Browsing by Subject "Modeling"

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An expert system for planning and designing dairy farms in hot climates
(2008) Samer Mohamed, Mohamed; Jungbluth, Thomas
Planning and designing dairy farm facilities is a sophisticated work where a multitude of procedures should be carried out which requires time and efforts; moreover, making mistakes is also possible. Therefore, it is necessary to develop computer tools that have the ability to pre-process the data so as to produce value-added information, in order to accelerate analyses and to improve decision-making. Eleven simulation models were developed to plan and design several dairy farm facilities. Subsequently, an electronic spark map (decision tree) was developed for each simulation model, and then the simulation models were integrated into the relevant spark maps. Afterwards, C# language (C Sharp), which is an object-oriented programming language, was used to develop an expert system via the simulation models and the electronic spark maps. The developed expert system is able to plan and design several dairy farm facilities, e.g. housing system (corrals system), shade structure and roof material, concrete base, cooling system, milking parlor, forage storage, and manure handling system. Subsequently, it plans the farmstead layout, and it leads to implement the technologies, equipments, and machines required for performing several farm operations. Furthermore, it studies water and electricity requirements of the planned dairy farm and the available sources on site. Moreover, it calculates the capital investment and the fixed, variable, and total costs. Data of 6 dairy farms were used to carry out the expert system validation and evaluation. The differences between the actual and calculated values were determined and the standard deviations were calculated. The coefficients of variation range between 3% and 7%. The required input data are 358 thereof a multitude will be recommended by the expert system itself; consequently, it computes and displays 372 output data with the ability of saving and retrieving data. Besides, the system?s accuracy had been calculated using the actual and calculated values of the different outputs and it was found 98.6%. However, the system?s syntax includes 22106 lines. It can be concluded that the developed expert system can be used successfully for planning and designing dairy cow farms in hot climates.
Combining remote sensing and crop modeling techniques to derive a nitrogen fertilizer application strategy
(2020) Röll, Georg; Graeff-Hönninger, Simone
The crucial question in this thesis was how can remote sensing data and crop models be used to derive a N fertilizer strategy that is capable to lower the environmental side effects of N fertilizer application. This raised the following detailed objectives: The first objective (i) how N content determination via spectral reflectance is influenced by different leaves and positions on the leaf was investigated in Publication I. Different wheat plants were cultivated under different N levels and under drought stress in two hydroponic greenhouse trials. Spectral reflectance measurements were taken from three leaves and at three positions on the leaf for each plant. In total, 16 vegetation indices broadly used in the literature were calculated based on the spectral reflectance for each combination of leaf and position. The plant N content was determined by lab analyses. Neither the position on the leaf nor leaf number had an impact on the accuracy of plant N determination via spectral reflectance measurements. Therefore measurements taken at the canopy level seem to be a valid approach. However, if other stress symptoms like drought or disease infection occur, a differentiation between leaves and positions on the leaf might play a more crucial role. Publication II dealt with the second objective on (ii), how to incorporate leaf disease into the DSSAT wheat model to enable the simulation of the impact of leaf disease on yield. An integration of sensor information in crop growth models requires the update of model state variables. A model extension was developed by adding a pest damage module to the existing wheat model. The approach was tested on a two-year dataset from Argentina with different wheat cultivars and on a one-year dataset from Germany with different inoculum levels of septoria tritici blotch (STB). After the integration of disease infection, the accuracy of the simulated yield and leaf area index (LAI) was improved. The Root mean squared error (RMSE) values for yield (1144 kg ha−1) and LAI (1.19 m2 m−2) were reduced by half (499 kg ha−1) for yield and LAI (0.69 m2 m−2). A sensitivity analysis also showed a strong responsiveness of the model by the integration of different STB disease infection scenarios. Increasing the modeling accuracy even further a MM approach seems to be suitable. Assembling more models increases the complexity of the simulation and the involved calibration procedure especially if the user is not familiar with all models. To avoid these conflicts, Publication III evaluated the third objective (iii) if an automatic calibration procedure in a MM approach for winter wheat can eliminate the subjectivity factor in model calibration. The model calibration was performed on a 4-yr N wheat fertilizer trial in southwest Germany. The evaluation mean showed satisfying results for the calibration (d-Index 0.93) and evaluation dataset (d-Index 0.81). This lead to the fourth (iv) objective to use a MM approach to improve the overall modeling accuracy. The evaluation of a fertilizer trial showed an improved modeling accuracy in most cases, especially in the drought season 2018. Based on the combination of a MM approach and the incorporation of sensor data, a Nitrogen Application Prescription System (NAPS) was developed. The initial NAPS setup requires long term recorded data (yield, weather, and soil) to ensure proper MM calibration. After calibration, the current growing season conditions are required (weather, management information) until the N application date. Afterward, the NAPS incorporates remote sensing information and generated weather for running future N application scenarios. The selection of the proper amount of N is determined by economic and ecological criteria. Furthermore, in order to account for differences in in-field variabilities and to deliver a N prescription site-specifically, the NAPS concept has to be applied on a geospatial scale by adjusting soil parameters spatially. The NAPS concept has the potential to adjust the N application more economically and ecologically by using current sensor data, historical yield records, and future weather prediction to derive a more precise N application strategy. Finally, this concept exhibits the potential for reconciliation of the issue of an economic, agricultural production without harming the environment.
Constraints on microbial pesticide degradation in soils
(2023) Wirsching, Johannes; Kandeler, Ellen
Pesticides are an essential component of intensified agriculture and have contributed significantly to the increase in food production observed in recent decades. Since 1960, pesticide use has increased by a factor of fifteen to twenty, representing a market value of $40 billion in 2016. Soil monitoring campaigns to track pesticide contamination of croplands across Europe are quantifying pesticide residues whose residence times in soils exceed expected values. Diffuse contamination by pesticide residues raises concerns about soil functions, soil biodiversity, and food safety, as well as the transport of contaminants by wind and water to surface waters or to adjacent, organically managed croplands. Data on the frequency of occurrence and concentrations of pesticide residues in soil demonstrate a discrepancy between the determination of persistence and subsequent approval and their actual fate in soil. This raises the question of whether degradability of individual organic compounds has been adequately studied. Microbiological degradation is the most important process for reducing pesticide loads in soils. A reliable estimate of pesticide residence time requires an expanded understanding of the factors limiting microbial degradation. Degradation of anthropogenic organic chemicals in soils occurs much more slowly than would be expected based on their physicochemical properties. While processes that determine the fate of pesticides in soil have often been studied at different spatial and temporal scales, reasons for discrepancies between the observed complete degradation of pesticides under laboratory conditions and their persistence in the field remain unclear. This thesis addresses this challenge by focusing on the central question of why inherently biodegradable compounds in soils display increased persistence under field conditions. Organic contaminants in low but detectable and environmentally significant concentrations could remain in the soil once available concentrations fall below a threshold where bioenergetic growth restrictions come into play. In addition, potential microbial and biophysical limitations and environmental factors such as soil temperature and soil moisture are often examined separately in current degradation studies. Combinations of temperature and soil moisture changes associated with different concentration levels have been less well examined, resulting in an incomplete understanding of the degradation process. Another key factor in the demonstrated discrepancy between predicted and actual persistence in the field could be due to laboratory experiments that cannot account for field-scale processes. Therefore, degradation rates determined in laboratory experiments cannot be confidently extrapolated to the field scale. . This thesis identified further important regulatory mechanisms for microbially mediated pesticide degradation. The previously unknown concentration-dependent degradation dynamics and the concentration-dependent influence of limiting environmental conditions on microbial degradation emphasize the importance of studies using a realistic concentration range. Evidence of deep transport of a highly sorptive pesticide such as glyphosate primarily via preferential flow pathways into the subsoil with lower degradation dynamics underscores the need to include processes that can only be verified in field studies as part of risk assessments. The results of this thesis suggest that the biodegradation rates of pesticides are not homogeneous at field scales and may account in part for the discrepancy between complete degradation of pesticides under laboratory conditions and their persistence in the field. Laboratory studies in which soil samples are pooled and mixed to obtain a single "representative" sample can provide a simplified understanding of the process, but the complexity, particularly that of soil heterogeneity, of pesticide distribution and microbial degradation associated with prevailing climatic conditions, requires calibration of previously used methods in field studies and possibly at landscape, watershed, or regional scales. The scale-dependent degradation aspect will become even more important in the future; as soil properties and processes that control the toxicological aspects of contaminants include temperature and moisture, and changes associated with climate change will lead to an increase in extreme precipitation, longer dry periods, and soil erosion.
Development of a generic, model-based approach to optimize light distribution and productivity in strip-intercropping systems
(2014) Munz, Sebastian; Claupein, Wilhelm
Due to a growing world population, an extension of bioenergy production and the larger proportion of meat and dairy products in the human diet, with the latter particularly in India and China, the demand for agricultural products will further increase. Under decreasing resources and negative environmental impacts related to past intensification, more sustainable agricultural production systems need to be developed in order to meet the future demand for agricultural products. China, as the most populous nation with an enormous economic growth since the end of the 1970’s, plays a major role in global agricultural production. On a national level, agricultural production has to be increased by 35% during the next 20 years. However, land and water resources in China are very limited. With this in mind, the Sino-German International Research Training Group (IRTG) entitled ‘Modeling Material Flows and Production Systems for Sustainable Resource Use in Intensified Crop Production in the North China Plain’ was initiated by the Deutsche Forschungs-Gemeinschaft (DFG) and the Chinese Ministry of Education (MOE). The present doctoral thesis was embedded in the IRTG and focused, in particular, on exploring combinations of different crops produced on the same land at the same time, known as intercropping. In general, the higher productivity in intercropping, compared with monocropping, arises from the complementary use of resources (radiation, water, and nutrients) over space and time by crops that differ in physiology, morphology and phenology. The decisive question is how to optimize intercropping systems over space and time. To address this question, the present doctoral thesis combined field experiments with modeling approaches with the following aims: (i) to investigate the light availability on high temporal and spatial resolutions; (ii) to develop and validate a model that simulates the light availability for the smaller crop and accounts for the major aspects of cropping design; (iii) to determine the effect of the modified light availability on growth of maize and the smaller, shaded crop; (iv) to evaluate the plant growth model CROPGRO for its ability to simulate growth of the smaller, shaded crop; (v) to investigate the interactions between maize cultivar, cropping design and local growth conditions; and, (vi) to identify promising cropping designs and detect future research needs to increase the productivity of strip-intercropping systems. For this purpose, field experiments comprising of strip-intercropping with maize (Zea mays L.) and smaller vegetables, including bush bean (Phaseolus vulgaris L. var. nana), were carried out over three growing seasons from 2010-2012 in southwestern Germany and in the North China Plain. Growing the crops in strips facilitates mechanized management, addressing the ongoing decrease of intercropping in China due to labor scarcity in rural areas. The crop combination of maize, a tall C4-crop with erectophile leaves, and bush bean, a small, N-fixating C3-crop with a more horizontal leaf orientation, was chosen due to the large potential for a complementary resource use. Special emphasis was given on the competition for light as it plays a major role in this cropping system due to the large height differences between the crops. In this context, measurements of the photosynthetically active radiation (PAR) were conducted on high spatial (individual rows across the strip) and temporal resolutions (five-minute intervals) at the top of the bush bean canopy over a two-month co-growing period with maize. The collected data formed the basis of the simulation study towards investigating competition for light and its influence on plant growth with modeling approaches. Experimental results showed that maize yields increased in the border rows of the strip due to a higher lateral incoming radiation in years with a sufficient water supply. On average, maize yields calculated for strips consisting of 18 to four rows increased by 3 to 12% and 5 to 24% at the German and Chinese sites, respectively. Analysis of yield components revealed that yield increases in the border rows of the maize strip were mainly determined by a larger number of kernels per plant. On the other hand, shading by the taller adjacent maize induced considerable shade adaptations of bush bean, such as larger canopy dimensions and a substantially increased leaf area index due to thinner, larger leaves. These shade adaptations increased light interception, and indicated that bush bean could tolerate shading up to 30%, resulting in a total and pod dry matter similar to that of monocropped bush bean. These results suggested that there is a good potential for utilizing bush bean in strip-intercropping systems in combination with taller crops. However, higher shade levels (>40%) resulted in considerable decreases of total and pod dry matter. The high temporal and spatial resolution of the PAR measurements clearly revealed a highly heterogeneous diurnal distribution of PAR across the bush bean strip. The developed light model simulated this heterogeneity with a high accuracy under both clear and cloudy conditions. Comparison of simulated and observed hourly values of PAR across several rows within the strip of bush bean showed a root mean square error (RMSE) ranging between 47 and 87 μmol m-2 s-1 and a percent bias (PBIAS) ranging between -3.4 and 10.0%. Furthermore, the model reasonably captured the influence of different widths of the bush bean strip, strip orientations and maize canopy architecture (height, leaf area index, and leaf angle distributions). Simulations run for different latitudes and sky conditions, including different strips widths, maize canopy heights and leaf area indices (LAI), indicate that: (i) increasing the strip width might only reduce shading in the border rows of the smaller crop at lower latitudes under a high fraction of direct radiation; (ii) at higher latitudes, the selection of a maize cultivar with reduced height and LAI are suitable options to increase the light availability for the smaller crop. The present doctoral thesis presents the first approach to use the monocrop plant growth model CROPGRO to simulate growth of a legume crop grown in an intercropping system. The CROPGRO model was chosen because it provides an hourly simulation of leaf-level photosynthesis, and algorithms that account for the effects of radiation intensity on canopy dimensions and specific leaf area. CROPGRO, calibrated on data of monocropped bush bean, captured, quite well, the effects of the strongly reduced radiation on leaf area, and total and pod dry matter in the most shaded bush bean row. This indicated the models’ applicability on other intercropping systems exhibiting high levels of shading. Under a lower level of shading, cultivar and ecotype parameters had to be calibrated individually for a respective row within the bush bean strip to achieve a high accuracy of the simulations. Model simulations aided in explaining the effects arising from different shares of direct and diffuse radiation on canopy photosynthesis. This is a very important point to be further explored as diffuse radiation remains a part of light distribution and photosynthesis hardly studied in general; and, in particular, becomes more important with the increasing impact of shading. The simulation of the light availability, plant growth and yield formation within the strip of maize can be handled in a similar way as described for the smaller crop, bush bean. Modifications of the light model and a suitable plant growth model are presented and discussed. In conclusion, the main outcomes of this thesis indicate that the selection of cultivars adapted to the modified light environment have the largest potential to increase the productivity of strip-intercropped maize and bush bean. The most important characteristics of suitable maize cultivars include: (i) a high potential of kernel set; (ii) a higher water stress tolerance; and, (iii) reduced canopy height and LAI. The importance given to each of the components would subsequently be determined by the local weather and management conditions and the shade tolerance of the neighboring crop. On the other hand, to optimize yields of the smaller shaded crop, we present two options: (i) to modify the co-growing period of the intercrops temporarily to alleviate light competition during shade-sensitive growth stages; and, (ii) to modify the cropping design spatially and/or select different maize cultivars to reduce shading to the tolerated degree during the respective growth stage of the smaller crop. When the shade tolerance during the respective growth stages is determined, the light model developed can be used to optimize the cropping system temporarily and spatially. In this thesis, a promising approach, which combines a specific light partitioning model with process-oriented monocropping plant growth models, was developed. All models included in the approach can be applied at any location, and their generic nature also facilitates the integration of other crops. These attributes present a highly valuable contribution to intercropping research as their future optimization will depend strongly on the efficiency of the research efforts given: (i) the complexity of the underlying processes that determine the productivity; and, (ii) the minor share of time and money invested in intercropping research. Intercropping research has to prevent reinventing the wheel by identifying aspects in common with and already studied in monocropping systems and focus on aspects particularly inherent to intercropping systems.
Development of management strategies to control soil erosion in field grown vegetables with a focus on white cabbage (Brassica oleracea convar. capitata var. alba L.)
(2014) Übelhör, Annegret; Claupein, Wilhelm
Soil erosion by wind and water is a widely recognized problem throughout the world. Field grown vegetables, such as white cabbage (Brassica oleracea convar. capitata var. alba L.), are particularly endangered by soil erosion because of high disturbance tillage, including deep inversion tillage by the mouldboard plough. Furthermore, wide row spacing and late soil covering by leaves intensify the problem. In light of this, field experiments were conducted from 2011 to 2013 in southwest Germany to investigate, develop and adapt soil erosion control strategies, in particular for field grown vegetables, with white cabbage as a model crop. Focus was placed first, on the use of row covers (fleece and nets), which are usually used as frost protection or for pest control in organic farming, and second, on the development and adoption of strip-tillage for field grown vegetables, which combine the benefits from conventional tillage (high yields) and no-tillage (erosion control). Artificial rainfall simulations demonstrated a high erosion control by row covers. Soil loss under fleece cover was reduced on average by 76% and under net cover by 48% compared to the uncovered control treatment. In 2012, fresh matter head yield was significantly higher under fleece (80 t ha-1) than control treatment (66 t ha1). The opposite was found in 2013, with highest yield under the non-covered control (64 t ha-1) and lowest under fleece cover (53 t ha-1). A higher prevalence of diseases under row covers compared to the control was only found in 2012 with Sclerotinia sclerotiorum on 4% of cabbage heads under fleece cover. Soil loss under strip-tillage during artificial rainfall simulations in 2011 was reduced by an average of 80% compared to conventional tillage (512 g m-2). In 2012, soil losses were reduced by an average of 90% under non-intensive strip-tillage and by 48% under intensive strip-tillage compared to conventional tillage (210 g m-2). The fresh matter head yield in 2011 and 2013 under strip-tillage (58 t ha-1 and 57 t ha-1, respectively) was similar to conventional tillage (59 t ha-1 and 58 t ha-1, respectively). In 2012, cabbage yield was significantly higher under strip-tillage (74 t ha-1) than under conventional tillage (65 t ha-1). The intensive strip-tillage treatments with broadcast and band-placed nitrogen fertilization did not show a yield increase. Yield potential under band-placed fertilized strip-tillage was, at 67 t ha 1 (2012) and 50 t ha-1 (2013), the lowest within the strip-tillage treatments. The CROPGRO cabbage model was evaluated for cabbage production under temperate European climate conditions. After calibration of main parameters of phenology and plant growth, the model showed a high accuracy with indices of agreement mostly above d=0.94. Observed dry matter cabbage head yields of the different years and different locations ranged between 6574 kg ha-1 and 11926 kg ha 1 which were predicted by the model with an accuracy of R2=0.98. Also the sensitivity analysis, conducted under different nitrogen fertilizer amounts and different fertilizer application strategies, generated realistic values from an agronomic point of view. Overall, row covers and strip-tillage seem to be suitable for minimizing the erosion risk in vegetable production. The hypotheses of high erosion control under row covers and strip tillage can be accepted. Due to the modified microclimate under row covers, the infestation with pests and diseases can increase and the influence on cabbage growth can result in either a yield increase or decrease. Based on the study results, there is no evidence that the intensive, double-tilled strip-tillage treatment or the band-placed nitrogen fertilization lead to a yield increase. The non-intensive strip-tillage with only soil preparation in autumn showed the highest yield potential within the strip-tillage treatments, with similar or even higher yields than under conventional tillage. Furthermore, the CROPGRO cabbage model is suitable to simulate growth parameters and yield potential of white cabbage under temperate European climate conditions. For the future, due to the prediction of increased frequency of heavy rainfall events, soil conservation will focus increasingly on intensive crop production and farmers, particularly vegetables growers, will be increasingly dependent on erosion control strategies. For this reason, the approaches presented in this thesis can contribute significantly to produce field grown vegetables in a sustainable way that promotes soil protection.
Improved prediction of dietary protein use and nitrogen excretion in tropical dairy cattle
(2023) Salazar‐Cubillas, Khaterine; Dickhöfer, Uta
The overall objective of the present doctoral thesis was to evaluate the adequacy (i.e., accuracy and precision) of existing laboratory methodologies and modeling tools, originally designed for temperate systems, in predicting the nitrogen (N) supply and excretion of cattle in tropical husbandry systems. It was hypothesized that the adoption of laboratory methodologies and modeling tools from temperate systems without validating and adapting them for tropical systems may result in inaccurate estimations of N supply, utilization, and excretion, which will hamper the assessment of N use efficiency. An in vitro study was conducted to evaluate the adequacy of the chemical method (Sniffen et al., 1992; Kirchhof et al., 2010; Valdés et al., 2011) to predict rumen-undegraded crude protein (RUP) of tropical forages grasses and legumes (n = 23). The adequacy of the predictions was assessed by comparing them with RUP proportions measured in situ at rumen passage rates of 2, 5, and 8% per hour. Results showed that the RUP of tropical forages estimated with the in situ method can be predicted using the chemical method. However, regression equations developed for temperate forages were not adequate enough to predict RUP proportions of tropical forages consistently for all rumen passage rates. Instead, developed equations in the present thesis can be used to predict RUP proportion of tropical forages with a similar chemical composition than the reference forage sample set. A second in vitro study was conducted to evaluate the adequacy of the chemical (Sniffen et al., 1992; Zhao and Cao, 2004) and in vitro methods (Steingaß et al., 2001) to predict post-ruminal crude protein (PRCP) supply of tropical forages (n = 23). The adequacy of the PRCP supply with the chemical and in vitro methods were tested against PRCP supply estimated from in situ measurements at rumen passage rates of 2, 5, and 8% per hour and digested organic matter. Results showed that the in vitro method can be used as an alternative method to estimate PRCP supply in tropical forages at moderate to fast rumen passage rate but not at slow rumen passage rate. Available regression equations developed for temperate forages were not adequate enough to predict the PRCP supply of tropical forages from concentrations of chemical crude protein fractions. Instead, developed equations in the present thesis can be used to estimate PRCP supply of tropical forages with a similar chemical composition than the reference forage sample set. A third study was conducted to assess the adequacy of modeling tools to predict N excretion of cattle in tropical husbandry systems. These models, namely model A (based on AFRC, 1993), model G (based on GfE, 2001), and model I (INRA, 2019), were selected to predict fecal N (FN), urine N (UN), and total N (TN) excretion as well as FN fractions of dairy cows, heifers, and steers kept under typical tropical husbandry conditions. The adequacy of the model predictions was assessed against reference values of UN (total collection and creatinine method) and FN excretion (total collection, internal and external markers) (n = 392 observations). Adjustments were made to the models with the greatest potential to predict N excretion. The adjustments were focused on the input variables driving the variability in N excretion predictions, identified through a sensitivity analysis. None of the tested models predicts adequately the excretion of UN, FN, and of different FN fractions of individual cattle kept under tropical conditions. Instead, model I in the present thesis, adjusted for increased efficiency of rumen microbial crude protein synthesis and reduced intercept of FN prediction, can be used to estimate FN and TN excretion of individual cattle kept under tropical conditions. The findings from the present thesis partially support our hypothesis. The adjustment of laboratory methodologies, such as the chemical method used to estimate the protein value of temperate forages, to tropical forages, results in more adequate estimates of the proportion of RUP and PRCP supply of tropical forages. Model I is, therefore, able to predict the N excretion of cattle more adequately in tropical husbandry systems, because it is sensitive to differences in the RUP proportion and PRCP supply. In addition to increasing the adequacy of these input variables, adjustments made to the microbial protein synthesis and intercept of the FN excretion of model I results in a more adequate prediction of N excretion by cattle in tropical husbandry systems. However, not all adjustments to laboratory methodologies and modeling tools from temperate systems yielded adequate predictions. Specifically, challenges remained in predicting RUP proportion and PRCP supply for tropical forage legume with slow rumen passage rates, as well as urinary N excretion in cattle with low N intakes. Consequently, further research is required to identify the factors contributing to their poor adequacy.
Land use management under climate change : a microeconomic analysis with emphasis on risk
(2018) Reinmuth, Evelyn; Dabbert, Stephan
This cumulative dissertation was conducted under a grant from the German Research Foundation (DFG) for the research group FOR 1695 - “Agricultural Landscapes under Global Climate Change – Processes and Feedbacks on a Regional Scale”. The goal of the sub-project from which this dissertation stems from was to explore, extend and strengthen the scientific basis for learning and risk strategies and the adaptation behavior of farmers’ economic planning decisions in crop production under the influence of climate change. The integrated bioeconomic simulation model FarmActor, was to be used as an experimental tool to develop an interdisciplinary methodological approach supported by empirical work in two study regions in Southwest Germany, the Kraichgau and the Swabian Alb. This dissertation examines risk in the context of land use management and specifically crop production. Risk in this context is related to how outcome distributions are affected by climatic influences. Risk strategies assess these contributions and account for them in the resulting decisions. The thesis is written as a cumulative dissertation and is composed of five articles. Four articles have been published by peer-reviewed journals. A fifth article has been published as a peer-reviewed conference proceeding. The article at fifth place represents the results of the main focus of this dissertation as presented in the following. Available economic models assume that farmers assess climatic risks only through yields or costs when building their land use management risk strategy for crop production. However, the available methodological approaches have been criticized for either under- or overestimating farmers’ actual behavior. In reality, and as a basis for field allocation planning, farmers have additional knowledge from monitoring crop development throughout the whole season. Yield is actually just the last point in a long sequence of (economic) evaluative observations about the production process. This influences how farmers define not only the riskiness of a yield distribution but also its costs. We hypothesize that, because it is not possible to methodologically integrate process evaluations in economic planning decisions, models lack performance, and as a consequence, it is very difficult to conduct proper research on the climate’s influences on land use management decisions. In this original research, we present a newly developed downside risk measure based on evaluations throughout the production process that can be included in the planning process as an additional parameter—so-called Annual Risk Scores. A comparative static analysis was performed to demonstrate how ARS scores assess future climatic conditions in the example of winter wheat production in the Kraichgau region as supported by empirical data. It was shown that the mechanism is sensitive to different climatic conditions. Furthermore, the ARS scores provide a different picture of climatic influence compared to an analysis based only on yields. The last article presented in this dissertation represents an integrative review that promotes more efficient model development and the reuse of newly developed methodologies in the field of integrated bio-economic simulation models. The review is based on lessons learned from working with the simulation model. Thus, the intended and outstanding full implementation of the ARS mechanism is presented in the last part of the synthesis, where we advise including the ARS scores as another constraint in the field allocation mechanisms of the FarmActor model. This is expected to improve the integration of both bio-physical and economic dimensions for complex integrated bio-economic simulation models.
Nachhaltige Biogasproduktion unter besonderer Berücksichtigung des Einsatzes von Zuckerrüben und Grünlandaufwuchs sowie der Gärrestverwertung
(2017) Auburger, Sebastian; Bahrs, Enno
The present cumulative dissertation assesses the sustainability of biogas production in Germany from different points of view. A special focus is brought to sugar beets and grassland as a biogas feedstock as well as to biogas residue utilization. Chapter 2 presents an approach of manure distribution within regions based on municipality biogas and livestock production data. The developed algorithm distributes nutrients of nutrient surplus municipalities to municipalities with nutrient adsorption capacity within a study area (Lower Saxonia and North Rhine-Westphalia). It was shown that farmers and biogas producers will be confronted with higher manure and biogas digestate disposal costs. Chapter 3 enlarges the view by taking pig producers and experts interviews into consideration. Chapter 4 presents an approach to determine the regional biogas feedstock input based on regional agricultural production cost data and almost 8,000 biogas plants in Germany. By using a linear optimization approach regional feedstock inputs are calculated. Furthermore greenhouse gas emissions of power production based on biogas are estimated. Chapter 5 enlarges the modeling approach by an energy balancing tool and assesses sugar beets as an energy crop for biogas production in detail. Therefore different scenarios are taken into account. Silage corn was the most competitive feedstock over almost every region in Germany. Round about 160 kg CO2eq per kWh from biogas production were calculated, which is a significant lower value in comparison to greenhouse gas emissions from current power mix in Germany. Chapter 6 focuses on grassland as a biogas feedstock. Based on data availability calculation had to be restricted to Federal States of Schleswig-Holstein, Lower Saxonia and Bavaria. Results show that grassland is a competitive biogas feedstock in regions, which are characterized by unfavorable production circumstances of silage corn and only if for grassland favorable scenario assumptions are chosen.
Ökonomische Bewertung regionaler Wettbewerbspotentiale verschiedener landwirtschaftlicher Biomassen im Rahmen der Bioökonomie unter besonderer Berücksichtigung Baden-Württembergs
(2020) Petig, Eckart; Bahrs, Enno
The finite nature of fossil resources and climate change pose major challenges to the global society and require a comprehensive transformation of the current economic system. One important aspect of this transformation, also known as bioeconomy, is the transition from a fossil-based to a bio-based supply of raw materials. In this context, agricultural production represents an important supplier of raw materials, which in Germany is already characterized by a strong competition for the scarce land. The scarce land is a major challenge of the expansion of the use of agricultural biomass for the bioeconomy. Accordingly, the derivation of the potential of agricultural biomass for bioeconomy requires consideration of the tradeoffs between various utilization paths. In this context, economic models can be valuable methods, which on one hand are able to depict the trade-offs of different value chains and can, on the other hand, incorporate the uncertainty by developing suitable scenarios. The aim of this thesis is the evaluation of the potential of different agricultural biomasses for the bioeconomy and to analyze the associated effects on agricultural production structures in Baden-Wuerttemberg. In chapter 2 the potential of grassland as a biogas substrate is evaluated, which might be important for the bioeconomy in the future. Due to the more complex harvesting process and partly unfavorable production conditions, grassland has higher production costs compared to arable biogas substrates. The consideration of iLUC Factors with high prices for GHG emissions could improve the competitiveness of grassland to such an extent that it is competitive with the production of biogas substrates on arable land. However, silage maize is often the more favorable biogas substrate in many respects, as chapter 3 shows by means of a site modeling for biogas plants in Baden-Wuerttemberg. In chapter 4 and 5 the potential of straw for energetic and material use is analyzed. These investigations are based on the combination of EFEM with the techno-economic location optimization model BIOLOCATE. The results clearly show the interaction between the economies of scale and the rising raw material supply costs. On the one hand, the average investment costs decrease with increasing plant size, but on the other hand the raw material costs increase, because the transport distances increase and an increasing demand for biomass results also in higher market prices. Additionally, the results show that straw can make a fundamental contribution to the bioeconomy by providing regional bioenergy and as feedstock for material value chains. However, even the use of by-products can have effects on cultivation structures and thus, reduce the production of agricultural biogas substrates, among other things. In Chapter 6 the effects of macroeconomic expansion paths of the bioeconomy on agricultural production structures in Baden-Wuerttemberg are investigated. For this purpose, the results of an iterative model coupling between the agricultural sector model ESIM and the energy sector model TIMES-PanEU of four bioeconomic scenarios are scaled down from national level to regional and farm level using EFEM. The results show different impacts on farm types and thus illustrate the advantages of a differentiated analysis of the expansion of the bioeconomy. Therefore, farms with mainly extensive production methods such as suckler cow husbandry do not profit from the expansion of the bioeconomy due to unfavorable production conditions, while especially large arable farms in fertile regions would benefit disproportional more than the average. Basically, the results reveal limits to the mobilization of additional biomass potential. The reason for this is the already high cultivation intensity of agricultural production in Germany, in which the expansion of one production restricts production of another due to competition for the limited agricultural land. For grassland, the results show that the decline in grassland-based cattle farming and unfavorable economic conditions can lead to a significant increase of unused grassland. Grassland thus presents itself as a promising resource for biomass production for the bioeconomy, as it can provide important ecosystem services (e.g. biodiversity) in addition to the provision of raw materials. However, a political framework has to be established that promotes ecological services accordingly. Finally, in chapter 7 additional research needs are identified, which include further development of the methodological approach. These comprise an extension of the analysis by macroeconomic models to integrate interactions with the material use in a more detailed way. Furthermore, an integration of ecological parameters is necessary for a holistic analysis in the context of bioeconomy.
Rubber production in Continental Southeast Asia : its potentialities and limitations
(2019) Golbon, Reza; Sauerborn, Joachim
This thesis focuses on three climate-related aspects of Para rubber (Hevea brasiliensis) cultivation in areas where altitudes and latitudes higher than its endemic range create conditions which are labeled nontraditional, suboptimal or marginal for rubber cultivation: 1. rubber yield in relation to the meteorological conditions preceding harvest events, 2. potential geographical shifts in rubber cultivation through climate change and 3. assessment of climate driven susceptibility to South American leaf blight (Pseudocercospora ulei) of rubber.
Use of modeling to characterize phenology and associated traits among wheat cultivars
(2008) Herndl, Markus; Claupein, Wilhelm
Predicting phenology of wheat is important for many aspects of wheat production as for example facilitating accurate timing of pesticides, fertilizers and irrigation, avoiding stress at critical growth stages, and adapting cultivar characteristics to specific environmental constraints or global changes in climate. The aim of the dissertation was to characterize and test the impact of wheat phenology on agronomic traits through integrated use of crop models and information on the genetic makeup of cultivars. In an initial study, cultivar differences in vernalization requirement, photoperiod response and earliness per se were distinguished by field-based indices and compared with corresponding model parameters in CSM-Cropsim-CERES-Wheat model Version 4.0.2.0. To determine whether field-based indices can provide accurate characterization of vernalization requirement, photoperiod response and earliness per se, 26 winter wheat cultivars were evaluated under field conditions at Ihinger Hof, Germany using two natural photoperiod regimes (from different transplanting dates) and vernalization pre-treatments. Results indicated that combining planting dates with vernalization pre-treatments can permit reliable, quantitative characterization of vernalization requirement, photoperiod response and earliness per se of wheat cultivars. Furthermore, genotypic model parameters appeared to be reliable estimates of cultivar differences in response to vernalization and photoperiod. In a second study, the model parameters for vernalization requirement (P1V) and photoperiod response (P1D) were estimated using gene information. To estimate these model parameters through integrating effects of Vrn and Ppd loci, flowering data obtained for 29 cultivars tested in the International Winter Wheat Performance Nursery (IWWPN) were used. Summarizing, results indicated that gene-based estimation of model coefficients was effective for prediction of phenology over a wide range of environments and appears feasible for studying wheat response to environment. To assist plant breeding with crop models, a possibility could be to assess model parameters for designing improved plant types (ideotypes). CMS-Cropsim-CERES-Wheat was used in a third study to test model parameters concerning plant development and grain yield. In ideotyping sequences, the parameters were varied and the model was run in four different scenarios in the North China Plain. The parameter G1 (corresponding trait: kernel number per spike) showed the highest influence on yield over all scenarios followed by G2 (corresponding trait: kernel weight). Results obtained in this study could help breeders to select the relevant traits and integrate them in their breeding program for a specific population of environments. To investigate the coherences between pre-anthesis phenology and grain protein content in a fourth study the statistical analysis of causal relationships with genotypic model parameters was used. It was tested whether model-based characterizations of vernalization requirement, photoperiod response and earliness per se can help explain genotype x environment interactions for grain protein content. Twenty four winter wheat and five spring wheat cultivars (IWWPN) and twelve winter wheat cultivars (of a two year field study at Ihinger Hof, Germany) were characterized using CSM-Cropsim-CERES-Wheat. Covariance analyses indicated that vernalization requirement, photoperiod response, and earliness per se all influenced grain protein content, but their effects varied with site and year within region. Path analyses using data from two seasons in Germany confirmed that grain protein content increased with a shorter pre-anthesis phase and indicated in accordance with the covariance analyses the environmental dependence of this trait. The results proposed that efforts to improve grain protein content should target levels of vernalization requirement, photoperiod sensitivity and earliness per se to specific populations of environments and seek to reduce the apparent large influence of environment on grain protein content. The improved understanding of traits affecting phenology and the linkage with genotypic model parameters can be applied e.g. in China to solve arising and existing agricultural challenges. Model-based analyses can help adapting cropping systems to global warming. In the North China plain a more accurate timing of N-fertilizers and irrigation, as a result of modeling, can ensure a sustainable resource use while maintaining high yields. Summarizing, the findings of this dissertation showed that traits affecting phenology in wheat can be successfully characterized by field-based indices, genotypic model parameters and gene-based estimates of genotypic model parameters. Furthermore, the research showed how genotypic model parameters can be used for breeding purposes, and to test causal relationships both at regional and local geographic scales.