Peri- and postnatal mortality of piglets is reported to be around 20% and genetic improvement in piglet survival has great potential benefits in terms of animal welfare, economics and the environment. The indication of an unfavourable genetic correlation between litter size and survival in particular points to the importance of including piglet survival in those pig breeding programmes that currently only aim to increase litter size. Phenotypically, individual birth weight is closely associated with piglet survival (Roehe and Kalm, 2000). Genetic parameters for piglet survival traits and individual birth weight therefore need to be estimated in order to genetically improve piglet survival efficiency

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Multivariate Bayesian linear-threshold models were used to estimate genetic parameters of peri- and postnatal piglet survival and individual birth weight of piglets reared under outdoor conditions. Data of 21,835 individual piglet observations were available from a 2-generation crossbreeding experiment selected for direct and maternal genetic effects of postnatal piglet survival on piglet and dam levels, respectively. In the first generation, approximately one-half of the Landrace sires used were selected for large or average breeding values of maternal genetic effects on postnatal piglet survival, whereas in the second generation the Large White sires used were selected for direct genetic effects of the same trait. Estimates of direct and maternal heritability were 0.21 and 0.15, 0.24 and 0.14, and 0.36 and 0.28 for piglet survival at birth and during the nursing period, and individual birth weight, respectively. In particular, direct heritabilities are substantially larger than those from the literature estimated for indoor-reared piglets, suggesting that genetic effects of these traits are substantially greater under outdoor conditions. Direct or maternal genetic correlations between survival traits or with birth weight were small (ranging from 0.06 to 0.17), indicating that peri- and postnatal survival are genetically under rather different control, and survival was only slightly positively influenced by birth weight. There were significant (P < 0.05) negative genetic correlations between direct and maternal genetic effects within each of the analyzed traits ranging from -0.36 to -0.45, which have to be considered when selecting for piglet survival. Adjustment of traits for litter size or inclusion of genetic groups showed insignificant effects on the magnitude of the estimated genetic parameters. The magnitude of genetic parameters suggested that there is substantial potential for genetic improvement of survival traits and birth weight in direct and maternal genetic effects, especially when piglets are kept under outdoor conditions.

Most crop models make use of a nutrient-balance approach for modelling crop response to soil fertility. To counter the vast input data requirements that are typical of these models, the crop water productivity model AquaCrop adopts a semi-quantitative approach. Instead of providing nutrient levels, users of the model provide the soil fertility level as a model input. This level is expressed in terms of the expected impact on crop biomass production, which can be observed in the field or obtained from statistics of agricultural production. The present study is the first to describe extensively, and to calibrate and evaluate, the semi-quantitative approach of the AquaCrop model, which simulates the effect of soil fertility stress on crop production as a combination of slower canopy expansion, reduced maximum canopy cover, early decline in canopy cover and lower biomass water productivity. AquaCrop's fertility response algorithms are evaluated here against field experiments with tef (Eragrostis tef (Zucc.) Trotter) in Ethiopia, with maize (Zea mays L.) and wheat (Triticum aestivum L.) in Nepal, and with quinoa (Chenopodium quinoa Willd.) in Bolivia. It is demonstrated that AquaCrop is able to simulate the soil water content in the root zone, and the crop's canopy development, dry above-ground biomass development, final biomass and grain yield, under different soil fertility levels, for all four crops. Under combined soil water stress and soil fertility stress, the model predicts final grain yield with a relative root-mean-square error of only 11–13% for maize, wheat and quinoa, and 34% for tef. The present study shows that the semi-quantitative soil fertility approach of the AquaCrop model performs well and that the model can be applied, after case-specific calibration, to the simulation of crop production under different levels of soil fertility stress for various environmental conditions, without requiring detailed field observations on soil nutrient content