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- ItemClimate change and potential distribution of potato (Solanum tuberosum) crop cultivation in Pakistan using Maxent(Springfield, MO : AIMS Press, 2021) Khalil, Tayyaba; Asad, Saeed A.; Khubaib, Nusaiba; Baig, Ayesha; Atif, Salman; Umar, Muhammad; Kropp, Jürgen P.; Pradhan, Prajal; Baig, SofiaThe impacts of climate change are projected to become more intense and frequent. One of the indirect impacts of climate change is food insecurity. Agriculture in Pakistan, measured fourth best in the world, is already experiencing visible adverse impacts of climate change. Among many other food sources, potato crop remains one of the food security crops for developing nations. Potatoes are widely cultivated in Pakistan. To assess the impact of climate change on potato crop in Pakistan, it is imperative to analyze its distribution under future climate change scenarios using Species Distribution Models (SDMs). Maximum Entropy Model is used in this study to predict the spatial distribution of Potato in 2070 using two CMIP5 models for two climate change scenarios (RCP 4.5 and RCP 8.5). 19 Bioclimatic variables are incorporated along with other contributing variables like soil type, elevation and irrigation. The results indicate slight decrease in the suitable area for potato growth in RCP 4.5 and drastic decrease in suitable area in RCP 8.5 for both models. The performance evaluation of the model is based on AUC. AUC value of 0.85 suggests the fitness of the model and thus, it is applicable to predict the suitable climate for potato production in Pakistan. Sustainable potato cultivation is needed to increase productivity in developing countries while promoting better resource management and optimization.
- ItemSimultaneous Calibration of Grapevine Phenology and Yield with a Soil–Plant–Atmosphere System Model Using the Frequentist Method(Basel : MDPI, 2021-8-20) Yang, Chenyao; Menz, Christoph; Fraga, Helder; Reis, Samuel; Machado, Nelson; Malheiro, Aureliano C.; Santos, João A.Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety–training systems in the Douro Demarcated Region, northern Portugal. An objective function is defined to search for the best-fit parameters that result in the minimum value of the unweighted sum of the normalized Root Mean Squared Error (nRMSE) of the studied variables. Parameter uncertainties are estimated as how a given parameter value can determine the total prediction variability caused by variations in the other parameter combinations. The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of −2 to 4 days for phenology and −232 to 159 kg/ha for yield. The corresponding variance in the observed data was generally well reproduced, except for one occasion. These parameters are a good trade-off to achieve results close to the best possible fit of each response variable. No parameter combinations can achieve minimum errors simultaneously for phenology and yield, where the best fit to one variable can lead to a poor fit to another. The proposed parameter uncertainty analysis is particularly useful to select the best-fit parameter values when several choices with equal performance occur. A global sensitivity analysis is applied where the fruit-setting parameters are identified as key determinants for yield simulations. Overall, the approach (including uncertainty analysis) is relatively simple and straightforward without specific pre-conditions (e.g., model continuity), which can be easily applied for other models and crops. However, a challenge has been identified, which is associated with the appropriate assumption of the model errors, where a combination of various calibration approaches might be essential to have a more robust parameter estimation.
- ItemEvaluating the grassland NPP dynamics in response to climate change in Tanzania(Amsterdam [u.a.] : Elsevier Science, 2021) Zarei, Azin; Chemura, Abel; Gleixner, Stephanie; Hoff, HolgerLivestock is important for livelihoods of millions of people across the world and yet climate change risk and impacts assessments are predominantly on cropping systems. Climate change has significant impacts on Net Primary Production (NPP) which is a grassland dynamics indicator. This study aimed to analyze the spatio-temporal changes of NPP under climate scenario RCP2.6 and RCP8.5 in the grassland of Tanzania by 2050 and link this to potential for key livestock species. To this end, a regression model to estimate NPP was developed based on temperature (T), precipitation (P) and evapotranspiration (ET) during the period 2001–2019. NPP fluctuation maps under future scenarios were produced as difference maps of the current (2009–2019) and future (2050). The vulnerable areas whose NPP is mostly likely to get affected by climate change in 2050 were identified. The number of livestock units in grasslands was estimated according to NPP in grasslands of Tanzania at the Provincial levels. The results indicate the mean temperature and evapotranspiration are projected to increase under both emission scenarios while precipitation will decrease. NPP is significantly positively correlated with Tmax and ET and projected increases in these variables will be beneficial to NPP under climate change. Increases of 17% in 2050 under RCP8.5 scenario are projected, with the southern parts of the country projected to have the largest increase in NPP. The southwest areas showed a decreasing trend in mean NPP of 27.95% (RCP2.6) and 13.43% (RCP8.5). The highest decrease would occur in the RCP2.6 scenario in Ruvuma Province, by contrast, the mean NPP value in the western, eastern, and central parts would increase in 2050 under both Scenarios, the largest increase would observe in Kilimanjaro, Dar-Es-Salaam and Dodoma Provinces. It was found that the number of grazing livestock such as cattle, sheep, and goats will increase in the Tanzania grasslands under both climate scenarios. As the grassland ecosystems under intensive exploitation are fragile ecosystems, a combination of improving grassland productivity and grassland conservation under environmental pressures such as climate change should be considered for sustainable grassland management.
- ItemAnalysis of Olive Grove Destruction by Xylella fastidiosa Bacterium on the Land Surface Temperature in Salento Detected Using Satellite Images(Basel : MDPI, 2021-9-16) Semeraro, Teodoro; Buccolieri, Riccardo; Vergine, Marzia; De Bellis, Luigi; Luvisi, Andrea; Emmanuel, Rohinton; Marwan, NorbertAgricultural activity replaces natural vegetation with cultivated land and it is a major cause of local and global climate change. Highly specialized agricultural production leads to extensive monoculture farming with a low biodiversity that may cause low landscape resilience. This is the case on the Salento peninsula, in the Apulia Region of Italy, where the Xylella fastidiosa bacterium has caused the mass destruction of olive trees, many of them in monumental groves. The historical land cover that characterized the landscape is currently in a transition phase and can strongly affect climate conditions. This study aims to analyze how the destruction of olive groves by X. fastidiosa affects local climate change. Land surface temperature (LST) data detected by Landsat 8 and MODIS satellites are used as a proxies for microclimate mitigation ecosystem services linked to the evolution of the land cover. Moreover, recurrence quantification analysis was applied to the study of LST evolution. The results showed that olive groves are the least capable forest type for mitigating LST, but they are more capable than farmland, above all in the summer when the air temperature is the highest. The differences in the average LST from 2014 to 2020 between olive groves and farmland ranges from 2.8 °C to 0.8 °C. Furthermore, the recurrence analysis showed that X. fastidiosa was rapidly changing the LST of the olive groves into values to those of farmland, with a difference in LST reduced to less than a third from the time when the bacterium was identified in Apulia six years ago. The change generated by X. fastidiosa started in 2009 and showed more or less constant behavior after 2010 without substantial variation; therefore, this can serve as the index of a static situation, which can indicate non-recovery or non-transformation of the dying olive groves. Failure to restore the initial environmental conditions can be connected with the slow progress of the uprooting and replacing infected plants, probably due to attempts to save the historic aspect of the landscape by looking for solutions that avoid uprooting the diseased plants. This suggests that social-ecological systems have to be more responsive to phytosanitary epidemics and adapt to ecological processes, which cannot always be easily controlled, to produce more resilient landscapes and avoid unwanted transformations.
- ItemQuantifying sustainable intensification of agriculture: The contribution of metrics and modelling(Amsterdam [u.a.] : Elsevier, 2021) Mouratiadou, Ioanna; Latka, Catharina; van der Hilst, Floor; Müller, Christoph; Berges, Regine; Bodirsky, Benjamin Leon; Ewert, Frank; Faye, Babacar; Heckelei, Thomas; Hoffmann, Munir; Lehtonen, Heikki; Lorite, Ignacio Jesus; Nendel, Claas; Palosuo, Taru; Rodríguez, Alfredo; Rötter, Reimund Paul; Ruiz-Ramos, Margarita; Stella, Tommaso; Webber, Heidi; Wicke, BirkaSustainable intensification (SI) of agriculture is a promising strategy for boosting the capacity of the agricultural sector to meet the growing demands for food and non-food products and services in a sustainable manner. Assessing and quantifying the options for SI remains a challenge due to its multiple dimensions and potential associated trade-offs. We contribute to overcoming this challenge by proposing an approach for the ex-ante evaluation of SI options and trade-offs to facilitate decision making in relation to SI. This approach is based on the utilization of a newly developed SI metrics framework (SIMeF) combined with agricultural systems modelling. We present SIMeF and its operationalization approach with modelling and evaluate the approach’s feasibility by assessing to what extent the SIMeF metrics can be quantified by representative agricultural systems models. SIMeF is based on the integration of academic and policy indicator frameworks, expert opinions, as well as the Sustainable Development Goals. Structured along seven SI domains and consisting of 37 themes, 142 sub-themes and 1128 metrics, it offers a holistic, generic, and policy-relevant dashboard for selecting the SI metrics to be quantified for the assessment of SI options in diverse contexts. The use of SIMeF with agricultural systems modelling allows the ex-ante assessment of SI options with respect to their productivity, resource use efficiency, environmental sustainability and, to a large extent, economic sustainability. However, we identify limitations to the use of modelling to represent several SI aspects related to social sustainability, certain ecological functions, the multi-functionality of agriculture, the management of losses and waste, and security and resilience. We suggest advancements in agricultural systems models and greater interdisciplinary and transdisciplinary integration to improve the ability to quantify SI metrics and to assess trade-offs across the various dimensions of SI.
- ItemFarm Production Diversity and Household Dietary Diversity: Panel Data Evidence From Rural Households in Tanzania(Lausanne : Frontiers Media, 2021-5-17) Habtemariam, Lemlem Teklegiorgis; Gornott, Christoph; Hoffmann, Harry; Sieber, StefanEvidence on whether diversifying farm production leads to improved household dietary diversity and nutrition remains inconclusive. Existing studies analyzing the link between production diversity and dietary diversity are mainly based on cross-sectional methods, which could be biased by omitted confounding factors. Using two waves of a panel household survey of 900 rural households in Tanzania, this paper examines the link between production diversity and dietary diversity, while minimizing potential confounding effects. We estimate four regression models with two different production diversity measures and two panel estimation methods—fixed effect (FE) and random effect (RE). In three out of the four models, production diversity is significantly and positively associated with the dietary diversity measure of the food consumption score. The production diversity indicator is represented by the total crop and livestock species count, as well as by counting only crop species. The total crop and livestock species count shows a significant positive association with dietary diversity across estimation methods while the positive association with crop species count is not significant in the FE method. Our results suggest that the selection of appropriate production diversity indicators tailored to the specific circumstances of the local agricultural system is likely one key factor in identifying a robust relationship between production diversity and dietary diversity.