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- ItemClassifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change(Amsterdam [u.a.] : Elsevier, 2017) Fronzek, Stefan; Pirttioja, Nina; Carter, Timothy R.; Bindi, Marco; Hoffmann, Holger; Palosuo, Taru; Ruiz-Ramos, Margarita; Tao, Fulu; Trnka, Miroslav; Acutis, Marco; Asseng, Senthold; Baranowski, Piotr; Basso, Bruno; Bodin, Per; Buis, Samuel; Cammarano, Davide; Deligios, Paola; Destain, Marie-France; Dumont, Benjamin; Ewert, Frank; Ferrise, Roberto; François, Louis; Gaiser, Thomas; Hlavinka, Petr; Jacquemin, Ingrid; Kersebaum, Kurt Christian; Kollas, Chris; Krzyszczak, Jaromir; Lorite, Ignacio J.; Minet, Julien; Minguez, M. Ines; Montesino, Manuel; Moriondo, Marco; Müller, Christoph; Nendel, Claas; Öztürk, Isik; Perego, Alessia; Rodríguez, Alfredo; Ruane, Alex C.; Ruget, Françoise; Sanna, Mattia; Semenov, Mikhail A.; Slawinski, Cezary; Stratonovitch, Pierre; Supit, Iwan; Waha, Katharina; Wang, Enli; Wu, Lianhai; Zhao, Zhigan; Rötter, Reimund P.Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
- ItemLivelihood and climate trade-offs in Kenyan peri-urban vegetable production(Amsterdam [u.a.] : Elsevier, 2017) Kurgat, Barnabas K.; Stöber, Silke; Mwonga, Samuel; Lotze-Campen, Hermann; Rosenstock, Todd S.Trade-offs between livelihood and environmental outcomes due to agricultural intensification in sub-Saharan Africa are uncertain. The present study measured yield, economic performance and nitrous oxide (N2O) emissions in African indigenous vegetable (AIV) production to investigate the optimal nutrient management strategies. In order to achieve this, an on-farm experiment with four treatments – (1) 40 kg N/ha diammonium phosphate (DAP), (2) 10 t/ha cattle manure, (3) 20 kg N/ha DAP and 5 t/ha cattle manure and (4) a no-N input control – was performed for two seasons. Yields and N2O emissions were directly measured with subsampling and static chambers/gas chromatography, respectively. Economic outcomes were estimated from semi-structured interviews (N = 12). Trade-offs were quantified by calculating N2O emissions intensity (N2OI) and N2O emissions economic intensity (N2OEI). The results indicate that, DAP alone resulted at least 14% greater yields, gross margin and returns to labour in absolute terms but had the highest emissions (p = 0.003). Productivity-climate trade-offs, expressed as N2OI, were statistically similar for DAP and mixed treatments. However, N2OEI was minimized under mixed management (p = 0.0004) while maintaining productivity and gross margins. We therefore conclude that soil fertility management strategies that mix inorganic and organic source present a pathway to sustainable intensification in AIV production. Future studies of GHG emissions in crop production need to consider not only productivity but economic performance when considering trade-offs.
- ItemAerial river management by smart cross-border reforestation(Amsterdam [u.a.] : Elsevier Science, 2019) Weng, Wei; Costa, Luís; Lüdeke, Matthias K.B.; Zemp, Delphine C.In the face of increasing socio-economic and climatic pressures in growing cities, it is rational for managers to consider multiple approaches for securing water availability. One often disregarded option is the promotion of reforestation in source regions supplying important quantities of atmospheric moisture transported over long distances through aerial rivers, affecting water resources of a city via precipitation and runoff (‘smart reforestation’). Here we present a case demonstrating smart reforestation's potential as a water management option. Using numerical moisture back-tracking models, we identify important upwind regions contributing to the aerial river of Santa Cruz de la Sierra (Bolivia). Simulating the effect of reforestation in the identified regions, annual precipitation and runoff reception in the city was found to increase by 1.25% and 2.30% respectively, while runoff gain during the dry season reached 26.93%. Given the city's population growth scenarios, the increase of the renewable water resource by smart reforestation could cover 22–59% of the additional demand by 2030. Building on the findings, we argue for a more systematic consideration of aerial river connections between regions in reforestation and land planning for future challenges. © 2019 The Authors
- ItemDrivers of sustainable intensification in Kenyan rural and peri-urban vegetable production(London : Taylor & Francis, 2018) Kurgat, Barnabas K.; Ngenoh, Evans; Bett, Hillary K.; Stöber, Silke; Mwonga, Samuel; Lotze-Campen, Hermann; Rosenstock, Todd S.Sustainable intensification promotes environmentally sound and productive agriculture. However, use of sustainable intensification practices (SIPs) is low in many sub-Sharan African countries. This study examined the adoption of SIPs in Kenyan rural and peri-urban vegetable production to understand the scale of and underlying factors in the use of SIPs. A multistage sampling technique was employed to randomly select 685 rural and peri-urban vegetable farm households. Household data was then collected and anaylsed for four practices namely improved irrigation, integrated soil fertility, organic manure and crop diversification using a pre-tested structured questionnaire. A multivariate probit model was run to model simultaneous interdependent adoption decisions. Adoption of organic manure and African indigenous vegetables (AIV) diversification was high in both rural and peri-urban areas. However, adoption of improved irrigation systems and integrated soil fertility management was low, and even significantly lower in rural areas than in peri-urban areas (p < 0.041). Similarly, adoption intensity of SIPs was lower in rural areas than in peri-urban areas. Furthermore, the findings also show complementarities and substitutabilities between SIPs. Market integration, the farm location and household income were the major factors heavily influencing the adoption of most SIPs. Policies and programmes that seek to build household financial capital base and integrate farm households into effective and efficient vegetable markets need to be formulated and implemented in order to enhance adoption of SIPs in AIV production.
- ItemRecurrent Governance Challenges in the Implementation and Alignment of Flood Risk Management Strategies: a Review(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2016) Dieperink, C.; Hegger, D.L.T; Bakker, M.H.N.; Kundzewicz, Z.W.; Green, C.; Driessen, P.P.J.In Europe increasing flood risks challenge societies to diversify their Flood Risk Management Strategies (FRMSs). Such a diversification implies that actors not only focus on flood defence, but also and simultaneously on flood risk prevention, mitigation, preparation and recovery. There is much literature on the implementation of specific strategies and measures as well as on flood risk governance more generally. What is lacking, though, is a clear overview of the complex set of governance challenges which may result from a diversification and alignment of FRM strategies. This paper aims to address this knowledge gap. It elaborates on potential processes and mechanisms for coordinating the activities and capacities of actors that are involved on different levels and in different sectors of flood risk governance, both concerning the implementation of individual strategies and the coordination of the overall set of strategies. It identifies eight overall coordination mechanisms that have proven to be useful in this respect.
- ItemForest carbon allocation modelling under climate change(Victoria, BC : Heron, 2019) Merganičová, Katarína; Merganič, Ján; Lehtonen, Aleksi; Vacchiano, Giorgio; Ostrogović Sever, Maša Zorana; Augustynczik, Andrey L. D.; Grote, Rüdiger; Kyselová, Ina; Mäkelä, Annikki; Yousefpour, Rasoul; Krejza, Jan; Collalti, Alessio; Reyer, Christopher P. O.Carbon allocation plays a key role in ecosystem dynamics and plant adaptation to changing environmental conditions. Hence, proper description of this process in vegetation models is crucial for the simulations of the impact of climate change on carbon cycling in forests. Here we review how carbon allocation modelling is currently implemented in 31 contrasting models to identify the main gaps compared with our theoretical and empirical understanding of carbon allocation. A hybrid approach based on combining several principles and/or types of carbon allocation modelling prevailed in the examined models, while physiologically more sophisticated approaches were used less often than empirical ones. The analysis revealed that, although the number of carbon allocation studies over the past 10 years has substantially increased, some background processes are still insufficiently understood and some issues in models are frequently poorly represented, oversimplified or even omitted. Hence, current challenges for carbon allocation modelling in forest ecosystems are (i) to overcome remaining limits in process understanding, particularly regarding the impact of disturbances on carbon allocation, accumulation and utilization of nonstructural carbohydrates, and carbon use by symbionts, and (ii) to implement existing knowledge of carbon allocation into defence, regeneration and improved resource uptake in order to better account for changing environmental conditions. © The Author(s) 2019. Published by Oxford University Press.