Records 1 - 20 / 2412
|Farming with mathematical precision
Mourik, S. van; Groot Koerkamp, P.W.G. ; Henten, E.J. van - \ 2018
precision farming - mathematical - models
mA key challenge in precision farming is complex decision making under variable and uncertain circumstances. A possible solution is offered by mathematical models and algorithms.
A Tier 3 Method for Enteric Methane in Dairy Cows Applied for Fecal N Digestibility in the Ammonia Inventory
Bannink, A. ; Spek, J.W. ; Dijkstra, J. ; Sebek, L.B. - \ 2018
Frontiers in Sustainable Food Systems 2 (2018). - ISSN 2571-581X - 14 p.
models - Tier 3 - dairy cows - nitrogen digestibility - Nitrogen excretion
The current inventory of N emission from cow excreta relies on fecal N digestibility data in Dutch feeding tables, assuming additivity of dietary ingredients to obtain diet values (CVB model). Alternatively, fecal N digestibility can be estimated by a dynamic, mechanistic model of digestion in the gastrointestinal tract, currently used as Tier 3 for enteric methane prediction in the Netherlands (Tier 3 model). Estimates of in situ rumen degradation characteristics for starch, neutral detergent fiber (NDF) and crude protein used as an input for the Tier 3 model were based on Dutch feeding tables (the protein evaluation system). Both methods were evaluated on independent dataset on fecal N digestibility that was constructed from peer-reviewed papers on N balance data for dairy cows published since 1999 (54 trials, 242 treatment means). Results indicate that observed apparent fecal N digestibility (67.0 ± 6.77%) was systematically over-predicted in particular by the CVB model (73.8 ± 4.35%) compared to the Tier 3 model (69.8 ± 4.52%). For the dataset including only observations from Dutch trials the observed fecal N digestibility (70.4 ± 7.33%) was also systematically over-predicted by the CVB model (76.4 ± 5.27%) but not by the Tier 3 model (69.7 ± 5.81%). Mixed model analysis with study as random factor indicated the slope of the regression between observed and predicted fecal N digestibility to be smaller than 1, in particular for the CVB model (CVB model slope varied between 0.405 and 0.560 and Tier 3 model slope between 0.418 and 0.657). The over-prediction by the CVB model with 6–7%-units of digestibility will lead to an over-predicted ammoniacal N excretion (urinary N) in the ammonia inventory, and biased estimation of N mitigating potential of nutritional measures. The present study demonstrates the benefit of using the Tier 3 model to predict the average level of apparent fecal N digestibility compared to the CVB model. The general estimates of in situ rumen degradation characteristics for starch, NDF and crude protein used as input for the Tier 3 model seemed applicable for the Dutch trials but less so for the non-Dutch trials.
Modeling the Effect of Nutritional Strategies for Dairy Cows on the Composition of Excreta Nitrogen
Dijkstra, J. ; Bannink, A. ; Bosma, Pieter M. ; Lantinga, E.A. ; Reijs, J.W. - \ 2018
Frontiers in Sustainable Food Systems 2 (2018). - ISSN 2571-581X
models - Dairy cattle - Feces - Urine - diet composition - Manure composition
For an integrated evaluation of the effect of nutritional strategies on the utilization and losses of N at dairy farms, reliable estimates of excreta production and composition are indispensable. An extant, dynamic, mechanistic model of rumen functioning was extended with static equations that describe intestinal digestion to simulate the composition of dairy cow feces and urine as a function of diet composition. The extended model predicts organic matter (OM), carbon (C), and nitrogen (N) output of both feces and urine, classified in different components. Total N excretion was partitioned in three fractions based on the C:N ratio of individual components representing their availability of N following manure application to crops, viz. NM (immediately available), NE (easily decomposable), and NR (resistant). Forty nutritional strategies for stall-fed dairy cows, covering diets with a wide range in protein content and OM digestibility, were evaluated. The simulated ranges in fecal and urinary composition were largely in line with values reported in literature. Diet intake and composition had a substantial effect on simulated total N excretion and excreta composition, mainly because of differences in the level of NM excretion and the C:N ratio of the NR fraction. Furthermore, it was shown that the type of OM excreted varies considerably between different diets. A simplified simulation of degradation processes during the first 4 months of excreta storage produced average values and ranges of slurry characteristics that were in line with values reported in literature. The simulated variation in slurry characteristics suggested a strong variability in ammonia N losses from the slurry pit and a moderate variability in plant availability of slurry N. Further efforts are required to integrate effects of manure storage conditions on the storage processes. In conclusion, the model can be a tool to predict fecal and urinary composition of cattle, and ultimately to improve the utilization of N from field applied manure as well as to evaluate the effects of different nutritional strategies on the whole-farm N balance.
Invited review: A position on the Global Livestock Environmental Assessment Model (GLEAM)
MacLeod, M.J. ; Vellinga, T. ; Opio, C. ; Falcucci, A. ; Tempio, G. ; Henderson, B. ; Makkar, H. ; Mottet, A. ; Robinson, T. ; Steinfeld, H. ; Gerber, P.J. - \ 2018
Animal 12 (2018)2. - ISSN 1751-7311 - p. 383 - 397.
climate change - environmental assessment - life-cycle analysis - livestock - models
The livestock sector is one of the fastest growing subsectors of the agricultural economy and, while it makes a major contribution to global food supply and economic development, it also consumes significant amounts of natural resources and alters the environment. In order to improve our understanding of the global environmental impact of livestock supply chains, the Food and Agriculture Organization of the United Nations has developed the Global Livestock Environmental Assessment Model (GLEAM). The purpose of this paper is to provide a review of GLEAM. Specifically, it explains the model architecture, methods and functionality, that is the types of analysis that the model can perform. The model focuses primarily on the quantification of greenhouse gases emissions arising from the production of the 11 main livestock commodities. The model inputs and outputs are managed and produced as raster data sets, with spatial resolution of 0.05 decimal degrees. The Global Livestock Environmental Assessment Model v1.0 consists of five distinct modules: (a) the Herd Module; (b) the Manure Module; (c) the Feed Module; (d) the System Module; (e) the Allocation Module. In terms of the modelling approach, GLEAM has several advantages. For example spatial information on livestock distributions and crops yields enables rations to be derived that reflect the local availability of feed resources in developing countries. The Global Livestock Environmental Assessment Model also contains a herd model that enables livestock statistics to be disaggregated and variation in livestock performance and management to be captured. Priorities for future development of GLEAM include: improving data quality and the methods used to perform emissions calculations; extending the scope of the model to include selected additional environmental impacts and to enable predictive modelling; and improving the utility of GLEAM output.
Citizen science and remote sensing for crop yield gap analysis
Beza, Eskender Andualem - \ 2017
Wageningen University. Promotor(en): M. Herold, co-promotor(en): L. Kooistra; P. Reidsma. - Wageningen : Wageningen University - ISBN 9789463436410 - 196
crop yield - maximum yield - yield forecasting - remote sensing - models - small farms - data collection - gewasopbrengst - maximum opbrengst - oogstvoorspelling - remote sensing - modellen - kleine landbouwbedrijven - gegevens verzamelen
The world population is anticipated to be around 9.1 billion in 2050 and the challenge is how to feed this huge number of people without affecting natural ecosystems. Different approaches have been proposed and closing the ‘yield gap’ on currently available agricultural lands is one of them. The concept of ‘yield gap’ is based on production ecological principles and can be estimated as the difference between a benchmark (e.g. climatic potential or water-limited yield) and the actual yield. Yield gap analysis can be performed at different scales: from field to global level. Of particular importance is estimating the yield gap and revealing the underlying explanatory factors contributing to it. As decisions are made by farmers, farm level yield gap analysis specifically contributes to better understanding, and provides entry points to increased production levels in specific farming systems. A major challenge for this type of analysis is the high data standards required which typically refer to (a) large sample size, (b) fine resolution and (c) great level of detail. Clearly, obtaining information about biophysical characteristics and crop and farm management for individual agricultural activities within a farm, as well as farm and farmer’s characteristics and socio-economic conditions for a large number of farms is costly and time-consuming. Nowadays, the proliferation of different types of mobile phones (e.g., smartphones) equipped with sensors (e.g., GPS, camera) makes it possible to implement effective and low-cost “bottom-up” data collection approaches such as citizen science. Using these innovative methodologies facilitate the collection of relatively large amounts of information directly from local communities. Moreover, other data collection methods such as remote sensing can provide data (e.g., on actual crop yield) for yield gap analysis.
The main objective of this thesis, therefore, was to investigate the applicability of innovative data collection approaches such as crowdsourcing and remote sensing to support the assessment and monitoring of crop yield gaps. To address the main objective, the following research questions were formulated: 1) What are the main factors causing the yield gaps at the global, regional and crop level? 2) How could data for yield gap explaining factors be collected with innovative “bottom-up” approaches? 3) What are motivations of farmers to participate in agricultural citizen science? 4) What determines smallholder farmers to use technologies (e.g., mobile SMS) for agricultural data collection? 5) How can synergy of crowdsourced data and remote sensing improve the estimation and explanation of yield variability?
Chapter 2 assesses data availability and data collection approaches for yield gap analysis and provides a summary of yield gap explaining factors at the global, regional and crop level, identified by previous studies. For this purpose, a review of yield gap studies (50 agronomic-based peer-reviewed articles) was performed to identify the most commonly considered and explaining factors of the yield gap. Using the review, we show that management and edaphic factors are more often considered to explain the yield gap compared to farm(er) characteristics and socio-economic factors. However, when considered, both farm(er) characteristics and socio-economic factors often explain the yield gap. Furthermore, within group comparison shows that fertilization and soil fertility factors are the most often considered management and edaphic groups. In the fertilization group, factors related to quantity (e.g., N fertilizer quantity) are more often considered compared to factors related to timing (e.g., N fertilizer timing). However, when considered, timing explained the yield gap more often. Finally, from the results at regional and crop level, it was evident that the relevance of factors depends on the location and crop, and that generalizations should not be made. Although the data included in yield gap analysis also depends on the objective, knowledge of explaining factors, and methods applied, data availability is a major limiting factor. Therefore, bottom-up data collection approaches (e.g., crowdsourcing) involving agricultural communities can provide alternatives to overcome this limitation and improve yield gap analysis.
Chapter 3 explores the motivations of farmers to participate in citizen science. Building on motivational factors identified from previous citizen science studies, a questionnaire based methodology was developed which allowed the analysis of motivational factors and their relation to farmers’ characteristics. Using the developed questionnaire, semi-structured interviews were conducted with smallholder farmers in three countries (Ethiopia, Honduras and India). The results show that for Indian farmers a collectivistic type of motivation (i.e., contribute to scientific research) was more important than egoistic and altruistic motivations. For Ethiopian and Honduran farmers an egoistic intrinsic type of motivation (i.e., interest in sharing information) was most important. Moreover, the majority of the farmers in the three countries indicated that they would like to receive agronomic advice, capacity building and seed innovation as the main returns from the citizen science process. Country and education level were the two most important farmers’ characteristics that explained around 20% of the variation in farmers’ motivations. The results also show that motivations to participate in citizen science are different for smallholders in agriculture compared to other sectors. For example fun has appeared to be an important egoistic intrinsic factor to participate in other citizen science projects, the smallholder farmers involved in this research valued ‘passing free time’ the lowest.
Chapter 4 investigates the factors that determine farmers to adopt mobile technology for agricultural data collection. To identify the factors, the unified theory of acceptance and use of technology (UTAUT2) model was employed and extended with additional constructs of trust, mastery-approach goals and personal innovativeness in information technology. As part of the research, we setup data collection platforms using open source applications (Frontline SMS and Ushahidi) and farmers provided their farm related information using SMS for two growing seasons. The sample for this research consisted of group of farmers involved in a mobile SMS experiment (n=110) and another group of farmers which was not involved in a mobile SMS experiment (n=110), in three regions of Ethiopia. The results from the structural equation modelling showed that performance expectancy, effort expectancy, price value and trust were the main factors that influence farmers to adopt mobile SMS technology for agricultural data collection. Among these factors, trust is the strongest predictor of farmer’s intention to adopt mobile SMS. This clearly indicates that in order to use the citizen science approach in the agricultural domain, establishing a trusted relationship with the smallholder farming community is crucial. Given that performance expectancy significantly predicted farmer’s behavioural intention to adopt mobile SMS, managers of agricultural citizen science projects need to ensure that using mobile SMS for agricultural data collection offers utilitarian benefits to the farmers. The importance of effort expectancy on farmer’s intention to adopt mobile SMS clearly indicates that mobile phone software developers need to develop easy to use mobile applications.
Chapter 5 demonstrates the results of synergetic use of remote sensing and crowdsourcing for estimating and explaining crop yields at the field level. Sesame production on medium and large farms in Ethiopia was used as a case study. To evaluate the added value of the crowdsourcing approach to improve the prediction of sesame yield using remote sensing, two independent models based on the relationship between vegetation indices (VIs) and farmers reported yield were developed and compared. The first model was based on VI values extracted from all available remote sensing imagery acquired during the optimum growing period (hereafter optimum growing period VI). The second model was based on VI values extracted from remote sensing imagery acquired after sowing and before harvest dates per field (hereafter phenologically adjusted VI). To select the images acquired between sowing and harvesting dates per field, farmers crowdsourced crop phenology information was used. Results showed that vegetation indices derived based on farmers crowdsourced crop phenology information had a stronger relationship with sesame yield compared to vegetation indices derived based on the optimum growing period. This implies that using crowdsourced information related to crop phenology per field used to adjust the VIs, improved the performance of the model to predict sesame yield. Crowdsourcing was further used to identify the factors causing the yield variability within a field. According to the perception of farmers, overall soil fertility was the most important factor explaining the yield variability within a field, followed by high presence of weeds.
Chapter 6 discusses the main findings of this thesis. It draws conclusions about the main research findings in each of the research questions addressed in the four main chapters. Finally, it discusses the necessary additional steps (e.g., data quality, sustainability) in a broader context that need to be considered to utilize the full potential of innovative data collection approaches for agricultural citizen science.
Control mechanisms of microtubule overlap regions
Jongerius, Aniek - \ 2017
Wageningen University. Promotor(en): M.E. Janson. - Wageningen : Wageningen University - ISBN 9789463436113 - 133
microtubuli - celbiologie - plantencelbiologie - modellen - cellen - microtubules - cellular biology - plant cell biology - models - cells
Microtubule organization in cells is an important process. An example of careful microtubule organization is the mitotic spindle. The spindle is a bipolar structure with microtubules emanating from the poles at both sides. These microtubules form antiparallel overlaps in the centre of the spindle where they are bundled by bundling proteins. The overlaps are centred in the spindle and their constant length is regulated. The overlaps are important for the stability of the microtubule network, without bundling proteins the overlaps are lost and the spindle collapses. The antiparallel overlaps are also the site where microtubules slide apart to induce spindle elongation. Sliding is induced by tetrameric motor proteins that can bind to two bundled microtubules. Spindle elongation also requires microtubule growth at the overlaps. All these different functions at the overlap, sliding, growth/shrinkage and bundling, have to cooperate to maintain overlap length. While sliding reduces overlap length, growth will increase overlap length. These activities have to be coordinated for the maintenance of a constant overlap length. We propose that a feedback mechanism is present where growth of the microtubules is limiting the sliding in the overlap. This would prevent sliding when the overlap decreases and helps to maintain the overlap. We designed in vitro experiments to make antiparallel overlaps in vitro. In these experiments we use purified proteins from S. pombe. We combine ase1 and klp9 in a relative sliding assay to mimic the sliding in the midzone. In our experiments we combine relative sliding with dynamic microtubules for the first time. This allows us to test how these activities are coordinated. In other experiments we combine ase1 and cls1 with dynamic microtubules to see if the rescue activity of cls1 can be confined to the overlaps. Furthermore, interactions between motor proteins and diffusive proteins are investigated on single microtubules.
How virtual shade sheds light on plant plasticity
Bongers, Franca J. - \ 2017
Wageningen University. Promotor(en): N.P.R. Anten, co-promotor(en): R. Pierik; J.B. Evers. - Wageningen : Wageningen University - ISBN 9789463432047 - 140
planten - fenotypen - fenotypische variatie - modellen - arabidopsis - natuurlijke selectie - schaduw - reacties - concurrentie tussen planten - licht - plants - phenotypes - phenotypic variation - models - arabidopsis - natural selection - shade - responses - plant competition - light
Phenotypic plasticity is the ability of a genotype to express multiple phenotypes in accordance with different environments. Although variation in plasticity has been observed, there is limited knowledge on how this variation results from natural selection. This thesis analyses how variation in the level of plasticity influences light competition between plants and how this variation could result from selection, driven by light competition, in various environments. As an exemplary case of phenotypic plasticity, this thesis focusses on phenotypic responses of the annual rosette plant Arabidopsis thaliana (Brassicaceae) in response to the proximity of neighbour plants, as signalled through the red : far—red (R:FR) ratio, which are responses associated with the shade avoidance syndrome (SAS).
Plant experiments were conducted to measure variation in these plastic responses and a functional-structural plant (FSP) model was created that simulates plant structures in 3D and includes these organ-level plastic responses while simulating explicitly a heterogeneous light environment. Simulating individual plants that explicitly compete for light, while their phenotype changes through plasticity, gave insights in the role of the level of phenotypic plasticity and site of signal perception on plant competitiveness. In addition, an analysis on how natural selection in different environments acts on the level of plasticity was performed by combining FSP simulations and evolutionary game theoretical (EGT) principles.
Scoping studie mestverdelingsmodule
Kros, Hans ; Groenendijk, Piet - \ 2017
Wageningen : Wageningen Environmental Research (Wageningen Environmental Research rapport 2817) - 47
mest - modellen - modules - manures - models - modules
Emissies naar lucht uit de landbouw in 2014 : berekeningen met het model NEMA
Bruggen, C. van; Bannink, A. ; Groenestein, C.M. ; Huijsmans, J.F.M. ; Luesink, H.H. ; Oude Voshaar, S.V. ; Sluis, S.M. van der; Velthof, G.L. ; Vonk, J. - \ 2017
Wageningen : Statutory Research Tasks Unit for Nature & the Environment (WOt-technical report 90) - 96
ammoniak - landbouw - emissie - mest - distikstofmonoxide - dierhouderij - modellen - nederland - ammonia - agriculture - emission - manures - nitrous oxide - animal husbandry - models - netherlands
Landbouwkundige activiteiten zijn in Nederland een belangrijke bron van ammoniak (NH3), stikstofoxide (NO), lachgas (N2O),methaan (CH4) en fijnstof (PM10 en PM2,5). De emissies in 2014 zijn berekend met het National Emission Model for Agriculture(NEMA). Tegelijk zijn enkele cijfers in de reeks 1990-2013 aangepast op basis van nieuwe inzichten. De rekenmethodiek gaatbij de berekening van de ammoniakemissie uit dierlijke mest uit van de hoeveelheid totaal ammoniakaal stikstof (TAN) in demest. De ammoniakemissie uit dierlijke mest, kunstmest en overige bronnen in 2014 bedroeg 121 miljoen kg NH3, bijna4 miljoen kg meer dan in 2013. De stijging komt voornamelijk door uitbreiding van de melkveestapel en een hogerstikstofgehalte van het ruwvoer. De N2O-emissie nam toe van 19,1 miljoen kg in 2013 naar 19,4 miljoen kg in 2014. De NOemissienam toe van 16,9 naar 17,2 miljoen kg. De methaanemissie nam iets toe van 499 tot 503 miljoen kg. De emissie vanfijnstof nam licht toe van 6,3 miljoen kg PM10 tot 6,4 miljoen kg, door een toename van het aantal stuks pluimvee. De emissievan PM2,5 bedroeg in beide jaren 0,6 miljoen kg. Sinds 1990 is de ammoniakemissie uit dierlijke mest en kunstmest mettweederde gedaald, vooral door een lagere stikstofuitscheiding door landbouwhuisdieren en emissiearme mesttoediening.Emissies van lachgas en stikstofoxide daalden in dezelfde periode eveneens, maar minder sterk (ca. 40%) omdat doorondergronds toedienen van mest de emissies hoger zijn geworden en door de omschakeling van stalsystemen met dunne naarvaste mest bij pluimvee. Tussen 1990 en 2014 daalde de emissie van methaan met 16% door een afname in de dieraantallenen een hogere voeropname en productiviteit van melkvee---Agricultural activities are in the Netherlands a major source of ammonia (NH3), nitrogen oxide (NO), nitrous oxide (N2O),methane (CH4) and particulate matter (PM10 and PM2.5). The emissions in 2014 were calculated using the National EmissionModel for Agriculture (NEMA). At the same time some figures in the time series 1990-2013 were revised. The method calculatesthe ammonia emission from livestock manure on the basis of the total ammonia nitrogen (TAN) content in manure. Ammoniaemissions from livestock manure, fertilizers and other sources in 2014 were 121 million kg, which was almost 4 million kghigher than in 2013, mainly due to expansion of the dairy herd and a higher N-content of roughage. N2O emissions increasedfrom 19.1 million kg in 2013 to 19.4 million kg in 2014. NO emission increased slightly from 16.9 to 17.2 million kg. Methaneemissions increased from 499 to 503 million kg. Emissions of particulate matter increased slightly from 6.3 to 6.4 million kgPM10 as a result of higher poultry numbers. Emission of PM2.5 in both years was 0.6 million kg. Ammonia emissions fromlivestock manure in the Netherlands dropped by almost two thirds since 1990, mainly as a result of lower nitrogen excretionrates by livestock and low-emission manure application. Nitrous oxide and nitrogen oxide also fell over the same period, butless steeply (by about 40%), due to higher emissions from manure injection into the soil and to the shift from poultry housingsystems based on liquid manure to solid manure systems. Methane emissions fell by 16% between 1990 and 2014 caused by adrop in livestock numbers and increased feed uptake and productivity of dairy cattle
A blooming business : Identifying limits to Lake Taihu's nutrient input
Janssen, Annette B.G. - \ 2017
Wageningen University. Promotor(en): Wolf Mooij, co-promotor(en): J.H. Janse; A.A. van Dam. - Wageningen : Wageningen University - ISBN 9789463431897 - 268
lakes - freshwater ecology - aquatic ecosystems - nutrients - cycling - nutrient flows - biodiversity - algae - models - critical loads - limnology - spatial variation - ecological restoration - china - meren - zoetwaterecologie - aquatische ecosystemen - voedingsstoffen - kringlopen - nutriëntenstromen - biodiversiteit - algen - modellen - critical loads - limnologie - ruimtelijke variatie - ecologisch herstel - china
Last century, Lake Taihu (China) became serious eutrophic due to excessive nutrient input. During the 1980s, the first algal blooms emerged in the lake, reaching disastrous proportions in 2007. During that year, the intake of drinking water had to be shut down and millions of people had to look for an alternative source of drinking water. This raises the question whether such problems can be avoided. Of crucial importance in avoiding and reducing toxic algal blooms is the identification of the maximum nutrient load ecosystems can absorb, while remaining in a good ecological state. In this thesis, I aim to determine the critical nutrient load for Lake Taihu. I approach the search for critical nutrient loads of Lake Taihu in five steps with diversity as an overarching topic throughout this thesis: diversity in lakes, diversity in models, diversity in spatial distribution of nutrient and water sources, diversity in the development of lakes around the earth and finally diversity within specific lakes. From the long list of available models I chose the model PCLake to use in my analysis because it is the most extensively used food web model applied for bifurcation analysis of shallow aquatic ecosystems. The approach has resulted in a range of critical nutrient loads for different parts of Lake Taihu. Furthermore, critical nutrient loads depend on management goals, i.e. the maximum allowable chlorophyll-a concentration. According to the model results, total nutrient loads need to be more than halved to reach chlorophyll-a concentrations of 30-40 μg.L-1 in most sections of the lake. To prevent phytoplankton blooms with 20 μg.L-1 chlorophyll-a throughout Lake Taihu, both phosphorus and nitrogen loads need a nearly 90% reduction. This range contrasts to the single point of recovery that is often found for small shallow lakes. The range in critical nutrient loads found for Lake Taihu can be interpreted as providing a path of recovery for which each step leads to water quality improvement in certain parts of the lake. To reach total recovery, nutrient reduction seems to be the most promising management option.
Using probabilistic graphical models to reconstruct biological networks and linkage maps
Wang, Huange - \ 2017
Wageningen University. Promotor(en): F.A. Eeuwijk, co-promotor(en): J. Jansen. - Wageningen : Wageningen University - ISBN 9789463431538 - 150
probabilistic models - models - networks - linkage - mathematics - statistics - quantitative trait loci - phenotypes - simulation - waarschijnlijkheidsmodellen - modellen - netwerken - koppeling - wiskunde - statistiek - loci voor kwantitatief kenmerk - fenotypen - simulatie
Probabilistic graphical models (PGMs) offer a conceptual architecture where biological and mathematical objects can be expressed with a common, intuitive formalism. This facilitates the joint development of statistical and computational tools for quantitative analysis of biological data. Over the last few decades, procedures based on well-understood principles for constructing PGMs from observational and experimental data have been studied extensively, and they thus form a model-based methodology for analysis and discovery. In this thesis, we further explore the potential of this methodology in systems biology and quantitative genetics, and illustrate the capabilities of our proposed approaches by several applications to both real and simulated omics data.
In quantitative genetics, we partition phenotypic variation into heritable, genetic, and non-heritable, environmental, parts. In molecular genetics, we identify chromosomal regions that drive genetic variation: quantitative trait loci (QTLs). In systems genetics, we would like to answer the question of whether relations between multiple phenotypic traits can be organized within wholly or partially directed network structures. Directed edges in those networks can be interpreted as causal relationships, causality meaning that the consequences of interventions are predictable: phenotypic interventions in upstream traits, i.e. traits occurring early in causal chains, will produce changes in downstream traits. The effect of a QTL allele can be considered to represent a genetic intervention on the phenotypic network. Various methods have been proposed for statistical reconstruction of causal phenotypic networks exploiting previously identified QTLs. In chapter 2, we present a novel heuristic search algorithm, namely the QTL+phenotype supervised orientation (QPSO) algorithm, to infer causal relationships between phenotypic traits. Our algorithm shows good performance in the common, but so far uncovered case, where some traits come without QTLs. Therefore, our algorithm is especially attractive for applications involving expensive phenotypes, like metabolites, where relatively few genotypes can be measured and population size is limited.
Standard QTL mapping typically models phenotypic variations observable in nature in relation to genetic variation in gene expression, regardless of multiple intermediate-level biological variations. In chapter 3, we present an approach integrating Gaussian graphical modeling (GGM) and causal inference for simultaneous modeling of multilevel biological responses to DNA variations. More specifically, for ripe tomato fruits, the dependencies of 24 sensory traits on 29 metabolites and the dependencies of all the sensory and metabolic traits further on 21 QTLs were investigated by three GGM approaches including: (i) lasso-based neighborhood selection in combination with a stability approach to regularization selection, (ii) the PC-skeleton algorithm and (iii) the Lasso in combination with stability selection, and then followed by the QPSO algorithm. The inferred dependency network which, though not essentially representing biological pathways, suggests how the effects of allele substitutions propagate through multilevel phenotypes. Such simultaneous study of the underlying genetic architecture and multifactorial interactions is expected to enhance the prediction and manipulation of complex traits. And it is applicable to a range of population structures, including offspring populations from crosses between inbred parents and outbred parents, association panels and natural populations.
In chapter 4, we report a novel method for linkage map construction using probabilistic graphical models. It has been shown that linkage map construction can be hampered by the presence of genotyping errors and chromosomal rearrangements such as inversions and translocations. Our proposed method is proven, both theoretically and practically, to be effective in filtering out markers that contain genotyping errors. In particular, it carries out marker filtering and ordering simultaneously, and is therefore superior to the standard post-hoc filtering using nearest-neighbour stress. Furthermore, we demonstrate empirically that the proposed method offers a promising solution to genetic map construction in the case of a reciprocal translocation.
In the domain of PGMs, Bayesian networks (BNs) have proven, both theoretically and practically, to be a promising tool for the reconstruction of causal networks. In particular, the PC algorithm and the Metropolis-Hastings algorithm, which are representatives of mainstream methods to BN structure learning, are reported to have been successfully applied to the field of biology. In view of the fact that most biological systems exist in the form of random network or scale-free network, in chapter 5 we compare the performance of the two algorithms in constructing both random and scale-free BNs. Our simulation study shows that for either type of BN, the PC algorithm is superior to the M-H algorithm in terms of timeliness; the M-H algorithm is preferable to the PC algorithm when the completeness of reconstruction is emphasized; but when the fidelity of reconstruction is taken into account, the better one of the two algorithms varies from case to case. Moreover, whichever algorithm is adopted, larger sample sizes generally permit more accurate reconstructions, especially in regard to the completeness of the resulting networks.
Finally, chapter 6 presents a further elaboration and discussion of the key concepts and results involved in this thesis.
Exploring opportunities for rural livelihoods and food security in Central Mozambique
Leonardo, Wilson José - \ 2017
Wageningen University. Promotor(en): K.E. Giller, co-promotor(en): G.W.J. van de Ven; H.M.J. Udo. - Wageningen : Wageningen University - ISBN 9789463431651 - 183
agricultural production systems - food security - crop production - livelihoods - small farms - biofuels - farming systems - models - intensification - mozambique - agrarische productiesystemen - voedselzekerheid - gewasproductie - middelen van bestaan - kleine landbouwbedrijven - biobrandstoffen - bedrijfssystemen - modellen - intensivering - mozambique
Growing awareness of widespread hunger and poverty in many countries in the SSA is spurring a focus on productivity increase in smallholder farming systems. The rationale is that with current production systems many SSA countries are not keeping pace with population growth and changing of peoples’ lifestyles. To respond to this challenge the Government of Mozambique developed its Strategic Plan for Agricultural Development (PEDSA) aiming to improve agricultural productivity of the majority of smallholder farmers who depend on agriculture for their livelihoods. Smallholder farmers are diverse in terms of resources and aspirations. The main objectives of this study are first to understand the diversity among maize-based smallholder farms and their current constraints in improving agricultural productivity in the Manica Plateau, Central Mozambique, and second, building on that understanding to explore options for biomass production either for food, cash or biofuel at farm level and contributions to maize availability in the region. The study was conducted in the Dombe and Zembe Administrative Posts. Farmers in the two posts cultivate both food and cash crops using the same resources, however, distances to the urban market differ, with Zembe close and Dombe far away from the markets. In addition, the agroecological conditions for crop production are more favourable in Dombe compared with Zembe. Using farm surveys, direct observations and on-farm measurements, followed by Principal Components Analysis (PCA) I identified land and labour as the variables that can best explain the variability found among smallholder farms (Chapter 2). Based on these variables I categorised farms into four Farm Types (FT): FT1. Large farms (4.4 ha in Dombe and 2.2 ha in Zembe), hiring in labour; FT2. Intermediate sized farms (1.9-1.2 ha), hiring in and out labour; FT3a. Small farms (1.1-0.9 ha), sharing labour; and FT3b. Small farms (1.0-0.7 ha), hiring out labour. The maize yield and maize labour productivities were higher on large farms (2.3 t ha-1 in Dombe and 2.0 t ha-1 in Zembe; 2.5×10-3 t h-1 in Dombe and 2.6 ×10-3 t h-1 in Zembe) compared with small farms (1.5 t ha-1 in Dombe and 1.1 t ha-1 in Zembe; 1.4×10-3 t h-1 in Dombe and 0.9×10-3 t h-1 in Zembe). The hiring in labour from small farms allowed large farms to timely weed their fields. Small farms were resource constrained and hired out labour (mutrakita) for cash or food to the detriment of weeding their own fields, resulting in poor crop yields. Excessive alcohol consumption by small farms also raised concerns on labour quality. Chapter 3 explored options aiming at addressing farmers’ objectives of being maize self-sufficient and increased gross margin and the contribution to national objective of producing food. A bio-economic farm model was used to investigate two pathways to increase agricultural production: (i) extensification, expanding the current cultivated area; and (ii) intensification, increasing input use and output per unit of land.
In the extensification pathway I considered the use of animal traction, herbicides and cultivators to save labour, whereas in the intensification pathway I explored the use improved varieties of maize, sesame, sunflower, pigeonpea and fertilizers. I focused on the large farms and the small farms hiring out labour as they represent both sides of the spectrum. The simulated results showed that combining labour and labour saving technologies substantially increased both gross margin and maize yields of large and small farms in both posts. Minor trade-offs is observed on large farms between the two goals whereas for small farms we see synergies between the goals. We concluded that prospects for increasing gross margin and food production are much better for large farms in Dombe compared with other farms. In Dombe, the maximum gross margin of large farms was 7530 $ y-1 per farm and maximum maize sales of 30.4 t y-1 per farm. In Zembe, the maximum gross margin of large farms (2410 $ y-1 per farm) and maximum maize sales (9.5 t y-1 per farm) were comparable to small farms in Dombe. I further assessed the impact of two biofuel investments (jatropha plantation and sunflower outgrower schemes) on farm level food security (food availability, access to food, stability of food, utilization of food). The results showed positive impact on small farms from employment on a jatropha plantation by increasing access to food and no impacts on intermediate and large farms. Impacts on food security from the sunﬂower outgrower scheme were minor which may be explained by the poor yields.
The need to link smallholder farmers to markets has been increasingly recognized as important strategy to promote rural development and poverty reduction. I developed an analytical framework, the Windmill Approach that looked at decision making at farm level to grow certain crops and at transaction strategies (Chapter 5). Through this framework I showed that a farmer decision to participate in a particular (new) value chain is determined by (a) the suitability of the new crop in the farm system (including the adaptability of the current farm system), and (b) the farmer’s experience with selling in various value chains. This has major policy implications as it highlights that to support smallholder farmers access to markets a holistic approach is needed that combines farming systems analysis and transaction cost theory.
In order to explore the opportunities for smallholder development there is need to understand the diversity of farms and farmers’ social and economic context. For large farms, in Central Mozambique farms with on average 2-4 ha of land, opportunities to improve their livelihoods through crop production can follow two pathways: intensification and extensification. Smallholders continue to produce staple food crops even when working on a plantation or participating in outgrower schemes. For small farms, off-farm opportunities such as those in a biofuel plantation are the best options to improve their livelihoods.
Assessing methane emission from dairy cows : modeling and experimental approaches on rumen microbial metabolism
Lingen, Henk J. - \ 2017
Wageningen University. Promotor(en): W.H. Hendriks, co-promotor(en): J. Dijkstra; A. Bannink; C.M. Plugge. - Wageningen : Wageningen University - ISBN 9789463431590 - 207
dairy cows - methane - emission - microbial degradation - rumen metabolism - rumen fermentation - models - fatty acids - biochemical pathways - animal nutrition - nutrition physiology - melkkoeien - methaan - emissie - microbiële afbraak - pensmetabolisme - pensfermentatie - modellen - vetzuren - biochemische omzettingen - diervoeding - voedingsfysiologie
Methane (CH4) is a greenhouse gas (GHG) with a global warming potential of 28 CO2 equivalents. The livestock sector was estimated to emit 7.1 gigatonnes of CO2 equivalents, which is approximately 14.5% of total global anthropogenic GHG emissions. Enteric CH4 production is the main source of GHG emissions from dairy cattle, representing 46% of the global GHG emissions in dairy supply chains. Dairy production has great value in view of the ability of ruminants to effectively turn human inedible biomass into human edible food and to produce food from non-arable land. Consequently, there is an urgent need to develop strategies to decrease dairy cattle enteric CH4 emission. Evaluation of these strategies requires meticulous quantification and increased understanding of anaerobic fermentation and methanogenesis in the rumen ecosystem. The overall aim of this PhD research was, therefore, to quantitatively evaluate enteric CH4 emission from dairy cows as affected by feeding and rumen microbial metabolism.
A meta-analysis was performed to quantify relationships between enteric CH4 yield (per unit of feed and unit of milk) and milk FA profile in dairy cattle and to develop equations to predict CH4 yield based on milk FA profile of cows fed a wide variety of diets. Various milk FA concentrations were significantly or tended to be positively or negatively related to CH4 yield per unit of feed or milk. Mixed model multiple regression resulted in various milk FA included in optimal equations to predict CH4 yield per unit of feed and per unit of milk. These regression equations indicated a moderate potential for using milk FA profile to predict CH4 yield.
For the development of a mechanistic model of CH4 production in the rumen, the thermodynamic control of pH2 on reaction rates of specific fermentation pathways, NADH oxidation and methanogenesis was theoretically explored. This control was determined using the thermodynamic potential factor (FT), which is a dimensionless factor that corrects a predicted kinetic reaction rate for the thermodynamic control exerted. The thermodynamic feasibility of these microbial conversions showed that the control of pH2 on individual VFA produced and associated yield of H2 and CH4 cannot be explained without considering NADH oxidation, with a considerable effect of pH.
For obtaining experimental support of the conclusions drawn from the theoretical exploration, diurnal patterns of gaseous and dissolved metabolite concentrations in the bovine rumen, H2 and CH4 emitted, and the rumen microbiota were monitored. In addition, the effect of dietary inclusion of linseed oil on these patterns was assessed. An in vivo experiment with rumen cannulated dairy cows was performed to study the anaerobic metabolism and the microbiota composition in the rumen. A 100-fold increase in pH2 in the rumen headspace was observed at 0.5 h after feeding, followed by a decline. Qualitatively similar patterns after feeding were observed for H2 and CH4 emission, ethanol and lactate concentrations, and propionate molar proportion, whereas an opposite pattern was seen for acetate molar proportion. Associated with these patterns, a temporal biphasic change in the microbial composition was observed as based on 16S ribosomal RNA with certain taxa specifically associated with each phase. Bacterial concentrations were affected by time and increased by linseed oil supplementation. Archaeal concentrations tended to be affected by time and were not affected by diet, despite linseed oil supplementation tending to decrease the partial pressure and emission of CH4 and tending to increase propionate molar proportion. The various diurnal profiles that were monitored support the key role of the NAD+ to NADH ratio in rumen fermentation and the importance of diurnal dynamics when understanding VFA, H2 and CH4 production.
A dynamic mechanistic model was developed, in which the thermodynamic control of pH2 on VFA fermentation pathways, and methanogenesis in the bovine rumen are incorporated. The model represents substrate degradation, microbial fermentation and methanogenesis in the rumen, with the type of VFA formed to be controlled by the NAD+ to NADH ratio, which in turn is controlled by pH2. Feed composition and feed intake rate representing a twice daily feeding regime were used as model input. The model predicted a marked peak in pH2 after feeding that rapidly declined in time. This peak in pH2 caused a decrease in NAD+ to NADH ratio followed by an increased propionate molar proportion at the expense of acetate molar proportion. In response to feeding, the model predicted a sudden increase and a steady decrease in CH4 production in time. The pattern of CH4 emission rate followed the patterns of pH2 and H2 emission rate, but its magnitude of increase in response to feeding was less pronounced. A global sensitivity analysis indicated the parameter that determines the NADH oxidation rate to explain the most substantial part of the variation of predicted daily CH4 emission. The modeling effort provides the integration of more detailed knowledge than accomplished in previous rumen fermentation models and enables assessment of diurnal dynamics of rumen metabolic pathways yielding VFA, H2 and CH4.
For assessing the general value of the research reported in this thesis, the potential for predicting enteric CH4 emission from dairy cattle based on milk FA profile was discussed in the light of published studies and compared with empirical modeling of enteric CH4 based on feed input. Moreover, the concept of NAD-controlled fermentation was considered in a more general perspective by comparing the rumen ecosystem with bioreactor systems. Furthermore, the feasibility of the developed models as an alternative for IPCC tiered approaches was explored. In conclusion, the research reported in this thesis contributes to an increased understanding of rumen fermentation and microbial metabolism, and has provides a basis to further improve prediction models of enteric CH4 emissions from dairy cattle.
Technical documentation of the soil model VSD+ : Status A
Mol-Dijkstra, J.P. ; Reinds, G.J. - \ 2017
Wageningen : Statutory Research Tasks Unit for Nature & the Environment (WOt-technical report 88) - 88
soil - soil acidity - models - nutrient availability - soil carbon sequestration - climatic change - precipitation - bodem - bodemaciditeit - modellen - voedingsstoffenbeschikbaarheid - koolstofvastlegging in de bodem - klimaatverandering - neerslag
VSD+ is een model om de gevolgen te berekenen van atmosferische depositie en klimaatverandering voorbodemverzuring, de beschikbaarheid van voedingsstoffen en het vastleggen van koolstof. Het model isontwikkeld ter onderbouwing van strategieën om de uitstoot van zwavel (S) en stikstof (N) in Europa teverminderen. Dit document biedt een samenvatting van de theorie waar het model op gestoeld is, detechnische documentatie hiervan alsmede een beschrijving van het testen, het valideren en de sensitiviteitsanalysevan het model. De processen zoals beschreven in het artikel over VSD+ zijn met goed gevolg getest.De gevoeligheidsanalyse gaf aan dat de constante voor het evenwicht tussen H+ en Al3+ in de bodemoplossingen de Ca-verweringssnelheid de parameters zijn, die voor een groot gedeelte de waarde van degesimuleerde pH bepalen. Voor basenverzadiging zijn de belangrijkste parameters de uitwisselingsconstantetussen H+ en basische kationen en de verwering van Ca. Voor de C/N ratio van bodemorganische stof zijn Cen N in het strooisel en de opname van N zeer bepalende factoren. De nitraatconcentratie hangt sterk samenmet het nerslagoverschot en de netto input van N---VSD+ is a model to calculate effects of atmospheric deposition and climate change on soil acidification,nutrient availability and carbon sequestration. The model has been developed to support emission abatementstrategies of sulphur (S) and nitrogen (N) in Europe. This document contains a summary of the modeltheory, technical documentation and descriptions of testing, validations and the sensitivity analysis of themodel. The processes described in the paper about VSD+ have been tested successfully. The sensitivityanalysis showed that the constant for the equilibrium between H+ and Al3+ in the soil solution and theweathering rate of Ca are the parameters that to a large extent determine the value of the simulated pH. Forbase saturation, most important parameters are the exchange constant between H+ and base cations andthe weathering of Ca. For the C/N ratio of soil organic matter, litterfall of C and N and the uptake of N areimportant influencing factors. The nitrate concentration strongly depends on the leaching flux and the net N input
Integrated strategy for the assessment of kidney toxicity : the case of aristolochic acids
Abdullah, Rozaini - \ 2017
Wageningen University. Promotor(en): Ivonne Rietjens, co-promotor(en): Ans Punt; Sebas Wesseling; Jochem Louisse. - Wageningen : Wageningen University - ISBN 9789463430807 - 207
animal testing alternatives - in vitro - toxicity - models - risk assessment - toxins - carboxylic acids - alternatieven voor dierproeven - in vitro - toxiciteit - modellen - risicoschatting - toxinen - carbonzuren
This PhD thesis aimed to provide additional evidence to demonstrate the potential of an integrated testing strategy using in vitro assays with physiologically based kinetic (PBK) modeling based-reverse dosimetry to predict in vivo toxicity without animal testing. Kidney toxicity was chosen as the toxicity endpoint and aristolochic acids (AAs) were selected as model chemicals. AAs are natural nephrotoxic, genotoxic and carcinogenic chemicals present in Aristolochia species. PBK models for rat, mouse and human were developed for aristolochic acid I (AAI) based on kinetic parameter values derived from in vitro incubations using relevant tissue fractions. Then, in vitro concentration-response curves for cytotoxicity of AAI were obtained in kidney cell lines and translated to in vivo dose-response curves for kidney toxicity using PBK modeling-based reverse dosimetry. The points of departure (PODs) obtained from these predicted in vivo dose-response curves generally fell within the range of PODs derived from in vivo literature data on kidney toxicity of AAI. The same PBK models were subsequently used to translate the in vitro concentration-response curves for AAI-DNA adduct formation to in vivo dose-response curves for kidney AAI-DNA adduct formation. The predicted in vivo AAI-DNA adduct formation in the rat, mouse and human kidney varied within an order of magnitude compared to the in vivo values reported in the literature. The PBK models were also used to predict the dose level that would be required in humans to obtain the level of DNA adducts in rats at the BMD10 (the benchmark dose causing a 10% extra risk above background level) value for AAI-induced tumor formation in the rat kidney. This analysis revealed that the dose level required to induce the level of DNA adduct formation that equals the DNA adduct level at the BMD10 were similar to AA doses estimated to be taken in Belgian patients that developed urinary tract cancer. Given that the exposure to AAI is often accompanied by the presence of AAII, in a next study the relative formation of DNA adducts by these two major AA congeners was investigated. The results revealed that the relative higher formation of AAI-DNA adducts as compared to AAII-DNA adducts observed in vitro was not reflected in vivo where the levels formed upon exposure to equal dose levels were relatively similar. PBK model based translation of the in vitro data to the in vivo situation revealed that PBK model based prediction of in vivo DNA adduct formation is feasible. However, predicted AAI-DNA adduct levels were higher than predicted AAII-DNA adduct levels, indicating that the difference between the in vitro and in vivo AAI-/AAII-DNA adduct ratios could only in part be explained by differences in in vivo kinetics of AAI compared to AAII. The discrepancy between the difference in DNA adduct formation of AAI and AAII in the in vitro and the in vivo situation is an issue that needs further investigation to also adequately predict the relative differences between the two AAs. In a final chapter this thesis aimed to investigate the possible risks associated with exposure to AAs based on AA levels measured in plant food supplements (PFS) and herbal products. This is of interest given the restrictions on the presence of AAs in food, installed in various countries including The Netherlands, after the incidences with induction of Aristolochic Acid Nephropathy upon use of herbal weight loss preparations that accidentally contained AAs. The risk assessment of PFS and herbal products containing AAs purchased via online markets revealed that consumers can still be exposed to AA-containing PFS and herbal products and that the corresponding levels of exposure raise concern especially for people who frequently use the products. Altogether, this thesis presented further support for the use of combined in vitro-PBK modeling based alternative tools for risk assessment and revealed the continued risks posed by AAs present in PFS and herbal products.
Multi-objective optimization for eco-efficient food supply chains
Banasik, Aleksander - \ 2017
Wageningen University. Promotor(en): Jacqueline Bloemhof-Ruwaard; Jack van der Vorst, co-promotor(en): Frits Claassen. - Wageningen : Wageningen University - ISBN 9789463430944 - 147
food chains - supply chain management - food production - mushrooms - decision support systems - production planning - models - voedselketens - ketenmanagement - voedselproductie - paddestoelen - beslissingsondersteunende systemen - productieplanning - modellen
Until recently, food production focused mainly on delivering high-quality products at low cost and little attention was paid to environmental impact and depletion of natural resources. As a result of the growing awareness of climate change, shrinking resources, and increasing world population, this trend is changing. A major concern in Food Supply Chains (FSCs) is food waste. To remain competitive, FSCs are challenged to adopt new technologies that reduce or valorize food waste. These technologies can contribute to maintaining or increasing economic output and concurrently reduce the environmental impact of current operations, i.e. achieving what has been defined as eco-efficiency. Designing eco-efficient supply chains requires complex decision support models that can deal with multiple dimensions of sustainability while taking into account the specific characteristics of products and their supply chain. Multi-Criteria Decision Making (MCDM), a research field within Operations Research, is particularly suitable to support decision making when multiple and (mostly) conflicting criteria are involved. In this research, multi-objective optimization was used to quantify trade-offs between conflicting objectives and derive eco-efficient solutions, i.e. solutions in which environmental performance can only be improved at higher cost. The overall objective of this thesis was to support decision making in FSCs by developing dedicated decision support models to optimize and re-design FSCs by balancing the economic and environmental criteria. The emphasis is directed towards valorization of product flows by means of closing loops and waste management at a chain level. In line with this overall objective, four research questions were defined, which are addressed in Chapters 2 to 5.
In Chapter 2, the use of MCDM approaches for designing Green Supply Chains (GSCs) is reviewed; GSCs extend traditional supply chains to include activities that minimize the environmental impact of a product throughout its life cycle. A conceptual framework was developed to find relevant publications and categorize papers with respect to decision problems, indicators, and MCDM approaches. The analysis shows that the use of MCDM approaches for designing GSCs is a new but emerging research field. Most publications focus on production and distribution problems, and there are only a few inventory models with environmental considerations. Most papers assume all data to be deterministic. Moreover, little attention has been given to minimization of waste in studies on FSCs, and numerous indicators are used to account for eco-efficiency, indicating the lack of standards. Chapter 2, therefore, identifies the need for more multi-criteria models for real-life GSCs, especially with respect to supply chains dealing with food production, and with inclusion of uncertainty in parameters.
Environmental concerns and scarcity of resources encourage decision makers in supply chains to consider alternative production options that include preventing the production of waste streams and simultaneously reusing and recycling waste materials. Until now, quantitative modelling approaches on closing loops in FSCs have been rare in the literature. The aim of Chapter 3 was to develop a mathematical model that can be used for quantitative assessment of alternative production options associated with different ways of dealing with waste in FSCs, i.e. prevention, recycling, and disposal of food waste. A multi-objective mixed integer linear programming model was developed to derive a set of eco-efficient solutions corresponding to production planning decisions. The environmental performance of the chain is expressed by an indicator based on exergy analysis, which has the potential to capture other commonly used indicators, such as energy consumption, fuel consumption, and waste generation, in a single value. This simplifies the calculation of the eco-efficient frontier and enables its intuitive graphical representation, which is much easier to communicate to the decision makers. The applicability of the model is demonstrated on a real-life industrial bread supply chain in the Netherlands. The results confirm the findings from the literature that prevention is the best waste management strategy from an environmental perspective. The advantages of using exergy as an indicator to capture the environmental performance is demonstrated by comparing the outcomes with other commonly used indicators of environmental performance. The potential of studying food production planning decision problems in a multi-objective context is illustrated and the applicability of the model in the assessment of alternative production options is demonstrated.
In contrast to closed-loop studies in industry involving discrete parts, in FSCs the value of the final product usually cannot be regained. However, the components used for production, such as organic matter or a growing medium, can be recycled. The aim of Chapter 4 was to reveal the consequences of closing loops in a mushroom supply chain. A multi-objective mixed integer linear programming model was proposed to quantify trade-offs between economic and environmental indicators and to explore alternative recycling technologies quantitatively. The model was developed to re-design the logistical structure and close loops in the mushroom supply chain. It was found that adopting closing loop technologies in industrial mushroom production has the potential to increase the total profitability of the chain by almost 11% and improve the environmental performance by almost 28%. It is concluded that a comprehensive evaluation of recycling technologies and re-designing logistical structures requires quantitative tools that simultaneously optimize managerial decisions at strategic and tactical levels.
Multi-objective optimization models are often developed under the assumption that all information required for model parameterization is known in advance. In practice, however, not all the required information is available in advance because of various sources of uncertainty in FSCs. In Chapter 5, a multi-objective two-stage stochastic programming model was proposed to analyse and evaluate the economic and environmental impacts to account for uncertainty in FSCs. A mushroom supply chain in the Netherlands is presented as an illustrative case study. Optimal production planning decisions calculated with a two-stage stochastic programming model are compared with the results of an equivalent deterministic model. It is demonstrated that taking uncertainty into account at the production planning phase in an FSC can bring substantial economic and environmental benefits.
The research presented in this thesis contributes to the scientific literature on eco-efficient FSCs by providing decision support models for use by decision makers to assess alternative logistical structures and quantify the economic and environmental implications of closing loop technologies. This thesis shows that technological innovations, which allow for reuse and recycling of waste streams, have the potential to improve the economic and environmental performance of an FSC substantially. The case studies illustrate that it is worthwhile investing in research on technological innovations (and their development) for closing loops in FSCs. The greatest benefits are brought about by using materials to their full potential by valorizing waste streams as much as possible.
Temperature in water and sediment in the pesticide model TOXSWA : implementation report
Beltman, W.H.J. ; Adriaanse, P.I. ; Jacobs, C.M.J. ; Mulder, H.M. - \ 2017
Wageningen : Wageningen Environmental Research (Wageningen Environmental Research report 2794) - 67
pesticides - water - temperature - models - sediment - surface water - pesticiden - water - temperatuur - modellen - sediment - oppervlaktewater
TOXSWA simuleert het gedrag van stoffen in oppervlaktewater om blootstellingsconcentratie te berekenen voor organismen die in water of sediment leven, als onderdeel van de aquatische risicobeoordeling van gewasbeschermingsmiddelen (GBM). Het vernieuwde concept voor de beschrijving van de temperatuur in het TOXSWA model werd getest aan de hand van een bestaande implementatie van het 1D bulk model.
Matching breeding goals with farming systems to enhance the sustainability of fish farming
Besson, Mathieu - \ 2017
Wageningen University. Promotor(en): Imke de Boer; Hans Komen, co-promotor(en): M. Vandeputte. - Wageningen : Wageningen University - ISBN 9789463430067 - 201
fish culture - sustainability - animal production - farming systems - models - feed conversion - breeding - growth rate - feed conversion efficiency - animal welfare - visteelt - duurzaamheid (sustainability) - dierlijke productie - bedrijfssystemen - modellen - voederconversie - veredelen - groeitempo - voederconversievermogen - dierenwelzijn
Fish farming is growing but is also facing challenges regarding economic viability and environmental sustainability. Selective breeding could enhance the sustainability of fish farming by changing animal performances. Thus, our aim was to develop sustainable breeding goals by using economic (EV) or environmental values (ENV) to weigh the traits to improve. EV and ENV represent the economic and environmental impacts of improving a trait. They were calculated using a bioeconomic model combined with a life cycle assessment. We showed that the EV and ENV of traits change with the factor constraining the production of the farm. It suggests that breeding goals should be finely tuned according to the limiting factor to maximize economic or environmental responses. In addition, we showed that improving feed conversion ratio is a major trait to improve because it always increases profit and decreases environmental impacts. We conclude that it is possible to develop breeding programs enhancing the sustainability of fish farming by improving the right trait in the right production system.
Advancement of farming by facilitating collaboration : reference architectures and models for farm software ecosystems
Kruize, Jan Willem - \ 2017
Wageningen University. Promotor(en): Adrie Beulens, co-promotor(en): Huub Scholten; Jacques Wolfert. - Wageningen : Wageningen University - ISBN 9789462579668 - 242
farming - information technology - computer software - farms - models - farm management - information systems - landbouw bedrijven - informatietechnologie - computer software - landbouwbedrijven - modellen - agrarische bedrijfsvoering - informatiesystemen
Since time began, mankind has been threatened by the combination of growing populations and diminishing resources. Present-day, this threat is very pertinent as mankind is challenged by a growing world population that is expected to exceed 10 billion in 2050, while resources diminish. Simultaneously, increase of food production should be accomplished in a sustainable manner as consumers require food to be produced environmentally-friendly. Moreover, consumers require safe food produced in transparent agri-food supply chain networks. Farm enterprises can contribute by advancing their management to increase food production in a sustainable, safe and transparent manner. A well-known advanced farm management style, which is knowledge and information intensive, is precision agriculture. Precision agriculture increases the profitability of crop production, while simultaneously reducing the negative environmental impact by tight monitoring and control, in which applications rates of agricultural inputs are adjusted to local needs. Such advanced farm management requires integrated farm information systems as it is knowledge and information intensive. However, advancement is hindered because of interoperability issues between software systems of multiple vendors. An integrated farm information system, containing components of multiple vendors, is required as single organisations cannot develop all technical solutions and ICT Components (e.g. tractors, implements, FMIS, decision support tools) that farmers require. A global overarching system, developed by a single vendor, that can support all business functions of farmers is therefore neither a feasible nor, from a competitive point of view, a desirable solution in agriculture. To realize farm enterprise integration we combine the approaches ICT Mass Customisation with Best-of-Breed. ICT mass customisation combines advantages of standard and customised software by enabling on-demand configuration of information systems from standard components with standardised interfaces. These ICT components can be supplied by different software vendors, which allow Best-of-Breed solutions. By realization of these approaches farm enterprise integration can improve. A farm enterprise can be an arable farm, livestock farm or horticultural farm. In this thesis we focus on arable farm enterprises.
To enable farm enterprise integration we have developed six artefacts that are presented in this thesis which are:
The Reference Architecture of Agricultural Enterprises (RAAgE) 1.0 that can describe farm enterprise architectures in a uniform and efficient manner;
A problem description, which is a case specific instantiation of RAAgE 1.0 generalized to a generic problem description;
An ontology that supports communication between collaborating actors and components;
Reference Architecture for Farm Software Ecosystems that defines generic relationships between actors and components;
RAAgE 2.0 that is a technical reference model to support configuration of business processes and ICT components, which is based on RAAgE 1.0;
Prototype software that serves as a proof of concept substantiating that all previous components will provide a solution for integration problems at farm enterprises.
RAAgE 1.0 supports designing enterprise architectures in a uniform and efficient manner. The reference model is described in a standard modelling language, named ArchiMate, and shows the interrelations between the business, application and technology layers of farm enterprises. The reference model includes an ontology to provide a concise and precise, formal specification of the object system. This is required to have a shared understanding and effective communication between researchers, farmers, software developers and other stakeholders involved. This ontology is used and extended in other parts of our research. The architectural descriptions can depict the relations between farm business processes and the ICT Components used. The model is validated by two experts that have experience in developing reference architectures and models.
A detailed problem description is created using RAAgE 1.0 to gain insight in the cause and nature of integration problems at farm enterprises. To find these problems a method was developed and applied in a case study research including three arable farm enterprises producing potatoes. These farm enterprises focused on improving their management and invested in new technologies for innovation. Within multiple steps of the method the architectural descriptions developed with RAAgE 1.0 facilitated communication and provided insight into problems of farm enterprises to achieve more advanced farm management. The case specific problems, described by instantiating RAAgE 1.0, have been analysed and formulated as more generic problems for farm enterprise integration. These generic problem descriptions have been validated with national and international experts. Based on this research we found that the cause and nature of current integration problems in farming are that ICT components used within the same farm enterprise:
have partly overlapping and partly unique application services, functions and interfaces (that are non-standard);
are missing required application services, functions and interfaces,
have disjoint data repositories;
have inadequate and incomplete data exchange as semantics are not unambiguously defined;
are hard to configure while this configuration is not supported by an actors and tools.
A design, addressing these problems is expected to solve current integration bottlenecks. First, this design must enable smooth data handling and seamless data exchange between ICT Components to solve inadequate and incomplete data exchange and enable integration of data repositories of multiple vendors. Second, it must include a configuration approach to link ICT Components to each other in a meaningful and coherent way. This should be supported by actors that are willing to configure ICT Component of multiple vendors into an integrated solution. Third, the design must enable the formation of a software enterprise to address the previous points and to organize collaboration between actors involved. This software enterprise should focus both on improving interoperability to contribute in solving problems with partly overlapping and partly unique application services, functions and interfaces as well as on organizing the development of missing application services, functions and interfaces.
To address these integration challenges a Reference Architecture for Farm Software Ecosystems and RAAgE 2.0 were developed, focusing on both technical and organizational aspects.
From literature we found that collaboration can take place within Software Ecosystems. Software Ecosystems are defined as the interaction of a set of actors on top of a common technological platform that results in a coherent set of ICT components or Services. They can provide an effective way to construct large software systems on top of a software platform by combining components, developed by actors that are part of different organisations. To support instantiation of Software Ecosystems for farming, a Reference Architecture was developed. This Reference Architecture describes how software developers, farmers and other stakeholders can collaborate to enable development, configuration and instantiation of integrated software solutions. More specifically, it can be used to map, assess, design and implement Farm Software Ecosystems to help to decrease current integration problems. The reference architecture comprises five main components:
Actors, which are basically app developers, business architects/software developers and end-users, i.e. farmers that finally use the configured ICT components and services;
Platform that enables configuration of Atomic Application Components into integrated information systems for farmers;
Open software enterprise that manages the relation between the actors and the platform;
Business services that support software configuration, development and hosting;
ICT Components that are configured application components from multiple vendors allowing seamless data exchange based on standards
After the design the reference architecture was first verified based on the requirements. Second, semi-structured interviews were held with experts to validate the model. Moreover, the assessment and mapping functionally was validated by using the reference architecture in a case study, in which two existing farm software ecosystems were assessed and mapped.
The Reference Architecture for Farm Software Ecosystems mainly addresses the organizational part of this research question. The technical part on the configuration of different ICT components into integrated solutions was not yet sufficiently covered in the Reference Architecture for Farm Software Ecosystems. Therefore we designed RAAgE 2.0 to improve the integrating capabilities of ICT Components, focussing on configuration and ICT Mass Customisation. In this research RAAgE 1.0 was extended into RAAgE 2.0 supporting technical aspects related to configuration of ICT Components by providing a hierarchical configuration methodology. This methodology divides configuration in two steps (i) business process configuration and (ii) software configuration. To enable business process configuration the model comprises three reference models, i.e. on products, processes and resources. The dependencies between these models are defined in rules that define possible combinations of products, processes and resources and that constrain the configuration of farm-specific models i.e. instances. The reference model also includes a configuration tree and templates. Templates describe a set of pre-configured product, process and resource models for typical cases. Variety in farm business processes can be modelled with business process variants. Such a variant realizes a similar kind of business services (e.g. basic fertilization, precision fertilization). Each variant has partly overlapping business processes and resources and unique ones. RAAgE 2.0 provides insight into these specific and generic parts. The other part of the methodology, software configuration, is divided in two additional sub-steps. The first sub-step is to create configuration templates that describe the required (generic) application services (capability types) to support specific business process variants. These configuration templates describe the interactions between the capability types. This sub step is typically performed by a business architect in close collaboration with software developers. The second sub-step is the selection and configuration of the specific capability of a capability type. Capabilities can be offered by atomic application components of multiple vendors that need to be selected. This second sub-step is performed by a business architect, in close collaboration with a farmer. With this extension RAAgE 2.0 supports (i) development of ICT components that fit within an ICT Mass Customisation and Best-of-Breed approach, (ii) selection of ICT components based on business processes that they should support and (iii) getting insight into configuration of different ICT components into an integrated farm information system.
To substantiate that our artefacts contribute to realizing ICT Mass Customisation in combination with Best-of-Breed in arable agriculture a proof of concept was developed. A proof of concept is defined as a phase in development, in which experimental hardware or software is constructed and tested to explore and demonstrate the feasibility of a new concept. Realizing ICT Mass Customisation requires: (i) software modularity, (ii) an information integration platform, (iii) component availability, (iv) configuration support and (v) reference information models. To fulfil these requirements a design was developed and instantiated for a specific use case on late blight protection in potato growing for a specific farmer in The Netherlands. For that purpose we:
configured the business processes that are involved in late blight protection using RAAgE 2.0 to identify which advanced ICT components are needed to support this process for this farmer;
developed the required advanced ICT components that were identified in the previous step using the FIspace platform. These components were provided by different app developers from 5 different European countries;
configured a composite application component within the FIspace platform using the configuration framework of RAAgE 2.0. This included involvement of 5 different European organizations;
instantiated and executed the application component within the FIspace platform for this specific farmer.
This resulted in prototype software that showed how we can configure business processes and multi-vendor atomic application components into a composite component to support late blight protection in potatoes for a specific farmer. It was made plausible that this approach is also applicable to other cases to create software able to support other business processes in agriculture.
Within this research we developed artefacts and substantiated that they facilitate collaboration between the actors involved and can help to develop ICT Components that improve farm enterprise integration. Still, to make ICT Mass Customisation and Best-of-Breed a more common practice, future research is required. In this research we recommend to focus on:
Development of business models to gain insight into the motives of software developers to become part of Farm Software Ecosystems. Insight into these motives can enhance the adoption of Software Ecosystems for agriculture, which makes the concept of ICT Mass Customisation more feasible.
Improving configuration of atomic application components and supporting tools as this is currently still cumbersome. We recommend focussing on one specific case to dig into all details of the case. Such a detailed description will be re-usable for many other farm business processes such as fertilization, other types of crop protection, seeding and harvesting.
Although, there are still hurdles to take we recommend continuing this research line as it can result in improved farm enterprise integration and adoption of advanced farm management styles by famers. This can enable farm enterprises to increase food production, while producing in a sustainable, safe and transparent manner.
On yield gains and yield gaps in wheat-maize intercropping : opportunities for sustainable increases in grain production
Gou, Fang - \ 2017
Wageningen University. Promotor(en): Martin van Ittersum, co-promotor(en): Wopke van der Werf. - Wageningen : Wageningen University - ISBN 9789462579811 - 202
zea mays - triticum - intercropping - crop yield - grain crops - crop production - models - photosynthesis - zea mays - triticum - tussenteelt - gewasopbrengst - graangewassen - gewasproductie - modellen - fotosynthese
Intercropping is the cultivation of two or more crop species simultaneously in the same field, while relay intercropping means that the growing periods of the crop species are only partially overlapping. Intercropping has advantages with respect to productivity, resource capture, build-up of soil organic matter, and pest and disease suppression. This thesis aims to quantify and explain the yield advantages in wheat-maize relay intercropping and to assess the importance of intercropping for food production and land use efficiency.
Wheat-maize intercropping had land equivalent ratios around or above one in two experiments in the Netherlands. Wheat in border rows showed major yield increases, and this yield increase was due to increases in the number of tillers per plant and the number of kernels per ear. The yield advantage of intercropped wheat was associated with a high radiation interception and radiation use efficiency (RUE). Under Dutch growing conditions, maize performance in the intercrop was constrained. Intercropping had a negative effect on the yield per plant and radiation use efficiency of maize. A strip intercrop model was developed, parameterized and tested with data on wheat-maize intercropping in the Netherlands. The model simulates radiation interception and growth in relay-strip intercrops with two species in different planting configurations. The model also allows simulating the consequences of border row effects for total system productivity. Bayesian analysis was applied to calibrate radiation use efficiency of wheat and maize in sole crops and intercrop. Intercropped wheat had higher a RUE than sole wheat, while intercropped maize had a lower RUE than sole maize. Intercropped maize had less favourable leaf traits (e.g. nitrogen content) during the flowering stage than sole maize in 2014, but the leaves in the intercrop had a higher photosynthetic rate than those in the sole crop. Possible explanations for this finding include differences between sole and mixed crops in water acquisition from soil, light distribution in the canopy, nitrogen distribution within the leaf and the contribution of the ear leaf to the growth of the cob. The low radiation use efficiency in intercropped maize may relate to nitrogen deficiency during grain filling. New concepts for potential yield, yield gain and yield gap in intercropping were developed in this thesis. Using crop model simulations and farm survey data, those concepts were operationalized in the context of wheat and maize production in an oasis area (Zhangye city) in northwest China. Wheat-maize intercropping resulted in substantial yield gains under potential and actual growing conditions. A comparison of potential and actual yields indicated a yield gap of 33% for sole wheat, 49% for sole maize, 15% for intercropped wheat, and 51% for intercropped maize. The land use analysis showed that discontinuing the use of intercropping in this region will decrease grain production substantially.
Overall, this thesis studied the growth and productivity of wheat-maize intercropping at organ, plant and cropping system level, and also assessed its contribution to grain production at a regional level. The findings suggest that intercropping of food crops provides opportunities to meet increasing food demands. New technologies are needed to make strip intercropping efficient in terms of labour use and breeding should pay attention to cultivars that are suitable for intercropping.