Micro- and nanoplastics in agricultural soils: Assessing impacts and navigating mitigation.

Micro-/nanoplastic contamination in agricultural soils raises concerns on agroecosystems and poses potential health risks. Some of agricultural soils have received significant amounts of micro-/nanoplastics (MNPs) through plastic mulch film and biosolid applications. However, a comprehensive understanding of the MNP impacts on soils and plants remains elusive. The interaction between soil particles and MNPs is an extremely complex issue due to the different properties and heterogeneity of soils and the diverse characteristics of MNPs. Moreover, MNPs are a class of relatively new anthropogenic pollutants that may negatively affect plants and food. Herein, we presented a comprehensive review of the impacts of MNPs on the properties of soil and the growth of plants. We also discussed different strategies for mitigating or eliminating MNP contamination. Moreover, perspectives for future research on MNP contamination in the agricultural soils are also highlighted.

A Forage Allowance by Forage Type Interaction Impacts the Daily Milk Yield of Early Lactation Dairy Cows.

We tested for a forage allowance effect on the milk yield of early lactation dairy cow herds grazing swards sown with perennial ryegrass (Lolium perenne L.), white clover (Trifolium repens L.) and plantain (Plantago lanceolata L.) relative to perennial ryegrass alone. The examined allowances consisted of offering 12, 14, 16, 18, 20 or 25 kg of dry matter (DM)/cow per day of grazeable herbage, with diverse swards sown as mixtures and spatially adjacent monocultures. After adapting cows to their assigned forage type for 8 days, treatment effects on milk yield and composition, blood metabolites (beta-hydroxybutyrate, non-esterified fatty acids and urea concentrations), body weight change, forage intake and selection differentials for forage species and certain nutrients were monitored over 7 days. We confirmed a forage allowance effect on milk yield improvements in dairy cows grazing diverse swards relative to perennial ryegrass monocultures. Improvements in milk yield were evident at forage allowances of 14 to 20 kg of DM/cow per day, diminishing at the highest allowance of 25 kg of DM/cow per day. Improvements in milk yield for the mixture and spatially adjacent monocultures peaked at forage allowances of 18 and 16 kg of DM/cow per day, equalling increases of 1.3 and 1.2 kg of milk/cow per day, respectively.

Pattern recognition describing spatio-temporal drivers of catchment classification for water quality.

Classification using spatial data is foundational for hydrological modelling, particularly for ungauged areas. However, models developed from classified land use drivers deliver inconsistent water quality results for the same land uses and hinder decision-making guided by those models. This paper explores whether the temporal variation of water quality drivers, such as season and flow, influence inconsistency in the classification, and whether variability is captured in spatial datasets that include original vegetation to represent the variability of biotic responses in areas mapped with the same land use. An Artificial Neural Network Pattern Recognition (ANN-PR) method is used to match catchments by Dissolved Inorganic Nitrogen (DIN) patterns in water quality datasets partitioned into Wet vs Dry Seasons and Increasing vs Retreating flows. Explainable artificial intelligence approaches are then used to classify catchments via spatial feature datasets for each catchment. Catchments matched for sharing patterns in both spatial data and DIN datasets were corroborated and the benefit of partitioning the observed DIN dataset evaluated using Kruskal Wallis method. The highest corroboration rates for spatial data classification with DIN classification were achieved with seasonal partitioning of water quality datasets and significant independence (p < 0.001 to 0.026) from non-partitioned datasets was achieved. This study demonstrated that DIN patterns fall into three categories suited to classification under differing temporal scales with corresponding vegetation types as the indicators. Categories 1 and 3 included dominance of woodlands in their datasets and catchments suited to classify together change depending on temporal scale of the data. Category 2 catchments were dominated by vineforest and classified catchments did not change under different temporal scales. This demonstrates that including original vegetation as a proxy for differences in DIN patterns will help guide future classification where only spatially mapped data is available for ungauged catchments and will better inform data needs for water modelling.

Comparison of Nutritive Values of Tropical Pasture Species Grown in Different Environments, and Implications for Livestock Methane Production: A Meta-Analysis.

The demand for dairy products is ever increasing across the world. The livestock sector is a significant source of greenhouse gas (GHG) emissions globally. The availability of high-quality pasture is a key requirement to increase the productivity of dairy cows as well as manage enteric methane emissions. Warm-season perennial grasses are the dominant forages in tropical and subtropical regions, and thus exploring their nutritive characteristics is imperative in the effort to improve dairy productivity. Therefore, we have collated a database containing a total of 4750 records, with 1277 measurements of nutritive values representing 56 tropical pasture species and hybrid cultivars grown in 26 different locations in 16 countries; this was done in order to compare the nutritive values and GHG production across different forage species, climatic zones, and defoliation management regimes. Average edaphoclimatic (with minimum and maximum values) conditions for tropical pasture species growing environments were characterized as 22.5 °C temperature (range 17.5-29.30 °C), 1253.9 mm rainfall (range 104.5-3390.0 mm), 582.6 m elevation (range 15-2393 m), and a soil pH of 5.6 (range 4.6-7.0). The data revealed spatial variability in nutritive metrics across bioclimatic zones and between and within species. The ranges of these nutrients were as follows: neutral detergent fibre (NDF) 50.9-79.8%, acid detergent fibre (ADF) 24.7-57.4%, crude protein (CP) 2.1-21.1%, dry matter (DM) digestibility 30.2-70.1%, metabolisable energy (ME)3.4-9.7 MJ kg-1 DM, with methane (CH4) production at 132.9-133.3 g animal-1 day-1. The arid/dry zone recorded the highest DM yield, with decreased CP and high fibre components and minerals. Furthermore, the data revealed that climate, defoliation frequency and intensity, in addition to their interactions, have a significant effect on tropical pasture nutritive values and CH4 production. Overall, hybrid and newer tropical cultivars performed well across different climates, with small variations in herbage quality. The current study revealed important factors that affect pasture nutritive values and CH4 emissions, with the potential for improving tropical forage through the selection and management of pasture species.

Classification of catchments for nitrogen using Artificial Neural Network Pattern Recognition and spatial data.

In hydrological modelling, classification of catchments is a fundamental task for overcoming deficits in observational datasets. Most attention on this issue has focussed on identifying the catchments with similar hydrological responses for streamflow. Yet, effective methods for catchment classification are currently lacking in respect to Dissolved Inorganic Nitrogen (DIN), a water quality constituent that, at increasing concentrations, is threatening nutrient sensitive environments. Pattern recognition, using standard Artificial Neural Network algorithm is applied, as a novel approach to classify datasets that are considered to be suitable proxies for biological and anthropogenic drivers of observed DIN releases. Eleven gauged Great Barrier Reef (GBR) catchments within Queensland Australia are classified using spatial datasets extracted from ecosystem (e.g. original ecosystem responses to biogeographic, land zone, land form, and soil type attributes) and land use maps. To evaluate the performance of the examined spatial datasets as a proxy for deductive classification, the classification process is repeated inductively, using observed DIN and streamflow data from gauging stations. The ANN-PR method is seen to generate the same classification score format for the differing dataset types, and this facilitates a direct comparison for model output for observed data corroborations. The Kruskal-Wallis test for independence, at p > 0.05, identifies the deductive classification approach as a predictor for classification using DIN observations, which lacks an independence from each other at a p value of 0.01 and 0.02. This study concludes that an ANN-PR method can integrate the ecosystem and land use mapping data to deductively classify the GBR catchments into four regions that also have similar patterns of DIN concentrations. Due to the uniform availability of the mapping data, the findings provide a sound basis for further investigations into the transposing of knowledge from gauged catchments to ungauged areas.

More milk from forage: Milk production, blood metabolites, and forage intake of dairy cows grazing pasture mixtures and spatially adjacent monocultures.

There is interest in the reincorporation of legumes and forbs into pasture-based dairy production systems as a means of increasing milk production through addressing the nutritive value limitations of grass pastures. The experiments reported in this paper were undertaken to evaluate milk production, blood metabolite concentrations, and forage intake levels of cows grazing either pasture mixtures or spatially adjacent monocultures containing perennial ryegrass (Lolium perenne), white clover (Trifolium repens), and plantain (Plantago lanceolata) compared with cows grazing monocultures of perennial ryegrass. Four replicate herds, each containing 4 spring-calving, cross-bred dairy cows, grazed 4 different forage treatments over the periods of early, mid, and late lactation. Forage treatments were perennial ryegrass monoculture (PRG), a mixture of white clover and plantain (CPM), a mixture of perennial ryegrass, white clover, and plantain (RCPM), and spatially adjacent monocultures (SAM) of perennial ryegrass, white clover, and plantain. Milk volume, milk composition, blood fatty acids, blood ß-hydroxybutyrate, blood urea N concentrations, live weight change, and estimated forage intake were monitored over a 5-d response period occurring after acclimation to each of the forage treatments. The acclimation period for the early, mid, and late lactation experiments were 13, 13, and 10 d, respectively. Milk yield (volume and milk protein) increased for cows grazing the RCPM and SAM in the early lactation experiment compared with cows grazing the PRG, whereas in the mid lactation experiment, milk fat increased for the cows grazing the RCPM and SAM when compared with the PRG treatments. Improvements in milk production from grazing the RCPM and SAM treatments are attributed to improved nutritive value (particularly lower neutral detergent fiber concentrations) and a potential increase in forage intake. Pasture mixtures or SAM containing plantain and white clover could be a strategy for alleviating the nutritive limitations of perennial ryegrass monocultures, leading to an increase in milk production for spring calving dairy cows during early and mid lactation.

Giving drought the cold shoulder: a relationship between drought tolerance and fall dormancy in an agriculturally important crop.

The growth of fall dormant/freezing tolerant plants often surpasses the growth of non-fall dormant/non-freezing tolerant types of the same species under water-limited conditions, while under irrigated conditions non-fall dormant types exhibit superior yield performance. To investigate the mechanism behind this phenomenon, we exposed seven diverse alfalfa (Medicago sativa) cultivars to water-limited and fully watered conditions and measured their shoot growth, shoot water potential and gas exchange parameters and the relative abundance of taproot RNA transcripts associated with chilling stress/freezing tolerance. Fall dormant cultivars had greater shoot growth relative to the fully watered controls under a mild water deficit (a cumulative water deficit of 625 mL pot(-1)) and did not close their stomata until lower shoot water potentials compared with the more non-fall dormant cultivars. Several gene transcripts previously associated with freezing tolerance increased in abundance when plants were exposed to a mild water deficit. Two transcripts, corF (encodes galactinol synthase) and cas18 (encodes a dehydrin-like protein), increased in abundance in fall dormant cultivars only. Once water deficit stress became severe (a cumulative water deficit of 2530 mL pot(-1)), the difference between fall dormancy groups disappeared with the exception of the expression of a type 1 sucrose synthase gene, which decreased in fall dormant cultivars. The specific adaptation of fall dormant cultivars to mild water deficit conditions and the increase in abundance of specific genes typically associated with freezing tolerance in these cultivars is further evidence of a link between freezing tolerance/fall dormancy and adaption to drought conditions in this species.