Enhancing pedotransfer functions with environmental data for estimating bulk density and effective cation exchange capacity in a data‐sparse situation

Soil bulk density (BD) and effective cation exchange capacity (ECEC) are among the most important soil properties required for crop growth and environmental management. This study aimed to explore the combination of soil and environmental data in developing pedotransfer functions (PTFs) for BD and ECEC. Multiple linear regression (MLR) and random forest model (RFM) were employed in developing PTFs using three different data sets: soil data (PTF‐1), environmental data (PTF‐2) and the combination of soil and environmental data (PTF‐3). In developing the PTFs, three depth increments were also considered: all depth, topsoil (<0.40 m) and subsoil (>0.40 m). Results showed that PTF‐3 (R²; 0.29–0.69) outperformed both PTF‐1 (R²; 0.11–0.18) and PTF‐2 (R²; 0.22–0.59) in BD estimation. However, for ECEC estimation, PTF‐3 (R²; 0.61–0.86) performed comparably as PTF‐1 (R²; 0.58–0.76) with both PTFs out‐performing PTF‐2 (R²; 0.30–0.71). Also, grouping of data into different soil depth increments improves the estimation of BD with PTFs (especially PTF‐2 and PTF‐3) performing better at subsoils than topsoils. Generally, the most important predictors of BD are sand, silt, elevation, rainfall, temperature for estimation at topsoil while EVI, elevation, temperature and clay are the most important BD predictors in the subsoil. Also, clay, sand, pH, rainfall and SOC are the most important predictors of ECEC in the topsoil while pH, sand, clay, temperature and rainfall are the most important predictors of ECEC in the subsoil. Findings are important for overcoming the challenges of building national soil databases for large‐scale modelling in most data‐sparse countries, especially in the sub‐Saharan Africa (SSA).     

Field-scale assessment of deep drainage risk

Improving irrigation efficiency is of primary importance in arid and semi-arid regions of the world as a consequence of increasing incidences of soil and water salinisation. In the cotton-growing regions of Australia salinisation is generally a result of inefficient irrigation practices, which lead to excessive deep drainage (DD). There is therefore the need to apply a relatively inexpensive approach to assessing where inefficiencies occur and make prediction of suitability of existing and new water storage sites. However, physical methods of measuring DD, such as flux meters and lysimeters, are time-consuming and site-specific. In this paper we apply a rapid method for determining the spatial distribution of soil in an irrigated cotton field in the lower Gwydir valley. First, EC_a data (using EM38 and EM31) were used to determine a soil-sampling scheme for determining soil information such as clay content and exchangeable cations to a depth of 1.2 m. The soil data and water quality information were input into the SaLF (salt and leaching fraction) model to estimate DD rate (mm/year). In developing the relationship between EC_a and estimated DD, three exponential models (two-, three- and four-parameter) were compared and evaluated using the Aikakie information criteria (AIC). The three-parameter exponential model was found to be best and was used for further analysis. Using the geostatistical approach of multiple indicator kriging (MIK), maps of conditional probability of DD exceeding a critical cut-off value (i.e. 50, 75, 100 mm) were produced for various rates of irrigation (I=300, 600, 1,200 and 1,500 mm/year). The areas of highest risk were consistent with where water-use efficiency was problematic and thus leading to the creation of perched water tables. The advantage of this approach is that it is quick and is applicable to situations where efficient use of water is required. The results can be used for irrigation planning, particularly in the location of large irrigation infrastructure such as water reservoirs.     

The first report of Histophilus somni pneumonia in Nigerian dairy cattle

Clinical signs of severe bronchopneumonia, including anorexia, coughing, nasal discharge, dyspnoea, diarrhoea, distension of the neck, lethargy, recumbency, lameness preceding collapse, and death were observed among a herd of Holstein–Friesian dairy cattle. The outbreak occurred over a 30-day period, and attack and case-fatality rates were 0.4% and 50%, respectively. At necropsy, extensive consolidation in the cranioventral parts of the lungs was observed. Histologically, a severe acute bronchopneumonia with slight pleuritis was present. Both pathological and bacteriological evaluation of the lungs incriminated Histophilus somni (heavy growth). Supplementary laboratory investigations also isolated Clostridium and Klebsiella species (scanty growth) from the lungs. Histophilosis in cattle was confirmed for the first time in Nigeria.     

Comparative Assessment of Grassland NPP Dynamics in Response to Climate Change in China,North America,Europe and Australia from 1981 to 2010

Although climate change has been modifying grassland ecosystems for a long time, few studies on grassland ecosystems have focused on large‐scale responses to climate change. Hence, grassland net primary productivity (NPP) from 1981 to 2010, as well as its variations in China, North America, Europe and Australia, was assessed and compared using a synthetic model in this study. Subsequently, the correlations between the NPP of each grassland type and climate factors were evaluated to reveal the responses of grassland eco‐systems to climate change. The results showed that North America, which has the largest area of grassland ecosystems, exhibits maximum grassland NPP of 4225.30 ± 215.43 Tg DW year^?1, whereas Europe, which has the least area of grassland ecosystems among the four regions, exhibits minimum grassland NPP of 928.95 ± 24.68 Tg DW year^?1. Grassland NPP presented an increasing trend in China and Australia, but decreasing in Europe and North America from 1981 to 2010. In addition, grassland NPP is positively correlated with mean annual precipitation, but demonstrates notable differences with mean annual temperature. In conclusion, climate change has a significant role in explaining the spatiotemporal patterns of and the variations in grassland NPP in the four regions.     

Application of a mobile electromagnetic sensing system (MESS) to assess cause and management of soil salinization in an irrigated cotton-growing field

Abstract. Generally, traditional soil surveys do not adequately account for the spatial variability of soil properties. Maps that are derived using these cursory soil data are likely to contain errors and thus make interpretation and soil management difficult. On the other hand quantitative methods of soil inventory at the field scale involve the design and adoption of sampling regimes and laboratory analysis that are time consuming and costly. In the latter case new technologies are required to efficiently sample and observe the soil in the field. This is particularly the case where soil salinization is prevalent, and detailed quantitative information for determining its cause is required. In this paper, a Mobile Electromagnetic Sensing System (MESS) is described and its application in an irrigated-cotton field, located in the lower Namoi valley in New South Wales, Australia. Results from the EM38 instrument were found to be correlated with the effective cation exchange capacity ( r ²= 0.81). This was related to variability in soil mineralogy across the field. In addition, differences in soil chemical and textural variables, measured along a transect and adjacent to water storage, were used to identify seepage areas near a water reservoir where soil salinization was evident.     

Soil aggregate stability and aggregate-associated organic carbon under different land use or land cover types

Soil aggregate stability (SAS) is an indicator for soil condition and is greatly influenced by land use or land cover (LULC) type and other soil and environmental attributes. This study investigated the soil aggregate-size distribution, SAS, aggregate-associated organic carbon (AAOC) and the relative importance of factors affecting SAS and AAOC. Based on conditioned Latin hypercube sampling, soil aggregate samples were collected from the “A” horizon and wet sieved into large macroaggregates (>2.0 mm), small macroaggregates (0.25–2.0 mm), microaggregates (0.053–0.25 mm) and mineral fraction (<0.053 mm). The large macroaggregates accounted for 86% to 93% of the total aggregates under all LULC types except under dry land (64%) and paddy land (35%). The SAS under different LULC decreased in the order fir > shrubland > natural grassland > orchard > blue pine > broadleaf > mixed conifer > dry land > paddy land. The AAOC of the large macroaggregates constituted for 76%–90% of the total AAOC under all LULC types except under dry land (65%) and paddy land (38%). While SAS was largely influenced by the AAOC of small macroaggregates, microaggregates and large macroaggregates and LULC type, the AAOC of different aggregate fractions was mostly affected by LULC type, altitude and slope. SAS did not exhibit any significant relationship with the AAOC of different aggregate fractions under the natural LULC types but showed a strong relationship under the agricultural land indicating that AAOC is more critical for SAS under the agricultural land than under the natural LULC.     

Mapping the distribution of DDT residues as DDE in the soils of the irrigated regions of northern New South Wales,Australia using ELISA and GIS

An enzyme-linked immunosorbent assay (ELISA) specific for DDE [1,1-dichloro-2,2-bis(4-chlorophenyl)ethylene] has been used to map DDT [1,1,1-trichloro-2,2-bis(4-chlorophenyl)ethane)] residues in the top 10 cm of soil in three river valleys of northern New South Wales, Australia. Despite being almost 20 years since DDT was last applied for cotton growing in these areas, the relationship between sites of greatest application and current residue levels was strong. DDE concentrations in the range 0-2 ppm were found, although most the 389 soil samples examined contained less than 0.2 ppm of DDE. Although some relationship between mode of land use and current residue levels was apparent, this varied from valley to valley and may have reflected different farming practices and times of application. The study demonstrates that the combination of ELISA and geographical information system (GIS) analysis provides an effective means of displaying levels of soil contamination by a pesticide and the possible need for remediation.     

Structural and hydrological alterations of soil due to addition of coal fly ash

Purpose  

We tested the potential of using coal fly ash for improving the physical and hydrological characteristics of coarse and medium-textured agricultural soils.     

Assessing the Spatiotemporal Dynamic of Global Grassland Water Use Efficiency in Response to Climate Change from 2000 to 2013

Water use efficiency (WUE), which is a ratio of net primary production (NPP) to evapotranspiration (ET), is an important index representing the relationship between carbon and water cycles. This study evaluates the spatiotemporal dynamics of global grassland WUE from 2000 to 2013 to reveal the different responses of each grassland type to climate variations. Their correlations with climate variables are also investigated to reflect their dependence on climate. The average annual WUE of different grassland types follows an order of: closed shrublands > woody savannas > savannas > open shrublands > non‐woody grasslands. Although the NPP of all grassland types has increased from 2000 to 2013, 37.89 % of grassland ecosystems globally experienced a decreased WUE, in which 3.34 % has extremely significantly decreased. The WUE of open shrublands, woody savannas and non‐woody grasslands shows an overall descending trend because of the exceeding increasing rate of ET. By contrast, the decreased ET contributes to the overall ascending trend of the WUE of closed shrublands and savannas over this period. Moreover, the WUE of each grassland type reacts differently to climate variations in the northern and southern hemispheres. The grassland WUE dynamic is more controlled by precipitation than temperature at a global scale.