Soil Research最新文献

Do some pesticides run off more than others? How does pesticide runoff vary with pesticide properties?

Improve understanding of pesticide runoff from croplands.

Concentrations in surface soil and in runoff from three Australian rainfall simulation studies and three rainfall simulation and five catchment studies in North American croplands were used. The ratio of event averaged runoff concentrations and the surface soil concentrations is the runoff extraction ratio.

Pesticide runoff concentrations were closely related to soil surface concentrations at the start of rainfall. Runoff extraction ratios were not significantly different for 13 pesticides with a wide range of properties, on gentle slopes (0–3%), but were significantly lower for three pesticides. On steeper slopes, runoff extraction was significantly greater for atrazine but lower for glyphosate and metolachlor. Low sloping, furrow irrigated fields had low sediment concentrations and low pesticide runoff concentrations for more tightly sorbed pesticides, but not for less sorbed pesticides. Runoff extraction was not significantly different for simulated and most catchment studies.

Similar runoff extraction ratios were due to similar hydrology and limited sediment concentrations. Different runoff extraction occurs on bare soil if (a) pesticides are leached from the runoff-mixing layer, requiring sorption coefficients less than two and significant infiltration, and no interflow, (b) sediment concentrations are either low (<2 g L⁻¹) or high (>100 g L⁻¹) and (c) pesticides have different concentration profiles in the runoff-mixing layer.

Conditions studied apply for croplands in the North American mid-west on silty soils and for Australian clay soils.

Soil quality is declining in Europe and globally due to agricultural practices and climate change. The European market for novel biobased fertilisers (BBFs) is growing and the new European Union fertiliser regulation promotes their use. However, knowledge about the effects of many novel BBFs on soil quality is currently very limited. In a one-year laboratory incubation experiment, this study aimed to test the effect on biological (microbial biomass carbon (C)), physical (clay dispersibility and water-holding capacity) and chemical (pH, cation exchange capacity (CEC), total C and C in soil size fractions (<250, 50–250 and >50 μm)) soil quality indicators of 10 BBFs applied at two different rates on two soil types: an Arenosol and a Luvisol. The set-up also included a soil that was subjected to long-term annual application of the compost used in the incubation. The application of BBFs generally improved soil quality, with the compost material improving soil quality most, followed by a plant-based fertiliser and a biogas digestate. The effect of BBF application on CEC, total C and particulate organic matter (POM) was related to the amount of total C added with the BBF. Furthermore, the effect on total C and POM fractions was also related to easily decomposable C added with the BBF. Comparing the single accelerated application with annual application under field conditions indicated that the long-term incubation trial is a reasonable predictor of compost long-term effects in the field. Whether this applies to BBFs with very different properties remains to be shown.

Soils can be the largest terrestrial carbon source and a potential sink of atmospheric CO₂. Soil organic carbon (SOC) dynamics can be unravelled by ¹⁴C-derived mean residence times (MRT).

We aimed to understand SOC dynamics in surface and subsurface soils along a topo-climatic gradient in the rangelands of Khuzestan Province, Iran.

Study sites were selected under two contrasting regional climates in Izeh (MAT + 19.2°C, MAP 623 mm) and Ramhormoz (MAT + 27.5°C, MAP 200 mm). Soil physicochemical properties, SOC forms, and ¹⁴C MRT and δ¹³C signatures were determined in the control profiles.

The average MRT up to 1 m depth in Izeh and Ramhormoz were 2980 and 6582 years before present, respectively. On average, a loss of 300 Mg C ha⁻¹ in SOC stocks and a rise of 430 years in SOC MRT up to 1 m can be expected per 1°C increase in MAT, 50 mm reduction in MAP, and 100 m decrease in elevation, highlighting the potential significance of MAT in SOC dynamics. Using optimistic and pessimistic carbon emission scenarios, carbon emissions in the upland areas were projected to be between 50 and 100 Mg C ha⁻¹ over 80 years.

While the most influential element on SOC stock and its relative age was likely the temperature, other factors like erosion and deposition processes can cause enhanced SOC dislocation along the topo-climatic gradient.

Soil carbon pools stabilised for centuries to millennia are susceptible to alterations due to climate and land cover change.

Soil solarisation is a method for pest and weed control pioneered in agriculture, and it is increasingly being adopted by restoration practitioners. Solarisation works by covering moist soil during hot periods with a sheet of clear plastic. The success of soil solarisation depends in large part on increasing the temperature of the topsoil. Topsoil temperature depends on several physical variables, including soil moisture content, ambient temperature, and sunlight intensity. In restoration scenarios, solarisation can be used to reduce weed and pathogen loads prior to planting target plants. It is rarely possible to have tight control over all the variables that are important for solarisation; however, practitioners can time interventions to maximise seasonal temperature and sunlight intensity. In this study, we investigated how these two key physical variables – temperature and sunlight – contributed to the success of soil solarisation. We found that while both ambient temperature and sunlight contributed to soil temperature, the data suggests that sunlight was the more influential driver of soil temperature. These results show that, when planning for soil solarisation during ecological restoration, land managers can benefit by considering sunlight as well as air temperature. The result that sunlight may be the more influential driver of soil temperature empowers land managers to better plan solarisation using sunlight projections, even when temperature is not optimal or is unpredictable.

Atterberg limit tests may be useful for evaluating baseball infield soils because these tests directly link soil behaviour to water content. Prior research has demonstrated that the liquid and plastic limits (LL and PL) of sand-clay mixtures are affected by sand properties. However, these studies have used sand exclusively <425 μm and little attention has been devoted to sand angularity or sand-size uniformity.

This research tested the effects of sand angularity and sand-size uniformity on the Atterberg limits of infield mixes containing 0–80% sand with much of the sand 425–2000 μm.

Experiment 1 compared the effect of mixing angular or round sand of equivalent size with a kaolinitic clay. Experiment 2 compared the effect of mixing one of two sands having a similar average particle size but varying uniformity with an illitic clay.

For mixes having equivalent sand content and sand size, the shape of the sand particles did not affect LL (P = 0.47) or PL (P = 0.80). Mixtures with non-uniform sand yielded higher LL than those with uniform sand (mean difference ~0.6% water content g g⁻¹). The mixtures with non-uniform sand also remained plastic at higher sand content (~72.5%) than mixtures with uniform sand (~67.5%). Calculated threshold sand contents for the two sets of mixtures agreed closely with the experiments.

Sand angularity was shown to be unimportant in this context. When average particle size was held constant, sand uniformity affected the LL water content and the sand content corresponding to a transition between plastic and non-plastic behaviour.

This research suggests that baseball field managers need not consider the angularity of sand in an infield mix but should be aware of the uniformity of the sand used to produce the mix as this may influence the mixture’s plasticity.

Bacillus subtilis (BS) is a widely used microbial agent that could improve soil fertility and soil microenvironment. There is still uncertainty about the suitability of BS for cultivating crops with high demand for nitrogen fertiliser.

To evaluate the effects of BS agent on microbial community diversity and nitrogen-cycling genes in mulberry rhizosphere soil.

Pot experiments were conducted. Different dosages (CK, 0; T1, 0.5 × 10⁶ CFU g⁻¹ soil; T2, 1 × 10⁶ CFU g⁻¹ soil; T3, 2 × 10⁶ CFU g⁻¹ soil) of BS agent were applied to irrigate the mulberry soil. The soil nutrient content, enzyme activity, bacterial community, and nitrogen-cycling genes were determined.

T1 had the highest Chao1 and Shannon index, while T3 had the lowest. BS-treated samples had higher relative abundance of Actinobacteria and Chloroflexi than that of CK. Specially, BS-treated samples had higher relative abundance of Sphingomonas, Reyranella, and Hyphomicrobium, which was significantly positively correlated with the content of organic matter, total soluble nitrogen, ammonium nitrogen, and the activity of sucrase. The abundance of genes involved in amino acid metabolism, energy metabolism, metabolism of cofactors, and vitamin functions also increased in the BS-treated samples. BS treatment significantly increased the abundance of AOA-amoA and nirK genes, but decreased the abundance of nirS and nifH genes.

An appropriate amount of BS agent could improve soil fertility, regulate the dominant bacterium communities, and affect the abundance of functional genes involved in nitrogen cycling.

BS is probably a good choice for mulberry cultivation to improve nitrogen fertiliser utilisation efficiency.

Digital soil maps (DSM) across large areas have an inability to capture soil variation at within-fields despite being at fine spatial resolutions. In addition, creating field-extent soil maps is relatively rare, largely due to cost.

To overcome these limitations by creating soil maps across multiple fields/farms and assessing the value of different remote sensing (RS) and on-the-go proximal (PS) datasets to do this.

The value of different RS and on-the-go PS data was tested individually, and in combination for mapping three different topsoil and subsoil properties (organic carbon, clay, and pH) for three cropping farms across Australia using DSM techniques.

Using both PS and RS data layers created the best predictions. Using RS data only generally led to better predictions than PS data only, likely because soil variation is driven by a number of factors, and there is a larger suite of RS variables that represent these. Despite this, PS gamma radiometrics potassium was the most widely used variable in the PS and RS scenario. The RS variables based on satellite imagery (NDVI and bare earth) were important predictors for many models, demonstrating that imagery of crops and bare soil represent variation in soil well.

The results demonstrate the value of combining both PS and RS data layers together to map agronomically important topsoil and subsoil properties at fine spatial resolutions across diverse cropping farms.

Growers that invest in implementing this could then use these products to inform important decisions regarding management of soil and crops.

In no-tillage agriculture, maintenance of soil cover combined with liming without incorporation increases nitrogen (N) loss via ammonia (NH₃) volatilisation, decreasing the efficiency of nitrogen fertilisers.

To quantify N losses by NH₃ volatilisation from conventional and enhanced efficiency fertilisers applied to a clayey and a sandy loam soil subjected or not to lime (CaCO₃) application and straw mulching.

Two laboratory experiments were carried out; one using a clayey soil, and the other using a sandy loam soil. Both experiments followed a 4 × 2 × 2 factorial design with four N sources (urea, urea-NBPT, urea-formaldehyde, and ammonium sulfate), absence and presence of liming, and absence and presence of Brachiaria ruziziensis straw mulching. NH₃ volatilisation was measured using closed flasks containing filter paper soaked with sulfuric acid and quantified by titration with sodium hydroxide.

NH₃ volatilisation was up to 62% of the N applied. Losses due to NH₃ volatilisation from both soil types decreased in the following order of treatment: liming + straw mulching > straw mulching only > liming only. Urea-formaldehyde and ammonium sulfate were the most efficient in reducing NH₃ emissions. However, when ammonium sulfate was applied to a clayey soil after liming, it resulted in higher NH₃ emissions than conventional urea.

Urea-formaldehyde showed better performance in reducing NH₃ losses due to greater stability in the presence of straw or liming.

Soils with straw and limestone can lead to large NH₃ volatilisation losses if urea conventional is broadcast.

Legacy data from prior studies enable preliminary analysis for soil security assessment which will inform future research questions.

This study aims to utilise the soil security assessment framework (SSAF) to evaluate the capacity of soil in fulfilling various roles and understand the underlying drivers.

The framework entails: (1) defining a combination of role(s) × dimension(s) and identifying a target indicator (a soil property that can be used to evaluate a particular role × dimension combination) or a surrogate indicator (an alternative indicator when there is not a clear target indicator); (2) transforming the indicator into a unitless score (ranging from 0 to 1) using a utility graph based on expert knowledge; (3) fitting the remaining soil properties (potential indicators) into utility graphs and weighing them using (a) ordination and (b) regression method. The application of this framework is demonstrated in evaluating two soil roles: nutrient storage and habitat for biodiversity (with pH and microbial DNA Shannon’s diversity index as surrogates, respectively) for an area in the lower Hunter Valley region, New South Wales, Australia.

The regression model provides utility estimates that were similar to those obtained from surrogates, in comparison to the utility derived from the ordination model.

This study provides a methodological pathway to examine the capacity and drivers of fulfilling different soil roles. The standardisation of this method opens the door to a complete quantification under the SSAF.

Indicators derived from a legacy dataset can be used for soil security assessment.

Defining soil organic carbon (SOC) ‘potential’ storage, underpins the economic feasibility of carbon sequestration; however, ‘potential’ storage is not quantifiable using historical and current empirical data. We propose a framework to define ‘attainable’ SOC storage that varies with soil properties, environmental conditions and management practices.

Within the soil fine fraction, we quantified additional storage capacity of the fine fraction SOC attainable deficit (FF_SOC__{Attainable_Def}) by the difference between attainable (FF_{SOC_Attainable}) and actual fine fraction SOC.

Using three analyses, we developed a framework to: (1) estimate the FF_{SOC_Attainable_Def} of the fine fraction of Australian agricultural soils within broad mean annual precipitation ranges and soil depth classes; (2) establish rapid prediction capability for the FF_SOC__{Attainable_Def} using infrared/partial least square regression modelling; and (3) generate spatial FF_{SOC_Attainable_Def} estimates for agricultural regions with ensemble Random Forest modelling.

Global analyses of FF_SOC__{Attainable_Def} do not consider key environmental drivers of carbon inflows and outflows nor soil depth. Separate analyses of soils derived from different combinations of precipitation and soil depth need to include variations in environmental conditions and soil properties to accurately define FF_{SOC_Attainable} and FF_{SOC_Attainable_Def} within the fine fraction. Spatially estimated FF_SOC__{Attainable_Def} stocks revealed an opportunity to increase current fine fraction SOC stock by 3.47 GT (0–0.10 m depth) and 3.24 GT (0.10–0.30 m depth).

Our findings suggests that FF_SOC__{Attainable_Def} is dynamic, not static. Caution is needed when interpreting the results from this analysis.

Deriving estimates of FF_SOC__{Attainable_Def} will reduce risks in decision making on carbon farming in national policies.