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Cadmium (Cd) is a nonessential element that occurs at above-background concentrations in many New Zealand (NZ) soils. Most of this Cd is due to the historical application of single superphosphate that was made from Nauru phosphate rock containing between 400 and 600 mg Cd kg P. Pasture Cd uptake exacerbates the entry of Cd into animal products. We sought to determine the critical environmental factors affecting Cd uptake in NZ pastures and to calculate the likely Cd intake of sheep and cattle. We tested 69 pastures throughout NZ for a range of variables, including Cd. Soil Cd and pasture Cd were positively correlated with soil P and soil concentrations of other elements found in phosphate fertilizers. We found that no single environmental variable adequately predicted pasture Cd uptake. Nevertheless, pseudo-total soil Cd and Cd extracted using a 0.05 mol L Ca(NO) solution were positively correlated with pasture Cd. Although soil pH, soil Fe, and soil Cd provided an excellent predictor of the Ca(NO)-extractable soil Cd fraction, regression models explained just 38% of the variation of the Cd concentration in pasture grasses. Incorporating the effect of pasture species composition is a crucial next step in improving these models. A calculation of the likely exposure to Cd of sheep and cattle revealed that no pastures tested resulted in sheep and cattle ingesting Cd at a rate that would result in breaching muscle-tissue food standards. For offal products, which the NZ meat industry does not sell for human consumption, food safety standards exceedence was calculated in a few cases.

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Contrafreeloading (CFL) is the phenomenon when animals work for a resource although an identical resource is available for free. Possible explanations for CFL are that animals seek context for species-specific behaviours or to control their environments. We investigated whether goats show CFL and whether breeding for productivity traits has altered its occurrence. In a manipulation task, we compared two selection lines: 27 Nigerian dwarf goats, not bred for productivity traits, and 30 dairy goats, bred for high milk yield. Over 10 trials, each goat could perform one of three behaviours: not participating in the trial, feeding for free from an open door, or opening a sliding door for a feed of similar value. The results were analysed using an Item Response Tree (IRTree) generalized linear mixed model (GLMM). The fitted probabilities to participate were > 0.87 over all trials in both selection lines. For dwarf goats, the probability of choosing the closed door, and thereby demonstrating CFL, increased from 0.30 in Trial 1 to 0.53 in Trial 10. For dairy goats, this probability was constant at approximately 0.43. Unlike dwarf goats, dairy goats were faster to approach the closed compared to the open door. Overall, our results suggest that both selection lines were similarly interested in CFL.

Riverine floodplains downstream of active or former metal sulfide mines are in many cases contaminated with trace metals and metalloids, including arsenic (As). Since decontamination of such floodplains on a large scale is unfeasible, management of contaminated land must focus on providing land use guidelines or even restrictions. This should be based on knowledge about how contaminants enter the food chain. For As, uptake by plants may be an important pathway, but the As soil-to-plant transfer under field conditions is poorly understood. Here, we investigated the soil-to-shoot transfer of As and phosphorus (P) in wild populations of herbaceous species growing along an As contamination gradient across an extensive pasture in the mining-impacted Ogosta River floodplain. The As concentrations in the shoots of Trifolium repens and Holcus lanatus reflected the soil contamination gradient. However, the soil-to-shoot transfer factors (TF) were fairly low, with values mostly below 0.07 (TF=As/As). We found no evidence for interference of As with P uptake by plants, despite extremely high molar As:P ratios (up to 2.6) in Olsen soil extracts of the most contaminated topsoils (0-20cm). Considering the restricted soil-to-shoot transfer, we estimated that for grazing livestock As intake via soil ingestion is likely more important than intake via pasture herbage.

The analysis of multispectral imagery (MSI) acquired by unmanned aerial vehicles (UAVs) and mobile near-infrared reflectance spectroscopy (NIRS) used on-site has become increasingly promising for timely assessments of grassland to support farm management. However, a major challenge of these methods is their calibration, given the large spatiotemporal variability of grassland. This study evaluated the performance of two smart farming tools in determining fresh herbage mass and grass quality (dry matter, crude protein, and structural carbohydrates): an analysis model for MSI (GrassQ) and a portable on-site NIRS (HarvestLabTM 3000). We compared them to conventional look-up tables used by farmers. Surveys were undertaken on 18 multi-species grasslands located on six farms in Switzerland throughout the vegetation period in 2018. The sampled plots represented two phenological growth stages, corresponding to an age of two weeks and four to six weeks, respectively. We found that neither the performance of the smart farming tools nor the performance of the conventional approach were satisfactory for use on multi-species grasslands. The MSI-model performed poorly, with relative errors of 99.7% and 33.2% of the laboratory analyses for herbage mass and crude protein, respectively. The errors of the MSI-model were indicated to be mainly caused by grassland and environmental characteristics that differ from the relatively narrow Irish calibration dataset. The On-site NIRS showed comparable performance to the conventional Look-up Tables in determining crude protein and structural carbohydrates (error ≤ 22.2%). However, we identified that the On-site NIRS determined undried herbage quality with a systematic and correctable error. After corrections, its performance was better than the conventional approach, indicating a great potential of the On-site NIRS for decision support on grazing and harvest scheduling.

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