Zhanguo Bai’s research while affiliated with ISRIC - World Soil Information and other places

Access to data can facilitate better informed decision making. A soil information system (SIS) is used to efficiently use, produce, organize, analyse, and serve soil data and information in a country, region or at any other scale. The report 'Development options for a Soil Information Workflow and System' offers an aid for designing a SIS for soil data practitioners (users and producers). It provides an overview of the options, choices, results, and boundary conditions, and provides links to more detailed resources to execute the design and implementation from field data collection to serving organised and analysed fit-for-purpose soil information products. The report follows the steps of the generic Soil Information Workflow. Soil information workflows can vary widely depending on the user needs and specific circumstances in which the workflows are set up and need to function. An initial step in this process is to relate the user’s needs and capabilties The report was prepared at ISRIC as part of the SIStech project, funded by the Bill & Melinda Gates Foundation and led by CAB International.

Understanding the spatial-temporal dynamics of crop nitrogen (N) use efficiency (NUE) and the relationship with explanatory environmental variables can support land-use management and policymaking. Nevertheless, the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched. In this study, stepwise multiple linear regression (SMLR) and random forest (RF) were used to evaluate the spatial and temporal variation of NUE indicators (i.e., partial factor productivity of N (PFPN); partial nutrient balance of N (PNBN)) at county scale in Northeast China (Heilongjiang, Liaoning and Jilin provinces) from 1990 to 2015. Explanatory variables included agricultural management practices, topography, climate, economy, soil and crop types. Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990-2015 period. The NUE indicators decreased with time in most counties during the study period. The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN, and 0.67 and 0.89 for PNBN, respectively. The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model. The planting area index of vegetables and beans, soil clay content, saturated water content, enhanced vegetation index in November & December, soil bulk density, and annual minimum temperature were the main explanatory variables for both NUE indicators. This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR. This novel study gives reference measurements to improve crops NUE which is one of the most effective means of managing N for sustainable development, ensuring food security, alleviating environmental degradation and increasing farmer's profitability.

With increasing discrepancies between population growth and food production in China, the monitoring of crop yield is essential to support food security policies. However, current studies about spatio-temporal variation of yield mainly focus on the influence of climatic factors on grain crops, and do not explore the contributions of agricultural, environmental and economic factors on crop yield in China. In this study, a large yield dataset, covering 31 provinces and a 38-year period from 1978 to 2015, and related explanatory variables were collected for analyzing the spatio-temporal variation of different yield aggregations using stepwise multiple linear regression. At the national scale, the average aggregate yield increased from 3.04 Mg ha⁻¹ in 1978 to 10.04 Mg ha⁻¹ in 2015. Overall, the average aggregate yield increased in all provinces but the average annual growth rates varied: it was smaller than 2.5 % in Heilongjiang, Guizhou, Beijing, Qinghai and Jilin, more than 4.0 % in Hainan, Guangxi, Ningxia, Hebei and Shaanxi, and between 2.5 % and 4.0 % in other provinces. The spatial patterns of the average yield from 1978 to 2015 were different for different crop aggregations. Most of the regression models explained more than 60 % of the yield variance, except for rice, potato and cotton models. Agricultural management practices, soil and economic covariates were important explanatory variables in all models. Topography and climatic covariates were also important for some of the crop models. The regression model of the aggregate yield for all crops explained 95 % of the yield variance, which was mainly explained by planting area index of vegetables (20 %), followed by farmer income (14 %), planting area index of other crops (orchards 11 %, melons 8 %, sugar 6 %, cereals 6 %), and density of agricultural diesel engines (5 %). Although the regression residual of the aggregate yield model was zero on average, the trends were different in different provinces: most provinces demonstrated a small negative or positive residual; the yield was substantially lower (< -0.20 Mg ha⁻¹ y⁻¹) than predicted by the regression model in three provinces in central China (Hebei, Shaanxi and Anhui) and substantially higher (> 0.20 Mg ha⁻¹ y⁻¹) in four provinces (Shanxi, Shandong, Sichuan and Guangdong). These systematic over- and underpredictions may be caused by other factors, such as plagues, pests, natural hazards, market structures (such as competition for labor or impediments to market access) and farmer’s management skills. With the increasing population and limited agricultural land resources, enhancing economic growth might be an adequate solution to meet the growing demand for food. It can also promote agricultural efficiency in China, certainly when combined with better management practices, crop composition, breeding and planting technologies.

Spatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate change mitigation. In this work we report on the development, implementation and application of a data‐driven, statistical method for mapping SOC stocks in space and time, using Argentina as a pilot. We used quantile regression forest machine learning to predict annual SOC stock at 0–30 cm depth at 250 m resolution for Argentina between 1982 and 2017. The model was calibrated using over 5,000 SOC stock values from the 36‐year time period and 35 environmental covariates. We preprocessed normalized difference vegetation index (NDVI) dynamic covariates using a temporal low‐pass filter to allow the SOC stock for a given year to depend on the NDVI of the current as well as preceding years. Predictions had modest temporal variation, with an average decrease for the entire country from 2.55 to 2.48 kg C m ⁻² over the 36‐year period (equivalent to a decline of 211 Gg C, 3.0% of the total 0–30 cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62 to 4.34 kg C m ⁻² (5.9%) during the same period. For the 2001–2015 period, predicted temporal variation was seven‐fold larger than that obtained using the Tier 1 approach of the Intergovernmental Panel on Climate Change and United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and temporal distribution of the calibration data, and the limited explanatory power of the covariates. Cross‐validation confirmed that SOC stock prediction accuracy was limited, with a mean error of 0.03 kg C m ⁻² and a root mean squared error of 2.04 kg C m ⁻² . In spite of the large uncertainties, this work showed that machine learning methods can be used for space–time SOC mapping and may yield valuable information to land managers and policymakers, provided that SOC observation density in space and time is sufficiently large. Highlights We tested the use of machine learning for space–time mapping of soil organic carbon (SOC) stock. Predictions for Argentina from 1982 to 2017 showed a 3% decrease of the topsoil SOC stock over time. The machine learning model predicted a greater temporal variation than the IPCC Tier 1 approach. Accurate machine learning SOC stock prediction requires dense soil sampling in space and time.

In this paper we present effects of four paired agricultural management practices (organic matter (OM) addition versus no organic matter input, no-tillage (NT) versus conventional tillage, crop rotation versus monoculture, and organic agriculture versus conventional agriculture) on five key soil quality indicators, i.e., soil organic matter (SOM) content, pH, aggregate stability, earthworms (numbers) and crop yield. We have considered organic matter addition, no-tillage, crop rotation and organic agriculture as “promising practices”; no organic matter input, conventional tillage, monoculture and conventional farming were taken as the respective references or “standard practice” (baseline). Relative effects were analysed through indicator response ratio (RR) under each paired practice. For this we considered data of 30 long-term experiments collected from 13 case study sites in Europe and China as collated in the framework of the EU-China funded iSQAPER project. These were complemented with data from 42 long-term experiments across China and 402 observations of long-term trials published in the literature. Out of these, we only considered experiments covering at least five years. The results show that OM addition favourably affected all the indicators under consideration. The most favourable effect was reported on earthworm numbers, followed by yield, SOM content and soil aggregate stability. For pH, effects depended on soil type; OM input favourably affected the pH of acidic soils, whereas no clear trend was observed under NT. NT generally led to increased aggregate stability and greater SOM content in upper soil horizons. However, the magnitude of the relative effects varied, e.g. with soil texture. No-tillage practices enhanced earthworm populations, but not where herbicides or pesticides were applied to combat weeds and pests. Overall, in this review, yield slightly decreased under NT. Crop rotation had a positive effect on SOM content and yield; rotation with ley very positively influenced earthworms’ numbers. Overall, crop rotation had little impact on soil pH and aggregate stability − depending on the type of intercrop; alternatively, rotation of arable crops only resulted in adverse effects. A clear positive trend was observed for earthworm abundance under organic agriculture. Further, organic agriculture generally resulted in increased aggregate stability and greater SOM content. Overall, no clear trend was found for pH; a decrease in yield was observed under organic agriculture in this review.

Sampling and analysis or visual examination of soil to assess its status and use potential is widely practiced from plot to national scales. However, the choice of relevant soil attributes and interpretation of measurements are not straightforward, because of the complexity and site-specificity of soils, legacy effects of previous land use, and trade-offs between ecosystem services. Here we review soil quality and related concepts, in terms of definition, assessment approaches, and indicator selection and interpretation. We identify the most frequently used soil quality indicators under agricultural land use. We find that explicit evaluation of soil quality with respect to specific soil threats, soil functions and ecosystem services has rarely been implemented, and few approaches provide clear interpretation schemes of measured indicator values. This limits their adoption by land managers as well as policy. We also consider novel indicators that address currently neglected though important soil properties and processes, and we list the crucial steps in the development of a soil quality assessment procedure that is scientifically sound and supports management and policy decisions that account for the multi-functionality of soil. This requires the involvement of the pertinent actors, stakeholders and end-users to a much larger degree than practiced to date.

In this paper we present effects of four paired agricultural management practices (organic matter (OM) addition versus no organic matter input, no-tillage (NT) versus conventional tillage, crop rotation versus monoculture, and organic agriculture versus conventional agriculture) on five key soil quality indicators, i.e., soil organic matter (SOM) content, pH, aggregate stability, earthworms (numbers) and crop yield. We have considered organic matter addition, no-tillage, crop rotation and organic agriculture as “promising practices”; no organic matter input, conventional tillage, monoculture and conventional farming were taken as the respective references or “standard practice” (baseline). Relative effects were analysed through indicator response ratio (RR) under each paired practice. For this we considered data of 30 long-term experiments collected from 13 case study sites in Europe and China as collated in the framework of the EU-China funded iSQAPER project. These were complemented with data from 42 long-term experiments across China and 402 observations of long-term trials published in the literature. Out of these, we only considered experiments covering at least five years. The results show that OM addition favourably affected all the indicators under consideration. The most favourable effect was reported on earthworm numbers, followed by yield, SOM content and soil aggregate stability. For pH, effects depended on soil type; OM input favourably affected the pH of acidic soils, whereas no clear trend was observed under NT. NT generally led to increased aggregate stability and greater SOM content in upper soil horizons. However, the magnitude of the relative effects varied, e.g. with soil texture. No-tillage practices enhanced earthworm populations, but not where herbicides or pesticides were applied to combat weeds and pests. Overall, in this review, yield slightly decreased under NT. Crop rotation had a positive effect on SOM content and yield; rotation with ley very positively influenced earthworms’ numbers. Overall, crop rotation had little impact on soil pH and aggregate stability − depending on the type of intercrop; alternatively, rotation of arable crops only resulted in adverse effects. A clear positive trend was observed for earthworm abundance under organic agriculture. Further, organic agriculture generally resulted in increased aggregate stability and greater SOM content. Overall, no clear trend was found for pH; a decrease in yield was observed under organic agriculture in this review.