The objective of the study was to evaluate the potential of Fourier transform infrared spectroscopy (FTIR) analysis of milk samples to predict body energy status and related traits (energy balance (EB), dry matter intake (DMI) and efficient energy intake (EEI)) in lactating dairy cows. The data included 2371 milk samples from 63 Norwegian Red dairy cows collected during the first 105 days in milk (DIM). To predict the body energy status traits, calibration models were developed using Partial Least Squares Regression (PLSR). Calibration models were established using split-sample (leave-one cow-out) cross-validation approach and validated using an external test set. The PLSR method was implemented using just the FTIR spectra or using the FTIR together with milk yield (MY) or concentrate intake (CONCTR) as predictors of traits. Analyses were conducted for the entire first 105 DIM and separately for the two lactation periods: 5 ≤ DIM ≤ 55 and 55 < DIM ≤ 105. To test the models, an external validation using an independent test set was performed. Predictions depending on the parity (1st, 2nd and 3rd-to 6th parities) in early lactation were also investigated. Accuracy of prediction (r) for both cross-validation and external test set was defined as the correlation between the predicted and observed values for body energy status traits. Analyzing FTIR in combination with MY by PLSR, resulted in relatively high r-values to estimate EB (r = 0.63), DMI (r = 0.83), EEI (r = 0.84) using an external validation. Only moderate correlations between FTIR spectra and traits like EB, EEI and dry matter intake (DMI) have so far been published. Our hypothesis was that improvements in the FTIR predictions of EB, EEI and DMI can be obtained by (1) stratification into different stages of lactations and different parities, or (2) by adding additional information on milking and feeding traits. Stratification of the lactation stages improved predictions compared with the analyses including all data 5 ≤ DIM ≤105. The accuracy was improved if additional data (MY or CONCTR) were included in the prediction model. Furthermore, stratification into parity groups, improved the predictions of body energy status. Our results show that FTIR spectral data combined with MY or CONCTR can be used to obtain improved estimation of body energy status compared to only using the FTIR spectra in Norwegian Red dairy cattle. The best prediction results were achieved using FTIR spectra together with MY for early lactation. The results obtained in the study suggest that the modeling approach used in this paper can be considered as a viable method for predicting an individual cow's energy status.

The present study aim to explore temporal–spatial patterns of water use (WU) efficiency and its influencing factors of maize production in China during 1998–2010. WU and productivity (WP) were quantified by taking irrigation loss into account and the links between WP and nine selected indicators were revealed by the partial least squares regression (PLSR) model. Results showed that national WU and WP in maize production were 138.56 cubic gigametres (Gm3; 0.755 green and 0.245 blue) and 1.079 kg/m3, respectively. WP was enhanced in the present study due to an increase in irrigated crop yield. Provinces located in the middle–lower part of the Yellow River had high proportions of green water and WP, while high proportions of irrigation water and low WP were found in Northwest China. The dosage of pesticides per unit area, relative humidity, average temperature and precipitation were the dominant factors that affected WP. However, the relationships between WP and solar radiation, fertilizer, agricultural machinery power, irrigation proportion and irrigated efficiency were not significant. Findings of the present research may also provide a reference for regional agricultural water management.

The northern New England region includes the states of Vermont, New Hampshire, and Maine and encompasses a large degree of climate and edaphic variation across a relatively small spatial area, making it ideal for studying climate change impacts on agricultural weed communities. We sampled weed seedbanks and measured soil physical and chemical characteristics on 77 organic farms across the region and analyzed the relationships between weed community parameters and select geographic, climatic, and edaphic variables using multivariate procedures. Temperature-related variables (latitude, longitude, mean maximum and minimum temperature) were the strongest and most consistent correlates with weed seedbank composition. Edaphic variables were, for the most part, relatively weaker and inconsistent correlates with weed seedbanks. Our analyses also indicate that a number of agriculturally important weed species are associated with specific U.S. Department of Agriculture plant hardiness zones, implying that future changes in climate factors that result in geographic shifts in these zones will likely be accompanied by changes in the composition of weed communities and therefore new management challenges for farmers.

Although co-infection by multiple groups of pathogens is the norm rather than the exception in nature, most research on the effects of pathogens on their hosts has been largely based on a single or few pathogen species. Nevertheless, the health impact of co-occurring infections is evident, and it is important that scientists should consider pathogen communities rather than single relevant pathogen species when assessing the impact of multiple infections. In this work we illustrate the consequences of neglecting different pathogen taxa (viruses, protozoa, helminths, arthropods) in the explanatory power of a set of Partial Least Squares Regression (PLS-R) models used for exploring the impact of co-infections on the body condition of 57 adult feral cats; 71·5% cats were co-infected by ⩾3 groups of pathogens. The best two PLS-R models provided a first component based on the combination of helminths, protozoa and viruses, explaining 29·15% of body-condition variability. Statistical models, partially considering the pathogen community, lost between 24% and 94% of their explanatory power for explaining the cost of multiple infections. We believe that in the future, researchers assessing the impact of diseases on host life-history traits should take into account a broad representation of the pathogen community, especially during early assessment of the impact of diseases on host health.