Journal Paper Digests 2022 #22
- How well do recently reconstructed solar-induced fluorescence datasets model gross primary productivity?
- ConstraintID: An online software tool to assist grain growers in Australia identify areas affected by soil constraints
- Role of fertilization regime on soil carbon sequestration and crop yield in a maize-cowpea intercropping system on low fertility soils
- Using Bayesian compressed sensing and sparse dictionaries to interpolate soil properties
- An overview of natural soil amendments in agriculture
- The nature of spatial variability of four soil chemical properties and the implications for soil sampling
- Improving the fusion of global soil moisture datasets from SMAP, SMOS, ASCAT, and MERRA2 by considering the non-zero error covariance
- A Conceptual Framework to Integrate Biodiversity, Ecosystem Function, and Ecosystem Service Models
A Conceptual Framework to Integrate Biodiversity, Ecosystem Function, and Ecosystem Service Models
Global biodiversity and ecosystem service models typically operate independently. Ecosystem service projections may therefore be overly optimistic because they do not always account for the role of biodiversity in maintaining ecological functions. We review models used in recent global model intercomparison projects and develop a novel model integration framework to more fully account for the role of biodiversity in ecosystem function, a key gap for linking biodiversity changes to ecosystem services. We propose two integration pathways. The first uses empirical data on biodiversity-ecosystem function relationships to bridge biodiversity and ecosystem function models and could currently be implemented globally for systems and taxa with sufficient data. We also propose a trait-based approach involving greater incorporation of biodiversity into ecosystem function models. Pursuing both approaches will provide greater insight into biodiversity and ecosystem services projections. Integrating biodiversity, ecosystem function, and ecosystem service modeling will enhance policy development to meet global sustainability goals.
Improving the fusion of global soil moisture datasets from SMAP, SMOS, ASCAT, and MERRA2 by considering the non-zero error covariance
Surface soil moisture (SSM) estimates from different sources have distinct error characteristics. Multi-source data combination is an efficient way to obtain improved SSM data with reduced uncertainties. Previous data merging studies based on the linear weight averaging scheme rarely considered the impacts of data error covariance (EC) and usually needed a reference dataset, which can lead to suboptimal merging weights. This study applied the quadruple collocation (QC) to estimate EC and combine four SSM datasets simultaneously without the need for a reference. Specifically, two passive microwave satellite datasets (the L3 Soil Moisture Active Passive (SMAP)-V7 and the L3 Soil Moisture and Ocean Salinity-INRA-CESBIO (SMOS-IC)-V2), one active microwave dataset from the Advanced Scatterometer (ASCAT), and one model dataset from the Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA2) were combined. Generally, QC-based data combination reduced SSM data uncertainties with significantly reduced unbiased Root Mean Square Error (ubRMSE) scores against in situ observations and globally decreased fMSE scores. Moreover, in situ evaluation showed that the QC-based fusion products exhibited better skills than the Tripe Collocation (TC)-based products without considering EC. There were statistically significant differences in Pearson correlation coefficients and ubRMSE metric between the QC and TC-based products. Ignoring the EC between SMAPV7 and SMOS-ICV2 caused overestimations in their relative contributions to fusion data and degraded fusion accuracy. Specifically, the QC-based merging weight was reduced averagely by 0.27 (0.28) for SMAP (IC) when their error cross-correlation increased roughly from-0.42 to 0.9. This study can provide guidance for the generation of improved merged datasets at a global scale.
The nature of spatial variability of four soil chemical properties and the implications for soil sampling
Purpose A poor understanding of the nature of variation of soil properties within a field can lead to management decisions that reduce productivity or increase off-site environmental risks. Methods The variability in total C%, total N%, plant-available (Colwell) P, and pH in CaCl2 at multiple depths is examined from two sites near Wagga Wagga and Yerong Creek in the mixed farming zone of southern New South Wales, Australia. The minimum number of cores required to estimate the mean for each soil property with a given level of precision (1%, 5%, and 10%) was determined and the distribution of the sample data was described by calculating the skewness and kurtosis. Results The number of cores (42 mm diameter) required to estimate the mean was least for soil pH (3 cores) at both sites and greatest for Colwell P (25 and 58 cores) at a 10% precision in the surface 0.1 m of soil at the Wagga Wagga and Yerong Creek sites, respectively. The distributions of soil chemical properties were greatly varied with skewness and kurtosis both influencing the data. The mean value of the pH distributions sometimes exceeded the mode, leading to an underestimation of the extent of soil acidity. The mean value for Colwell P also often exceeded the mode, indicating an overestimate of the P status of the soil from a productivity perspective but concurrently underestimating the risk of off-site losses, associated with high P values at some locations. There was also an overestimation of the organic C fractions at these sites. Noteworthy in our data was a broad distribution across a large range in all soil parameters at the soil surface which, despite a generally normal distribution at that depth, gave a mean value that represents a relatively constrained approximation of the true fertility status of the soil. Conclusion This study highlights the importance of understanding the nature of the variability in soil properties for interpreting soil test results appropriately for agronomic and environmental purposes. Due to its highly variable nature, Colwell P could not be reliably measured within the level of precision assumed under existing soil sampling guidelines used in Australia.
An overview of natural soil amendments in agriculture
This manuscript describes the natural soil amendments used in agriculture, which are divided into three groups: organic, organic-mineral, and mineral amendments. It also describes less popular agents, such as clay minerals, sewage sludge, and amendments based on slaughterhouse wastes. A specific group of organic amendments are algae-base amendments which are becoming more and more popular. The soils most improved with natural amendments include sandy loam and clay soils. Natural organic amendments are best used on light soils that are poor in organic matter and nutrients (NPK). Whereas mineral amendments can be used as fertilizers (provide mainly Si, Ca and Al) or to restore degraded soils. Based on the analyzed literature, natural soil amendments may well be considered as an alternative to synthetic agricultural agents.
Using Bayesian compressed sensing and sparse dictionaries to interpolate soil properties
Capturing the spatial variations of soil properties through interpolation is an important aspect of soil mapping, usually conducted via geostatistics. Compressed sensing (CS) is an advanced signal processing theory that has been introduced in recent years for interpolating spatial data. Existing CS interpolation methods based on pre -constructed bases require regularization parameters and can produce only smooth interpolation results. To avoid the influence of artificially regularization parameters and to obtain more realistic maps of soil properties, an interpolation method based on Bayesian compressed sensing and sparse dictionaries (BCS-D) is proposed. The results of applications to two examples confirm its feasibility for mapping soil properties and show that BCS-D can provide kriging-like maps with global and local variability, reducing the risk of over-or under-estimation of soil properties over large areas. The greater prediction accuracy of BCS-D over geostatistical simulation is another advantage. A strategy for employing small and multisource training datasets is also developed for dic-tionary learning. Generally, BCS-D can be adopted as an interpolation method to meet the demand for realistic and accurate soil maps.
Role of fertilization regime on soil carbon sequestration and crop yield in a maize-cowpea intercropping system on low fertility soils
Achieving food security through intensive agricultural practices on low fertility soils is challenging as crop productivity is increasingly curtailed by the loss of soil structural stability and rapid depletion of soil organic carbon (SOC). As such, the conversion from traditional mono-cropping to legume-cereal intercropping, especially with integrated fertilization, may increase crop yields with the least ecological footprint. We set up a 2-year field experiment in a split-plot design with cowpea-maize monoculture and intercropping under different organic-inorganic fertilization regimes, including no fertilization (control), organic input only (compost), chemical input only (NPK), and multi-nutrient enriched compost (NPKEC). We observed that intercropped maize had a significantly higher biomass yield compared to the corresponding monoculture when fertilized with NPKEC fertilizer. However, cowpea biomass yield differences between monoculture and intercropped plots were comparable under all fertilization regimes. In contrast, the grain yield advantage of both maize and cowpea was significantly enhanced under the intercropping system compared to monoculture, with NPKEC showing the most significant effect among all fertilization regimes. When comparing the relative contribution of the fertilization regime to SOC, the NPKEC fertilizer provided the highest SOC-sequestration (0.30 Mg C/ha yr(-1)). At the same time, the effect of the cropping system on C-sequestration showed that intercropping provided the highest C-sequestration (0.17 Mg C/ha yr(-1)) compared to monocultures of both crops. Although compost application significantly increased mineral associated (MAOC) and particulate associated organic carbon (PAOC) concentrations compared to unfertilized control plots, NPKEC fertilization with intercropping system was the most effective combination causing the greatest increase of both soil C pools over time. Based on redundancy analysis (RDA), the positive association of MAOC and PAOC with C-sequestration suggests the importance of both organic fractions as primary C reservoirs conducting SOC storage. Importantly, although compost alone in association with intercropping had a lower C-sequestration, it was associated to a better soil structure as confirmed by its positive relationship with macro-and micro-aggregation, water stable aggregates (WSA), and mean weight diameter (MDA). Overall, our results indicate the importance of restoring soil structure in degraded soils through appropriate land management solutions, such as stoichiometrically balanced fertilization practices (NPKEC) and crop diversification (intercropping), in order to achieve significant gains in SOC storage and, ultimately, improve crop productivity.
ConstraintID: An online software tool to assist grain growers in Australia identify areas affected by soil constraints
Soil constraints, such as soil sodicity, salinity and acidity, are one cause of yield loss for grain growers globally. A first step towards reducing the associated yield gap is the identification of the area affected and the soil constraint/s responsible for the yield loss. Within a single field, persistent spatial variation of crop growth that repeats year after year is likely due to some form of soil constraint. Vegetation indices derived from satellite imagery can provide a valuable alternative to yield maps for assessing these persistent patterns. The variation identified from remote-sensing data can be compared with soil data from the field to help identify which soil constraints are potentially causing yield differences. This application note describes ConstraintID (www. constraintid.com.au), a web-based software tool that makes it easy for growers to use and interact with remote-sensing data to (1) reveal the persistent long-term variation of crop growth within their fields, (2) devise targeted soil sampling plans and (3) interpret the resulting soil data in view of critical values for certain soil constraints.
How well do recently reconstructed solar-induced fluorescence datasets model gross primary productivity?
The collection of various long-term reconstructed solar-induced fluorescence (SIF) datasets derived at a range of spatio-temporal scales provides new opportunities for modelling vegetation dynamics, in particular, gross pri-mary productivity (GPP). Information about the proximity of the reconstructed SIF (SIFr) datasets to GPP across land cover types and climatic conditions provides important support for a better application of these products for modelling applications. We conducted a multiscale analysis of four different long-term (12 years, 2007-2018) high-resolution global SIFr datasets (0.05 degrees x 0.05 degrees), namely - CSIF (Contiguous SIF), GOSIF (Global OCO-2 SIF), LUE-SIF (Light Use Efficiency SIF), and HSIF (Harmonized SIF) -at 4-day, 8-day, and monthly time scales and found that for the majority of sites, the SIFr is linearly related to ground-based GPP measurements with the eddy covariance method. While the relationship between SIFr and GPP (i.e., the slope -GPP/SIFr) varied significantly across the SIFr datasets, sites, and land cover types, all four SIFr datasets were unequivocally a better predictor of GPP compared to remotely sensed vegetation indices - NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index), sensed by the MODIS satellite. Furthermore, we also analyzed SIF-GPP relation-ships during drought vs non-drought conditions and found that for about 30% of the sites, comprising mostly non-forests site, the SIF-GPP relationship became weaker (decreased R2) with a lower slope during drought conditions compared to non-drought conditions. Among the four different products, the CSIF (at 4-day timescale) and GOSIF (at 8-day timescale) predicted GPP better compared to LUE-SIF and HSIF across all land cover types. Owing to their long-term availability (since 2000 for CSIF and GOSIF), these SIFr datasets combined with proxies of ecosystem properties can be used to appropriately capture vegetation dynamics and the interannual vari-abilities across a wide range of climatic conditions.