Statistical Analysis versus the M5P Machine Learning Algorithm to Analyze the Yield of Winter Wheat in a Long-Term Fertilizer Experiment

To compare how different analytical methods explain crop yields from a long-term field experiment (LTFE), we analyzed the grain yield of winter wheat (WW) under different fertilizer applications in Müncheberg, Germany. An analysis of variance (ANOVA), linear mixed-effects model (LMM), and MP5 regression tree model were used to evaluate the grain yield response. All the methods identified fertilizer application and environmental factors as the main variables that explained 80% of the variance in grain yields. Mineral nitrogen fertilizer (NF) application was the major factor that influenced the grain yield in all methods. Farmyard manure slightly influenced the grain yield with no NF application in the ANOVA and M5P regression tree. While sources of environmental factors were unmeasured in the ANOVA test, they were quantified in detail in the LMM and M5P model. The LMM and M5P model identified the cumulative number of freezing days in December as the main climate-based determinant of the grain yield variation. Additionally, the temperature in October, the cumulative number of freezing days in February, the yield of the preceding crop, and the total nitrogen in the soil were determinants of the grain yield in both models. Apart from the common determinants that appeared in both models, the LMM additionally showed precipitation in June and the cumulative number of days in July with temperatures above 30 °C, while the M5P model showed soil organic carbon as an influencing factor of the grain yield. The ANOVA results provide only the main factors affecting the WW yield. The LMM had a better predictive performance compared to the M5P, with smaller root mean square and mean absolute errors. However, they were richer regressors than the ANOVA. The M5P model presented an intuitive visualization of important variables and their critical thresholds, and revealed other variables that were not captured by the LMM model. Hence, the use of different methods can strengthen the statement of the analysis, and thus, the co-use of the LMM and M5P model should be considered, especially in large databases involving multiple variables.Peer Reviewe

Proximal Soil Sensing – A Contribution for Species Habitat Distribution Modelling of Earthworms in Agricultural Soils?

Earthworms are important for maintaining soil ecosystem functioning and serve as indicators of soil fertility. However, detection of earthworms is time-consuming, which hinders the assessment of earthworm abundances with high sampling density over entire fields. Recent developments of mobile terrestrial sensor platforms for proximal soil sensing (PSS) provided new tools for collecting dense spatial information of soils using various sensing principles. Yet, the potential of PSS for assessing earthworm habitats is largely unexplored. This study investigates whether PSS data contribute to the spatial prediction of earthworm abundances in species distribution models of agricultural soils

Organic carbon content determination in soils: challenges and opportunities of elemental analysis versus thermogravimetry

Sustainable soil management needs reliable and accurate monitoring of soil organic carbon (SOC) content. However, despite of the development of analytical techniques during last decades, the detection opportunities for short term and rather small changes in SOC induced by organic fertilization, organic amendments or land use changes are still limited with the available methods. This study aims to quantify the theoretical detection opportunities for changes in SOC content with elemental analysis (EA) as the standard method in comparing with thermogravimetry (TG) as an enhanced traditional approach derived from soil organic matter determination via mass losses on ignition. The carried out experiments consist of mixing soil samples from non-fertilized plots of three long-term agricultural experiments in Bad Lauchstaedt, Großbeeren and Muencheberg (silty loam, loamy sand and silty sand) with straw, farmyard manure, sheep faeces and charcoal in four quantities (3 t×ha-1, 20 t×ha-1, 60 t×ha-1 and 180 t×ha‑1fresh matter) under laboratory conditions.The quantities were based on fresh matter application in agricultural practice accepting different amounts of added organic carbon. The results confirm EA as a method of higher reliability and accuracy for carbon content determination. TG allows to distinguish the different types of added amendments with high sensitivity. This was achieved by using newly developed evaluation algorithms for the thermal decay dynamics. We conclude from these results that TG cannot substitute EA to determine organic carbon on a routine base. However, TG could be a supplementary fingerprinting technique for the detection of added organic carbon to soils from organic fertilizers and to distinguish sources of geological or anthropogenic origin enabling a future assessment of soil organic carbon quality

Multi-modelling predictions show high uncertainty of required carbon input changes to reach a 4‰ target

Soils store vast amounts of carbon (C) on land, and increasing soil organic carbon (SOC) stocks in already managed soils such as croplands may be one way to remove C from the atmosphere, thereby limiting subsequent warming. The main objective of this study was to estimate the amount of additional C input needed to annually increase SOC stocks by 4%(0) at 16 long-term agricultural experiments in Europe, including exogenous organic matter (EOM) additions. We used an ensemble of six SOC models and ran them under two configurations: (1) with default parametrization and (2) with parameters calibrated site-by-site to fit the evolution of SOC stocks in the control treatments (without EOM). We compared model simulations and analysed the factors generating variability across models. The calibrated ensemble was able to reproduce the SOC stock evolution in the unfertilised control treatments. We found that, on average, the experimental sites needed an additional 1.5 +/- 1.2 Mg C ha(-)(1) year(-1) to increase SOC stocks by 4%(0) per year over 30 years, compared to the C input in the control treatments (multi-model median +/- median standard deviation across sites). That is, a 119% increase compared to the control. While mean annual temperature, initial SOC stocks and initial C input had a significant effect on the variability of the predicted C input in the default configuration (i.e., the relative standard deviation of the predicted C input from the mean), only water-related variables (i.e., mean annual precipitation and potential evapotranspiration) explained the divergence between models when calibrated. Our work highlights the challenge of increasing SOC stocks in agriculture and accentuates the need to increasingly lean on multi-model ensembles when predicting SOC stock trends and related processes. To increase the reliability of SOC models under future climate change, we suggest model developers to better constrain the effect of water-related variables on SOC decomposition

Nitrogen fertiliser replacement values for organic amendments appear to increase with N application rates

Nitrogen (N) supply from organic amendments [such as farmyard manure (FYM), slurries or crop residues] to crops is commonly expressed in the amendment’s Nitrogen Fertiliser Replacement Value (NFRV). Values for NFRV can be determined by comparison of crop yield or N uptake in amended plots against mineral fertiliser-only plots. NFRV is then defined as the amount of mineral fertiliser N saved when using organic amendment-N (kg/kg), while attaining the same crop yield. Factors known to affect NFRV are crop type cultivated, soil type, manuring history and method or time of application. We investigated whether long-term NFRV depends on N application rates. Using data from eight long term experiments in Europe, values of NFRV at low total N supply were compared with values of NFRV at high total N supply. Our findings show that FYM has a significant higher NFRV value at high total N supply than at low total N supply (1.12 vs. 0.53, p = 0.04). For the other amendment types investigated, NFRV was also higher at high total N supply than at low total N supply, but sample sizes were too small or variations too large to detect significant differences. Farmers in Europe usually operate at high rates of total N applied. If fertiliser supplements are based on NFRV of the manure estimated at low total N supply, N fertiliser requirements might be overestimated. This might lead to overuse of N, lower N use efficiency and larger losses of N to the environment