Kansenkaarten voor duurzaam benutten Natuurlijk Kapitaal

Local projects conducted within the framework of the Natural Capital Netherlands (NKN) programmeidentified various opportunities for mutual improvement of natural capital and the economy. In a follow-upstudy we investigated whether the insights gained also apply to other parts of the Netherlands. Which areasoffer the best opportunities? What measures are needed in these areas to actually capitalise on theseopportunities, and who are the relevant stakeholders? To address these questions, the local opportunitiesidentified in the NKN projects were explored at the national level, using ‘opportunity maps’. The three localprojects are: Greening the Common Agricultural Policy, Clean Water and Delta Programm

How natural capital delivers ecosystem services: a typology derived from a systematic review

There is no unified evidence base to help decision-makers understand how the multiple components of natural capital interact to deliver ecosystem services. We systematically reviewed 780 papers, recording how natural capital attributes (29 biotic attributes and 11 abiotic factors) affect the delivery of 13 ecosystem services. We develop a simple typology based on the observation that five main attribute groups influence the capacity of natural capital to provide ecosystem services, related to: A) the physical amount of vegetation cover; B) presence of suitable habitat to support species or functional groups that provide a service; C) characteristics of particular species or functional groups; D) physical and biological diversity; and E) abiotic factors that interact with the biotic factors in groups A–D. ‘Bundles’ of services can be identified that are governed by different attribute groups. Management aimed at maximising only one service often has negative impacts on other services and on biological and physical diversity. Sustainable ecosystem management should aim to maintain healthy, diverse and resilient ecosystems that can deliver a wide range of ecosystem services in the long term. This can maximise the synergies and minimise the trade-offs between ecosystem services and is also compatible with the aim of conserving biodiversity

Effects of different management regimes on soil erosion and surface runoff in semi-arid to sub-humid rangelands

Over one billion people's livelihoods depend on dry rangelands through livestock grazing and agriculture. Livestock grazing and other management activities can cause soil erosion, increase surface runoff and reduce water availability. We studied the effects of different management regimes on soil erosion and surface runoff in semi-arid to sub-humid rangelands. Eleven management regimes were assessed, which reflected different livestock grazing intensities and rangeland conservation strategies. Our review yielded key indicators for quantifying soil erosion and surface runoff. The values of these indicators were compared between management regimes. Mean annual soil loss values in the 'natural ungrazed', 'low intensity grazed', 'high intensity grazed rangelands' and 'man-made pastures' regimes were, respectively, 717 (SE = 388), 1370 (648), 4048 (1517) and 4249 (1529) kg ha-1 yr-1. Mean surface runoff values for the same regimes were 98 (42), 170 (43), 505 (113) and 919 (267) m3 ha-1 yr-1, respectively. Soil loss and runoff decreased with decreasing canopy cover and increased with increasing slope. Further analyses suggest that livestock grazing abandonment and 'exotic plantations' reduce soil loss and runoff. Our findings show that soil erosion and surface runoff differ per management regime, and that conserving and restoring vulnerable semi-arid and sub-humid rangelands can reduce the risks of degradation

Kansenkaarten voor duurzaam benutten natuurlijk kapitaal

Lokaal uitgevoerde praktijkprojecten in het kader van het programma Natuurlijk Kapitaal Nederland laten zien dat er kansen zijn voor de wederzijdse versterking van natuur en economie. Dit leidt tot de volgende vragen: welke kansen zijn er om de opgedane kennis binnen deze praktijkprojecten op te schalen naar andere gebieden in Nederland? Waar liggen deze kansrijke gebieden. Wat zijn mogelijke maatregelen en relevante stakeholders om deze kansen daadwerkelijk in winst om te zetten? Om antwoorde te krijgen op deze vragen zijn de praktijkprojecten met behulp van 'kansenkaarten' in landelijk perspectief geplaatst

A deterministic equation to predict the accuracy of multi-population genomic prediction with multiple genomic relationship matrices

BACKGROUND: A multi-population genomic prediction (GP) model in which important pre-selected single nucleotide polymorphisms (SNPs) are differentially weighted (MPMG) has been shown to result in better prediction accuracy than a multi-population, single genomic relationship matrix ([Formula: see text]) GP model (MPSG) in which all SNPs are weighted equally. Our objective was to underpin theoretically the advantages and limits of the MPMG model over the MPSG model, by deriving and validating a deterministic prediction equation for its accuracy. METHODS: Using selection index theory, we derived an equation to predict the accuracy of estimated total genomic values of selection candidates from population [Formula: see text] ([Formula: see text]), when individuals from two populations, [Formula: see text] and [Formula: see text], are combined in the training population and two [Formula: see text], made respectively from pre-selected and remaining SNPs, are fitted simultaneously in MPMG. We used simulations to validate the prediction equation in scenarios that differed in the level of genetic correlation between populations, heritability, and proportion of genetic variance explained by the pre-selected SNPs. Empirical accuracy of the MPMG model in each scenario was calculated and compared to the predicted accuracy from the equation. RESULTS: In general, the derived prediction equation resulted in accurate predictions of [Formula: see text] for the scenarios evaluated. Using the prediction equation, we showed that an important advantage of the MPMG model over the MPSG model is its ability to benefit from the small number of independent chromosome segments ([Formula: see text]) due to the pre-selected SNPs, both within and across populations, whereas for the MPSG model, there is only a single value for [Formula: see text], calculated based on all SNPs, which is very large. However, this advantage is dependent on the pre-selected SNPs that explain some proportion of the total genetic variance for the trait. CONCLUSIONS: We developed an equation that gives insight into why, and under which conditions the MPMG outperforms the MPSG model for GP. The equation can be used as a deterministic tool to assess the potential benefit of combining information from different populations, e.g., different breeds or lines for GP in livestock or plants, or different groups of people based on their ethnic background for prediction of disease risk scores.</p

Genomic prediction for numerically small breeds, using models with pre-selected and differentially weighted markers

Background: Genomic prediction (GP) accuracy in numerically small breeds is limited by the small size of the reference population. Our objective was to test a multi-breed multiple genomic relationship matrices (GRM) GP model (MBMG) that weighs pre-selected markers separately, uses the remaining markers to explain the remaining genetic variance that can be explained by markers, and weighs information of breeds in the reference population by their genetic correlation with the validation breed. Methods: Genotype and phenotype data were used on 595 Jersey bulls from New Zealand and 5503 Holstein bulls from the Netherlands, all with deregressed proofs for stature. Different sets of markers were used, containing either pre-selected markers from a meta-genome-wide association analysis on stature, remaining markers or both. We implemented a multi-breed bivariate GREML model in which we fitted either a single multi-breed GRM (MBSG), or two distinct multi-breed GRM (MBMG), one made with pre-selected markers and the other with remaining markers. Accuracies of predicting stature for Jersey individuals using the multi-breed models (Holstein and Jersey combined reference population) was compared to those obtained using either the Jersey (within-breed) or Holstein (across-breed) reference population. All the models were subsequently fitted in the analysis of simulated phenotypes, with a simulated genetic correlation between breeds of 1, 0.5, and 0.25. Results: The MBMG model always gave better prediction accuracies for stature compared to MBSG, within-, and across-breed GP models. For example, with MBSG, accuracies obtained by fitting 48,912 unselected markers (0.43), 357 pre-selected markers (0.38) or a combination of both (0.43), were lower than accuracies obtained by fitting pre-selected and unselected markers in separate GRM in MBMG (0.49). This improvement was further confirmed by results from a simulation study, with MBMG performing on average 23% better than MBSG with all markers fitted. Conclusions: With the MBMG model, it is possible to use information from numerically large breeds to improve prediction accuracy of numerically small breeds. The superiority of MBMG is mainly due to its ability to use information on pre-selected markers, explain the remaining genetic variance and weigh information from a different breed by the genetic correlation between breeds.</p

Inbreeding depression across the genome of Dutch Holstein Friesian dairy cattle

Background: Inbreeding depression refers to the decrease in mean performance due to inbreeding. Inbreeding depression is caused by an increase in homozygosity and reduced expression of (on average) favourable dominance effects. Dominance effects and allele frequencies differ across loci, and consequently inbreeding depression is expected to differ along the genome. In this study, we investigated differences in inbreeding depression across the genome of Dutch Holstein Friesian cattle, by estimating dominance effects and effects of regions of homozygosity (ROH). Methods: Genotype (75 k) and phenotype data of 38,792 cows were used. For nine yield, fertility and udder health traits, GREML models were run to estimate genome-wide inbreeding depression and estimate additive, dominance and ROH variance components. For this purpose, we introduced a ROH-based relationship matrix. Additive, dominance and ROH effects per SNP were obtained through back-solving. In addition, a single SNP GWAS was performed to identify significant additive, dominance or ROH associations. Results: Genome-wide inbreeding depression was observed for all yield, fertility and udder health traits. For example, a 1% increase in genome-wide homozygosity was associated with a decrease in 305-d milk yield of approximately 99 kg. For yield traits only, including dominance and ROH effects in the GREML model resulted in a better fit (P < 0.05) than a model with only additive effects. After correcting for the effect of genome-wide homozygosity, dominance and ROH variance explained less than 1% of the phenotypic variance for all traits. Furthermore, dominance and ROH effects were distributed evenly along the genome. The most notable region with a favourable dominance effect for yield traits was on chromosome 5, but overall few regions with large favourable dominance effects and significant dominance associations were detected. No significant ROH-associations were found. Conclusions: Inbreeding depression was distributed quite equally along the genome and was well captured by genome-wide homozygosity. These findings suggest that, based on 75 k SNP data, there is little benefit of accounting for region-specific inbreeding depression in selection schemes

Kansenkaarten voor duurzaam benutten Natuurlijk Kapitaal

Local projects conducted within the framework of the Natural Capital Netherlands (NKN) programmeidentified various opportunities for mutual improvement of natural capital and the economy. In a follow-upstudy we investigated whether the insights gained also apply to other parts of the Netherlands. Which areasoffer the best opportunities? What measures are needed in these areas to actually capitalise on theseopportunities, and who are the relevant stakeholders? To address these questions, the local opportunitiesidentified in the NKN projects were explored at the national level, using ‘opportunity maps’. The three localprojects are: Greening the Common Agricultural Policy, Clean Water and Delta Programm

Genomic prediction from whole genome sequence in livestock: the 1000 bull genomes project

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