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• Soil water repellency (SWR) was investigated on 145 Greenlandic agricultural soils. • 98% of the investigated samples exhibited extreme SWR at their maximum SWR. • Loss-on-ignition at 550 °C was an excellent predictor of SWR. • SWR at the wilting point decreased at clay/OC ratios > 2. • The SWR was highly similar to New Zealandic pasture soils from the literature. The rapid warming of the Arctic is changing the conditions for agricultural activity in southwest Greenland markedly, which necessitates studies of the physical properties of the soil resource. Soil water repellency (SWR) is a soil property that changes soil functional behaviour across a soil-specific range in water-content (W). Although SWR occurs worldwide, it has not been studied in sub-arctic pasture and grass fields. Thus, the aim was to examine the prevalence of SWR in South Greenland and to establish pedotransfer functions for SWR on soil properties that are faster to measure than SWR, i.e. soil organic fractions, texture and soil water retention. This study included 145 soil samples from 22 sub-arctic agricultural fields distributed across three areas of South Greenland, with broad distributions in texture (clay: 0.017–0.194 kg kg−1) and organic carbon (OC) (0.009–0.241 kg kg−1) contents. The degree of soil water repellency (SWR) as a function of water content (SWR-W curve) was measured from oven-dry conditions to the water-content at which the soil became hydrophilic (W NON), and total SWR (SWR AREA) was calculated as the integrated trapezoidal area (SWR AREA) of the SWR-W curve. A total of 99% and 98% of the soil samples were water-repellent and extremely water repellent at their maximum SWR, respectively. Among the three soil organic fractions (OC, Loss-on-ignition at 550 °C, and 225 °C (LOI 550 and LOI 225)), LOI 550 was the best predictor of SWR AREA and W NON (both with r2 = 0.93). Multiple linear regressions including clay content increased r2 adj to 0.92 and 0.95 with OC and LOI 550 , respectively. The Campbell-Shiozawa (CS) model was fitted to the soil–water retention curves (pF 3.0–6.9), and the inverse slope (−α−1) of the CS model exhibited a high correlation to both SWR AREA (r2 adj of 0.87) and W NON (r2 adj of 0.93), thus suggesting that soil water retention governs SWR. Lastly, it was shown that the coefficient of proportionality between OC and both SWR AREA and W NON for these sub-arctic soils coincided with pasture soils from New Zealand. [ABSTRACT FROM AUTHOR]

• Irrigated pasture had greater SWRP than dryland pasture and irrigated crop. • Soils with SOC < 2% tended to be wettable and soils with SOC > 4% repellent. • SWRP curve described by a Gaussian model with three physically meaningful parameters. • Model parameters and SWRPC could be predicted by SOC. Soil water repellency (SWR) affects hydrological processes such as water infiltration, preferential flow, run-off, and evapotranspiration. Understanding the relationship between SWR and volumetric soil water content (θ) is critical for predicting hydrological processes in hydrophobic soils. In this study, water droplet penetration time (WDPT) was used to investigate the SWR persistence (SWRP) in three soil types with contrasting drainage levels, namely a well-drained Lismore (LIS) stony silt loam, a moderately well-drained Templeton (TEM) silt loam, and a poorly drained Waterton/Temuka (WAT) clay loam under three long-term (>20 years) land uses (irrigated pasture [IP], dryland pasture [DP], and irrigated crop [IC]) in Canterbury, New Zealand. The three soils belong to different soil orders (i.e., Brown, Pallic, and Gley, respectively) in the New Zealand soil classification. Within each land use, 9, 12, and 9 sites were sampled in the LIS, TEM, and WAT soils respectively. There was strong evidence of SWR in all three soil types under three land uses. The maximum SWRP (WDPT max), ranging from 6 to 1470 s with the median value of 56 s, differed with land use by following descending order of IP > DP > IC. The effects of land use on SWRP differed with soil type. Compared with other two soils, LIS soil had higher differences in WDPT max between IP and DP, but lower differences in WDPT max between DP and IC. Most of the SWRP parameters were significantly correlated with soil organic carbon (SOC) content. In soils with higher SOC content, SWR usually developed under drier conditions and declined as soils were maintained under wetter conditions. This study suggests that the WDPT max appears at soil water contents of 0.17 to 0.19 cm3 cm−3 (equivalent to 0.15 to 0.16 g g−1). Soil water repellency persistence was positively related to SOC content. Soils with SOC content < 2% tended to be wettable, while soils with SOC content > 4% tended to be water-repellent. A Gaussian model with three physically meaningful parameters was developed to successfully fit the SWRP curve (SWRPC, LnWDPT as a function of θ). Our study also showed the possibility to predict the model parameters and SWRPC using SOC content. The model needs to be tested across other soil types, land uses, and climates before it can be widely applied to predict the SWR occurrence from SOC data. Future work should also focus on the impacts of SOC composition on SWRP parameters and other hydrological properties for predicting soil water processes in water-repellent soils. [ABSTRACT FROM AUTHOR]

• More intensive land use led to higher risk of soil structural degradation (SSD) in NZ. • Pasture production declined by 2.5% for each 1% (0.01 cm3 cm−3) decrease in macroporosity. • Compaction increased N 2 O emissions by 51–814% (grazing) and 19–1300% (traffic). • Compaction effects on contaminant losses via runoff and drainage were inconsistent. • Estimating costs of SSD is challenging due to the lack of data. Agricultural intensification has enhanced productivity, but has also negatively affected soil structure and environmental outcomes. Agriculture is among New Zealand (NZ)'s largest industries. Like other countries, significant land use intensification over the last 20–30 years has occurred in NZ, resulting in undesirable side-effect of soil structural degradation (SSD) (e.g., soil compaction, aggregate fragmentation). Using NZ as a case study, we reviewed and, where possible, quantified the extent of SSD in NZ and its impacts and implications on production, contaminant losses via drainage and runoff, and N 2 O emissions. Knowledge gaps were identified that will help guide future research both in NZ and internationally. Our review revealed that SSD is common in many regions and under different land uses in NZ. At the national scale, 44% of sites monitored between 2014 and 2017 were below the national target for macroporosity (pore diameter > 30 μm). The occurrence of SSD was greater under more intensive land uses such as dairying and continuous cropping. Soil structural degradation from compaction is typically associated with reduced pasture and crop production. In NZ, pasture production was estimated to decrease by an average of 2.5% for every 1.0% (0.01 cm3 cm−3) decrease in macroporosity (0–10 cm soil). Compaction from livestock treading and wheel traffic has been shown to increase N 2 O emissions by 51–814% and 19–1300%, respectively, with no significant evidence that this increase is related to N loading. Effects of compaction on contaminant losses via runoff and drainage, and in particular via preferential flow, are less well researched and findings were less consistent and dependent on many factors including the degree of compaction. Important knowledge gaps include a lack of quantitative relationships between degree of SSD and soil hydraulic properties and processes (e.g., water movement and contaminant losses), and poor knowledge of critical thresholds or optimum ranges of soil physical indicators in relation to critical ecosystem services (e.g., pasture yield, gas and water regulation in soils). We also found few estimates of SSD-induced costs related to production and environmental outcomes (e.g., contaminant losses and N 2 O emissions) at either farm system, regional or national scales. More data are needed to better determine the true costs and implications for farm production and environmental effects associated with SSD. [ABSTRACT FROM AUTHOR]

• New Zealand soils had an extreme gradient in glyphosate sorption coefficient (K d). • A multiple linear regression for K d included aluminum, phosphorous and pH. • The soil properties controlling K d could be predicted with vis–NIRS. • Improved K d predictions using vis–NIRS spectral subsets were obtained. • Vis–NIRS provided more accurate determination of K d than basic soil properties. Glyphosate [N-(phosphonomethyl) glycine] is the active ingredient in Roundup, which is the most used herbicide around the world. It is a non-selective herbicide with carboxyl, amino, and phosphonate functional groups, and it has a strong affinity to the soil mineral fraction. Sorption plays a major role for the fate and transport of glyphosate in the environment. The sorption coefficient (K d) of glyphosate, and hence its mobility, varies greatly among different soil types. Determining K d is laborious and requires the use of wet chemistry. In this study, we aimed to estimate K d using basic soil properties, and visible near-infrared spectroscopy (vis–NIRS). The latter method is fast, requires no chemicals, and several soil properties can be estimated from the same spectrum. The data set included 68 topsoil samples collected across the South Island of New Zealand, with clay and organic carbon (OC) contents ranging from 0.001 to 0.520 kg kg−1 and 0.021 to 0.217 kg kg−1, respectively. The K d was determined with batch equilibration sorption experiments and ranged from 13 to 3810 L kg−1. The visible near-infrared spectra were obtained from 400 to 2500 nm. Multiple linear regression was used to correlate K d to oxalate extractable aluminium and phosphorous and pH, which resulted in an R2 of 0.89 and an RMSE of 259.59 L kg−1. Further, interval partial least squares regression with ten-fold cross-validation was used to predict K d by vis–NIRS, and an R2 of 0.93 and an RMSECV of 207.58 L kg−1 were obtained. Thus, these results show that both basic soil properties and vis–NIRS can predict the variation in K d across these samples with high accuracy and hence, that glyphosate sorption to a soil can be determined with vis–NIRS. [ABSTRACT FROM AUTHOR]