Journal Paper Digests

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Journal Paper Digests 2021 #4

  • Prediction of available phosphorus in soil: Combined use for crop production and water quality protection
  • Life cycle assessment of wheat production and wheat-based crop rotations
  • Quantifying organic carbon stocks using a stereological profile imaging method to account for rock fragments in stony soils
  • How many sampling points are needed to estimate the mean nitrate-N content of agricultural fields? A geostatistical simulation approach with uncertain variograms
  • Downscaling digital soil maps using electromagnetic induction and aerial imagery
  • In-situ and triple-collocation based evaluations of eight global root zone soil moisture products
  • Conceptual Links between Landscape Diversity and Diet Diversity: A Roadmap for Transdisciplinary Research
  • Perspectives on validation in digital soil mapping of continuous attributes-A review
  • Objective functions from Bayesian optimization to locate additional drillholes

Prediction of available phosphorus in soil: Combined use for crop production and water quality protection

Abstract Optimizing phosphorus (P) application to agricultural soils is fundamental to crop production and water quality protection. We sought to relate soil P tests and P sorption characteristics to both crop yield response to P application and environmentally critical soil P status. Barley (Hordeum vulgare L.) was grown in pot experiments with 45 soils of different P status. Half the pots were fertilized at 20 kg P ha(-1), and half received no P. Soils were extracted with ammonium lactate, sodium bicarbonate (Olsen P), dilute salt (0.0025 M CaCl2), and diffusive gradient in thin films. Soil adsorption coefficients were determined using the Freundlich isotherm equation, and the degree of P saturation was determined from both oxalate and ammonium lactate extracted Fe, Al, and P. All soil P analyses showed a nonlinear and significant relationship with yield response to P application, and all analyses manifested a threshold value above which no P response was observed. For the commonly used ammonium lactate test, inclusion of Al and Fe improved prediction of plant-available soil P. The threshold for yield response coincided with the environmentally critical values determined from the degree of P saturation. Results support the conclusion that soil P levels for which no P application is needed also have elevated risk of P loss to runoff.

Life cycle assessment of wheat production and wheat-based crop rotations

Abstract In the northern Great Plains (NGP), wheat is the primary grain commodity. There is a need for the NGP to have a detailed analysis of environmental impacts for wheat-based agricultural production systems to better understand regional agroecosystems. This article provides a cradle-to-field gate life cycle assessment (LCA) for NGP dryland wheat (Triticum aestivum L.) production. The environmental impacts for winter wheat production using crop rotation and agricultural intensification are quantified. Fourteen no-till crop rotations ranging in duration from 2 to 6 yr were evaluated and compared using data from a historical 13-yr replicated rotation study (>300 observations). Midpoint LCA categories chosen for this comparison are energy, agricultural land use, climate change potential, freshwater eutrophication, and freshwater ecotoxicity due to their direct links with agricultural management practices. The NGP farmers commonly use a fallow period every other year due to moisture limitations. This specific agricultural practice and allocations within rotations are critical considerations within agricultural LCAs. Two aspects of fallow considerations and a sensitivity analysis were also performed. The allocated midpoint impacts between crops in rotational studies averaged 0.31, 0.79, 0.62, and 0.63 kg CO2 eq. per unit of winter wheat when energy, economic, mass, and cereal unit allocations were used, respectively. Economic analysis of the studied experimental crop was performed; results demonstrated that crop insurance policies improved diversification economics by 20%. Agricultural diversification benefits and burdens were better represented by endpoint damage assessments than by midpoint impact analysis.

Quantifying organic carbon stocks using a stereological profile imaging method to account for rock fragments in stony soils

Accounting for soil organic carbon (SOC) in stony soils is critical for estimating global SOC stocks. However, accurate and cost-effective assessments of SOC stocks in stony soil profiles remain challenging due to the difficulties in accurately determining the rock fragment volume. The objective of this study was to develop a stereological profile imaging method for rock fragment volume fraction estimation and SOC stock assessment in stony soil profiles. Three soil profiles with different rock fragment lithologies, concentrations, and distributions were imaged and quantitatively sampled. The stereological profile imaging method reproduced consistent estimation of the 3-dimensional rock fragment number density distribution and volume fraction compared to the direct measurement. The SOC stocks of the three stony profiles estimated by visual estimation, profile imaging, and stereological profile imaging methods were also assessed and compared to direct measurement. The average difference in total SOC stock between direct measurement and visual estimation, profile imaging, and stereological profile imaging methods was 0.32, 0.17, and 0.09 kg m(-2), respectively. The total profile (0-100 cm) SOC stock for profile 2 differed among the visual estimation and profile imaging methods, but estimated SOC stock from the proposed method did not differ from direct measurement for the three profiles. Results also showed that the differences in SOC stock assessment between rock fragment estimation methods increased with rock fragment concentration. The proposed method was less affected by rock fragment concentration and was more stable compared to the visual and profile imaging methods. The stereological profile imaging method was demonstrated to be a reasonably accurate method for the quantification of rock fragment number density distribution, volume fraction, and SOC stock in stony soil profiles.

How many sampling points are needed to estimate the mean nitrate-N content of agricultural fields? A geostatistical simulation approach with uncertain variograms

Knowledge of how many sampling points are needed to estimate the mean content of soil nutrients in agricultural fields, given a precision requirement on the estimated mean, is limited. This paper describes a versatile geo-statistical simulation approach for predicting the variance of the mean nitrate-N (NO3-N) content within an agricultural field estimated by random sampling. In fall of 2016 sixteen agricultural fields were sampled on a square grid to model the spatial variation of NO3-N. On twelve out of sixteen fields NO3-N showed a lognormal distribution rather than a normal distribution. Variograms for (log-transformed) NO3-N are estimated using a Bayesian approach, resulting in 100 vectors with possible variogram parameters per field, obtained by MCMC sampling from the posterior distribution. Each of these variograms is used to simulate 100 maps of NO3-N, resulting in 100 x 100 maps of NO3-N per field. Each map is used to compute the variance of the estimated mean with stratified simple random sampling of 5,10, …, 50 points, with one point per compact geographical stratum. For each sample size (number of sampling points) the mean, median and P90 of the uncertainty distribution are computed. Based on the medians, the sample size required for a maximum expanded measurement uncertainty of 50% varies from < 5 to > 50. This large variation in required sample size shows the large variation among the sixteen fields in variance of NO3-N within a field.

Downscaling digital soil maps using electromagnetic induction and aerial imagery

Coarse-resolution soil maps at regional to national extents are often inappropriate for mapping intra-field variability. At the same time, sensor data, such as electromagnetic induction measurements and aerial imagery, can be highly useful for mapping soil properties that correlate with electrical conductivity or soil color. However, maps based on these data nearly always require calibration with local samples, as multiple factors can affect the sensor measurements. In this study, we present a downscaling method, which combines coarse-resolution, large extent soil maps with sensor data in order to improve predictions of soil properties. The method modifies values from coarse-resolution soil maps to predict soil properties at a location, using relationships between soil properties and sensor data from other locations. We test this method for predicting clay and soil organic matter contents at five agricultural fields located in Denmark. We test the method for one field at a time, using soil samples from the four other fields to predict soil properties. The maps produced with the method are generally more accurate than the coarse-resolution soil maps, especially for soil organic matter. The method generally overestimates prediction uncertainties, a disadvantage, which will require improvements. Overall, the method is a simple, promising tool for giving a quantitative estimate of soil properties, when no local soil samples are available.

In-situ and triple-collocation based evaluations of eight global root zone soil moisture products

Root zone soil moisture (RZSM) is a vital variable for vegetation growth, drought monitoring and agricultural water management. Satellite remote sensing measures soil moisture at the surface layer, while RZSM is derived usually by model-based simulations. Here, we provide the first comprehensive evaluation of eight RZSM products at a global scale, including GLDAS NOAH, ERA-5, MERRA-2, NCEP R1, NCEP R2, JRA-55, SMAP level 4 and SMOS level 4 datasets. An in-situ validation based on the stations from the International Soil Moisture Network (ISMN) and a triple collocation (TC) evaluation are both conducted to assess the accuracy of these RZSM products. SMAP exhibits the median highest correlation and the median lowest RMSE with in-situ stations over North America. In the TC analysis, MERRA-2 shows the highest median correlation and the median lowest error standard deviation with the unknown truth, followed by GLDAS, SMAP, JRA-55 and ERA-5. A temporal pattern analysis indicates that SMOS has a dry bias relative to other datasets and NCEP R1 has larger seasonal variations relative to other datasets over Asia and North America. The TC analysis indicates that MERRA-2, SMAP, GLDAS, JRA-55, and ERA-5 have better performance relative to other datasets. SMAP is not as good as GLDAS, MERRA-2 and JRA-55 in RZSM estimation over forest areas. The possible factors influencing RZSM performance are discussed, including precipitation forcing, assimilated observations, radio frequency interference issue and validation methods. These results and conclusions may provide new insights for the improvement of model-based RZSM estimation.

Malnutrition linked to poor quality diets affects at least 2 billion people. Forests, as well as agricultural systems linked to trees, are key sources of dietary diversity in rural settings. In the present article, we develop conceptual links between diet diversity and forested landscape mosaics within the rural tropics. First, we summarize the state of knowledge regarding diets obtained from forests, trees, and agroforests. We then hypothesize how disturbed secondary forests, edge habitats, forest access, and landscape diversity can function in bolstering dietary diversity. Taken together, these ideas help us build a framework illuminating four pathways (direct, agroecological, energy, and market pathways) connecting forested landscapes to diet diversity. Finally, we offer recommendations to fill remaining knowledge gaps related to diet and forest cover monitoring. We argue that better evaluation of the role of land cover complexity will help avoid overly simplistic views of food security and, instead, uncover nutritional synergies with forest conservation and restoration.

Perspectives on validation in digital soil mapping of continuous attributes-A review

We performed a systematic mapping of validation methods used in digital soil mapping (DSM), in order to gain an overview of current practices and make recommendations for future publications on DSM studies. A systematic search and screening procedure, largely following the RepOrting standards for Systematic Evidence Syntheses (ROSES) protocol, was carried out. It yielded a database of 188 peer-reviewed DSM studies from the past two decades, all written in English and all presenting a raster map of a continuous soil property. Review of the full-texts showed that most publications (97%) included some type of map validation, while just over one-third (35%) estimated map uncertainty. Most commonly, a combination of multiple (existing) soil sampe datasets was used and the resulting maps were validated by single data-splitting or cross-validation. It was common for essential information to be lacking in method descriptions. This is unfortunate, as lack of information on sampling design (missing in 25% of 188 studies) and sample support (missing in 45% of 188 studies) makes it difficult to interpret what derived validation metrics represent, compromising their usefulness. Therefore, we present a list of method details that should be provided in DSM studies. We also provide a detailed summary of the 28 validation metrics used in published DSM studies, how to interpret the values obtained and whether the metrics can be compared between datasets or soil attributes.

Objective functions from Bayesian optimization to locate additional drillholes

The key available information to choose new locations for drilling are the estimated ore grade values and the corresponding uncertainties at the tentative locations. These pieces of information are combined to generate a single objective function. The mathematical form of the objective function should reflect the effect of these values and their relative importance. Traditional objective function use multiplication of these parameters by different powering values. In this study, we develop two novel objective functions from the Bayesian optimization: the probability of improvement (PI), and the expected improvement (EI). These two objective functions seek new drillholes while considering the effect of the used value and their relative importance. Therefore, they can provide a trade-off between exploration and exploitation. All the objective functions have adjustable parameters. These parameters are typically tuned using expert knowledge or heuristic rules. Here, a statistical method based on cross-validation is proposed to adjust the parameters of the traditional and novel objective functions. The performance of the novel objective functions is validated against other ones using a distance based ranking method, in a phosphate deposit. The obtained results demonstrate the robustness of the EI and PI, the newly introduced objective functions from the Bayesian optimization framework.

Journal Paper Digests

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Journal Paper Digests

Published on July 01, 2021

Journal Paper Digests

Published on June 24, 2021