Statistical analysis of nitrogen use efficiency in Northeast China using multiple linear regression and Random Forest

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 crop 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.

Geostatistical modelling and mapping of nematode-based soil ecological quality indices in a polluted nature reserve

Nematodes are indicators of soil quality and soil health.Knowledge of the relationships between nematode-based soil quality indices and environmental properties is beneficial for assessing environmental threats on soil biota.This study evaluated the spatial distribution of nematode-based soil quality indices in a 23-ha heavy metal-polluted nature reserve using geostatistical methods.We expected that a selection of abiotic soil properties(pH and moisture,clay,organic matter,cadmium(Cd),and zinc(Zn)contents)could explain a significant portion of the spatial variation of the indices and that regression kriging could more accurately model their spatial distribution than ordinary kriging.A stratified simple random sampling scheme was used to select 80 locations where soil samples were taken to extract nematodes and derive the indices.The area had a distinct gradient in soil properties with Cd and Zn content ranging from 0.07 to 68.9 and 5.3 to 1329 mg kg^(-1),respectively.Linear regression models were fitted to describe the relationships between the indices and soil properties.By also modelling the spatial correlation structure of regression residuals using spherical semivariograms,regression kriging was used to produce maps of the indices.The regression models explained between 21% and 44% of the total original variance in the indices.Soil pH was a significant explanatory variable in almost all cases,while heavy metal conent had a remarkably low effect.In some cases,the regression residuals had spatial structure.Independent validation indicated that in all cases,regression kriging performed slightly better because of having lower values of the root mean square prediction error and a mean prediction error closer to zero than ordinary kriging.This study showed the importance of soil properties in explaining the spatial distribution of biological soil quality indices in ecological risk assessment.

Global mapping of volumetric water retention at 100,330 and 15000 cm suction using the WoSIS database

Present global maps of soil water retention(SWR)are mostly derived from pedotransfer functions(PTFs)applied to maps of other basic soil properties.As an alternative,'point-based'mapping of soil water content can improve global soil data availability and quality.We developed point-based global maps with estimated uncertainty of the volumetric SWR at 100,330 and 15000 cm suction using measured SWR data extracted from the WoSIS Soil Profile Database together with data estimated by a random forest PTF(PTF-RF).The point data was combined with around 200 environmental covariates describing vegetation,terrain morphology,climate,geology,and hydrology using DSM.In total,we used 7292,33192 and 42016 SWR point observations at 100,330 and 15000 cm,respectively,and complemented the dataset with 436108 estimated values at each suction.Tenfold cross-validation yielded a Root Mean Square Error(RMSE)of6380,7.112 and 6.48510^(-2)cm^(3)cm^(-3),and a Model Efficiency Coefficient(MEC)of0.430,0386,and 0.471,respectively,for 100,330 and 15000 cm.The results were also compared to three published global maps of SWR to evaluate differences between point-based and map-based mapping approaches.Point-based mapping performed better than the three map-based mapping approaches for 330 and 15000 cm,while for 100 cm results were similar,possibly due to the limited number of SWR observa-tions for 100 cm.Major sources or uncertainty identified included the geographical clustering of the data and the limitation of the covariates to represent the naturally high variation of SWR.