ALBERT

Description: In this paper we develop an agent-based model to explore a feasible way of simultaneously providing sufficient food and bioenergy feedstocks in China. Concerns over the competition for agricultural land resources between food and bioenergy supply hinder the further development of bioenergy, especially in China, the country that needs to feed the world’s largest population. Prior research has suggested the introduction of energy crops and reviewed the resulting agricultural land use change in China. However, there is a lack of quantitative studies which estimate the value, contribution, and impact of bioenergy for specific conditions at the county level and provide adequate information to guide local practices. To fill this gap, we choose the Jiangsu Province in China as a case study, build up a spatial-agent dynamic model of agricultural land use, and perform a sensitivity analysis for important parameters. The simulation results show that straw from conventional crops generally dominates Jiangsu’s biomass supply with a contribution above 85%. The sensitivity analyses reveal severe consequences of bioenergy targets for local land use. For Jiangsu Province, reclaimed mudflats, an alternative to arable lands for energy crop plantation, help to secure the local biomass supply and to alleviate the land use conflict between food and biomass production.

Description: In the lignite mining region of Lower Lusatia (NE-Germany), Robinia pseudoacacia L. is an increasingly popular tree for the biomass production with short rotation coppices (SRCs) on reclamation sites. In order to evaluate biomass production, C and N allocation patterns in R. pseudoacacia stands between shoot, stump, coarse, and fine roots samples were collected from seedlings and three adjacent plantations and plants that were one, two and twelve years old. Results indicated that the summarized average dry matter production (DM) of the woody plant parts increased with plant age up to 7.45 t DM ha−1 yr−1 with a corresponding shoot increment of up to 4.77 t DM ha−1 yr−1 in the twelve-year-old stands. The shoot to root ratio changed from 0.2 for the one-year-old trees to 2.0 in the twelve-year-old plantation, whereby an average amount of 3.4 t C ha−1 yr−1 and 0.1 t N ha−1 yr−1 was annually bound in the living woody plant parts over the period of twelve years. Summing up, the results suggest a high potential for C and N storage of R. pseudoacacia what is also beneficial for land reclamation due to positive implications on soil humus and general site fertility.

Description: In developed countries, more than half of all cancer patients receive radiotherapy at some stage in the management of their disease. However, a radiation-induced secondary malignancy can be the price of success if the primary cancer is cured or at least controlled. Therefore, there is increasing concern regarding radiation-related second cancer risks in long-term radiotherapy survivors and a corresponding need to be able to predict cancer risks at high radiation doses. Of particular interest are second cancer risk estimates for new radiation treatment modalities such as intensity modulated radiotherapy, intensity modulated arc-therapy, proton and heavy ion radiotherapy. The long term risks from such modern radiotherapy treatment techniques have not yet been determined and are unlikely to become apparent for many years, due to the long latency time for solid tumor induction. Most information on the dose-response of radiation-induced cancer is derived from data on the A-bomb survivors who were exposed to γ-rays and neutrons. Since, for radiation protection purposes, the dose span of main interest is between zero and one Gy, the analysis of the A-bomb survivors is usually focused on this range. With increasing cure rates, estimates of cancer risk for doses larger than one Gy are becoming more important for radiotherapy patients. Therefore in this review, emphasis was placed on doses relevant for radiotherapy with respect to radiation induced solid cancer. Simple radiation protection models should be used only with extreme care for risk estimates in radiotherapy, since they are developed exclusively for low dose. When applied to scatter radiation, such models can predict only a fraction of observed second malignancies. Better semi-empirical models include the effect of dose fractionation and represent the dose-response relationships more accurately. The involved uncertainties are still huge for most of the organs and tissues. A major reason for this is that the underlying processes of the induction of carcinoma and sarcoma are not well known. Most uncertainties are related to the time patterns of cancer induction, the population specific dependencies and to the organ specific cancer induction rates. For radiotherapy treatment plan optimization these factors are irrelevant, as a treatment plan comparison is performed for a patient of specific age, sex, etc. If a treatment plan is compared relative to another one only the shape of the dose-response curve (the so called risk-equivalent dose) is of importance and errors can be minimized.

Description: Accounts of Chemical Research DOI: 10.1021/ar300008t

Description: Journal of the American Chemical Society DOI: 10.1021/jacs.7b01542

Description: Journal of the American Chemical Society DOI: 10.1021/jacs.6b06767

Description: Agriculture, Vol. 8, Pages 53: Assessing the Economic Impacts of Pesticide Regulations Agriculture doi: 10.3390/agriculture8040053 Authors: Uwe Schneider Livia Rasche Bruce McCarl Economic impacts of pesticide regulations are assessed using five alternative methodologies. The regulations include crop supply-enhancing eradication programs and crop supply-decreasing pesticide bans. Alternative assessment methodologies differ regarding assumptions about market price and crop acreage adjustments. Results show that market and producer adjustments substantially impact conclusions about winners and losers from regulations, and estimated welfare effects can differ widely between the different methodologies. For small technological changes such as the hypothetical pendimethalin regulation, farm budgeting and sector modeling yield similar estimates. For more severe technological changes—like the boll weevil eradication program—simple budgeting approaches lead to a substantial bias.

Description: In a modeling study we examine vulnerability of income from mobile (transhumant) pastoralism and sedentary pastoralism to reduced mean annual precipitation (MAP) and droughts. The study is based on empirical data of a 3410 km2 research region in southern, semi-arid Morocco. The land use decision model integrates a meta-model of the Environmental Policy Integrated Climate (EPIC) simulator to depict perennial and annual forage plant development. It also includes livestock dynamics and forward-looking decision making under uncertain weather. Mobile livestock in the model moves seasonally, sedentary livestock is restricted to pastures around settlements. For a reduction of MAP by 20%, our model shows for different experimental frequencies of droughts a significant decrease of total income from pastoralism by 8%–19% (p 〈 0.05). Looking separately at the two modes of pastoralism, pronounced income losses of 18%–44% (p 〈 0.05) show that sedentary pastoralism is much more vulnerable to dryer climate than mobile pastoralism, which is merely affected. Dedicating more pasture area and high quality fodder to mobile pastoralism significantly abates impacts from reduced MAP and droughts on total income by 11% (p 〈 0.05). Our results indicate that promotion of mobile pastoralism in semi-arid areas is a valuable option to increase resilience against climate change.

Description: Energies, Vol. 11, Pages 152: Impacts of Bioenergy Policies on Land-Use Change in Nigeria Energies doi: 10.3390/en11010152 Authors: Stanley Okoro Udo Schickhoff Uwe Schneider In recent years, bioenergy policies have increased the competition for land as well as the risk of adverse environmental impacts resulting from deforestation and greenhouse gas emissions (GHGs). Primary land-use objectives confronting society today include meeting the growing demand for agricultural products, especially energy crops, preserving essential ecosystem services for human well-being and long-run agrarian production, and contributing to the climate policy target. Here, future agricultural, societal and environmental consequences of bioenergy policies under different global climate and societal development scenarios were assessed using a novel Forest and Agricultural Sector Optimization Model for Nigeria (NGA–FASOM). The results reveal that, in Nigeria, meeting emission reduction requires an implementation of a minimum carbon price of $80/ton within the forest and agricultural sectors. A carbon price alone is not sufficient to preserve the remaining forests and pasture land in Nigeria when bioenergy is subsidized. Furthermore, the result shows that subsidy on bioenergy does not have any significant effect on the total social welfare. The findings in this study provide a guide for policymakers in designing appropriate policies addressing bioenergy industry issues in Nigeria.

Description: Land, Vol. 7, Pages 4: Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks Land doi: 10.3390/land7010004 Authors: Leonel Lara-Estrada Livia Rasche L. Sucar Uwe Schneider Climate data availability plays a key role in development processes of policies, services, and planning in the agricultural sector. However, data at the spatial or temporal resolution required is often lacking, or certain values are missing. In this work, we propose to use a Bayesian network approach to generate data for missing variables. As a case study, we use relative humidity, which is an important indicator of land suitability for coffee production. For the model, we first extracted climate data for the variables precipitation, maximum and minimum air temperature, wind speed, solar radiation and relative humidity from the surface reanalysis dataset Climate Forecast System Reanalysis. We then used machine learning algorithms to define the model structure and parameters from the relationships of the variables found in the dataset. Precipitation, maximum and minimum air temperature, wind speed, and solar radiation are then used as proxy variables to infer missing values for monthly relative humidity and relative humidity for the driest month. For this, we used both complete and incomplete initial data. In both scenarios of data availability, the comparison of estimated and measured values of relative humidity shows a high level of agreement. We conclude that using Bayesian Networks is a practical solution to estimate relative humidity for coffee agricultural planning.