivity- enhancing, environmentally sound technologies and inclusive development approaches. This yielded more favorable outcomes than investment in quick economic wins from commercializing agriculture

Crop–Livestock Intensification in the Face of Climate Change: Exploring Opportunities to Reduce Risk and Increase Resilience in Southern Africa by Using an Integrated Multi-modeling Approach

The climate of Southern Africa is highly variable at most time-scales and follows a pronounced gradient with arid conditions in the west and humid conditions in the east. There is also a marked latitudinal rainfall distribution pattern, with the southern part having a low rainfall index and high variability and the northern part having higher annual rainfall and lower interannual variability (Kandji et al., 2006). Over the last 100 years, temperatures have increased by about 0.5◦C in the region and downward trends in rainfall have also occurred (Kandji et al., 2006; Morton, 2007). There has also been an increase in drought eventswith over 15 drought events reported in the region between 1988 and 1992. The frequency and intensity of El Nin˜o episodes have increased. Prior to the 1980s, strong El Nin˜o events occurred every 10–20 years; between 1980 and 2000, the region experienced five episodes with the 1982–1983 and 1997–1998 episodes being the most intense of the century (Reason and Jagadheesha, 2005; Rouault and Richard, 2005). These episodes have contributed to stagnant or decreasing agricultural production and worsening food insecurity in the region (Kandji et al., 2006). Unfavorable climatic conditions and projected climate change are among the major obstacles to achieving food security in the region and also have dire consequences for macro-economic performance