Influence of Soil Fertility Management on Nitrogen Mineralization, Urease Activity and Maize Yield

For studying the effect of soil fertility management practices on N mineralization, urease activity and maize yield, replicated field trials were established in 2015 at Misamfu and Msekera agricultural research stations (ARS) representing two geo-climatic regions of Zambia. The soil at Msekera ARS is a sandy clay loam (SCL) from a Paleustult, while that at Misamfu is a loamy sand (LS) from a Kandiustult. The field trials had three categories of treatments namely legumes, traditional and conventional. The legumes group consisted of researcher-recommended legume-cereal intercrop systems of maize with Cajanus cajan, Crotalaria juncea and Tephrosia vogelii in combination with compound D (10% N, 20% P2O5, 10% K2O) and urea (46% N) at the recommended rate (200 kg ha-1) and half of the recommended rate (100 kg ha-1). Composted cattle manure and Fundikila, a special plant biomass management technique, were the inputs under the traditional category. The conventional category consisted of a treatment to which only chemical fertilizer was applied. Urease activity was determined in surface soil samples (0-20 cm) collected from the field trials after 3 years. For N mineralization, a laboratory incubation study was conducted over 13 weeks. For the laboratory incubation, an additional treatment to which no input was applied was included as control. Application of organic inputs significantly increased the potentially mineralizable N (No) by 127% to 256% on the LS and by 51% to 131% on the SCL in comparison to the control. Similarly, the cumulative N mineralized (Ncum) was twice or thrice higher where organic inputs had been applied in comparison to the control. The No followed the order traditional > legumes > conventional > control, while the mineralization rate constant (k) followed the order legumes > conventional > traditional > control on both soils. The rate of N mineralization was significantly higher on the LS than the SCL. Higher rates of chemical fertilizer resulted in high Ncum and higher maize yield. Maize yield was significantly and positively correlated to Ncum, but inversely correlated to the amount of applied N that was mineralized (%Nmin). Urease activity was stimulated by application of organic inputs and suppressed by higher rates of chemical fertilizers. The type of organic inputs; the rate of chemical fertilizers; and soil texture are factors influencing N mineralization and maize yield. Urease activity was largely influenced by the rate of chemical fertilizer, but not the type of organic inputs or soil texture.

Topsoil Texture Regionalization for Agricultural Soils in Germany—An Iterative Approach to Advance Model Interpretation

Site-specific spatially continuous soil texture data is required for many purposes such as the simulation of carbon dynamics, the estimation of drought impact on agriculture, or the modeling of water erosion rates. At large scales, there are often only conventional polygon-based soil texture maps, which are hardly reproducible, contain abrupt changes at polygon borders, and therefore are not suitable for most quantitative applications. Digital soil mapping methods can provide the required soil texture information in form of reproducible site-specific predictions with associated uncertainties. Machine learning models were trained in a nested cross-validation approach to predict the spatial distribution of the topsoil (0–30 cm) clay, silt, and sand contents in 100 m resolution. The differential evolution algorithm was applied to optimize the model parameters. High-quality nation-wide soil texture data of 2,991 soil profiles was obtained from the first German agricultural soil inventory. We tested an iterative approach by training models on predictor datasets of increasing size, which contained up to 50 variables. The best results were achieved when training the models on the complete predictor dataset. They explained about 59% of the variance in clay, 75% of the variance in silt, and 77% of the variance in sand content. The RMSE values ranged between approximately 8.2 wt.% (clay), 11.8 wt.% (silt), and 15.0 wt.% (sand). Due to their high performance, models were able to predict the spatial texture distribution. They captured the high importance of the soil forming factors parent material and relief. Our results demonstrate the high predictive power of machine learning in predicting soil texture at large scales. The iterative approach enhanced model interpretability. It revealed that the incorporated soil maps partly substituted the relief and parent material predictors. Overall, the spatially continuous soil texture predictions provide valuable input for many quantitative applications on agricultural topsoils in Germany.

Soil Texture Distribution for East Wasit Province, Iraq

Soil texture affects many physical and chemical properties of soil. Knowledge of soil texture is essential for all water and soil studies. The aim of the research is to draw a map of the spatial distribution of soil texture in the region of eastern Wasit province and know the relationship of texture to the soil’s hydrological groups. Laboratory tests were conducted on 25 soil samples. With a depth of 50-75 cm, were selected from locations that represent the study area. According to the unified classification system, The results showed that the soil texture for the samples locations was 40% sand, 16% for both silt loam and sandy loam, 12% for loamy sand, 8% for both sandy clay loam and sandy loam. A soil texture classification map was produced for the study area. The first soil texture map for the area differs significantly from the World Food and Agriculture Organization soil texture classification map. It adopts signed tests of the site. The statistical analysis showed that the per cent sand’s standard deviation was 22.65%, silt 19.247%, and 6.416% clay. It turns out that 52% of the soil models from hydrologic group A, 24% from hydrologic group B and 24% from hydrologic group C, Arc GIS software was used to produce maps.

Risk Assessment of Runoff Generation Using Artificial Neural Network and Field Plots in The Area of Roads and Forest Lands

Runoff generation potential (RGP) on hillslopes is an important issue in the forest roads network monitoring process. In this study, the artificial neural network (ANN) was used to predict RGP in forest road hillslopes. We trained, optimized, and tested the ANN by using field plot data from the Shirghalaye watershed located in the southern part of the Caspian Sea (Iran). 45 plots were installed to measure actual runoff volume (RFP) in different environmental conditions including land cover, slope gradient, soil texture, and soil moisture. A multi-layer perceptron (MLP) network was implemented. The runoff volume was the output variable and the ground cover, slope gradient, initial moisture of soil, soil texture (clay, silt, and sand percentage) were the network inputs. The results showed that ANN can predict runoff volume within the values of an appropriate level in the training (R2=0.95, MSE= 0.009) and test stages (R2=0.80, MSE= 0.01). Moreover, the tested network was used to predict the runoff volume on the forest road hillslopes in the study area. Finally, an RGP map was generated based on the results of the prediction of the ANNs and the GIS capabilities. The results showed that using both an ANN and a GIS is a good tool to predict the RGP in the forest road hillslopes.

Carbon, nitrogen, and phosphorus stoichiometry of organic matter in Swedish forest soils and its relationship with climate, tree species, and soil texture

While the carbon (C) content of temperate and boreal forest soils is relatively well studied, much less is known about the ratios of C, nitrogen (N), and phosphorus (P) of the soil organic matter, and the abiotic and biotic factors that shape them. Therefore, the aim of this study was to explore carbon, nitrogen, and organic phosphorus (OP) contents and element ratios in temperate and boreal forest soils and their relationships with climate, dominant tree species, and soil texture. For this purpose, we studied 309 forest soils with a stand age >60 years located all over Sweden between 56° N and 68° N. The soils are a representative subsample of Swedish forest soils with a stand age >60 years that were sampled for the Swedish Forest Soil Inventory. We found that the N stock of the organic layer increased by a factor of 7.5 from −2 °C to 7.5 °C mean annual temperature (MAT), it increased almost twice as much as the organic layer stock along the MAT gradient. The increase in the N stock went along with an increase in the N : P ratio of the organic layer by a factor of 2.1 from −2 °C to 7.5 °C MAT (R2 = 0.36, p < 0.001). Forests dominated by pine had higher C : N ratios in the litter layer and mineral soil down to a depth of 65 cm than forests dominated by other tree species. Further, also the C : P ratio was increased in the pine-dominated forests compared to forests dominated by other tree species in the organic layer, but the C : OP ratio in the mineral soil was not elevated in pine forests. C, N and OP contents in the mineral soil were higher in fine-textured soils than in coarse-textured soils by a factor of 2.3, 3.5, and 4.6, respectively. Thus, the effect of texture was stronger on OP than on N and C, likely because OP adsorbs very rigidly to mineral surfaces. Further, we found, that the P and K concentrations of the organic layer were inversely related with the organic layer stock. The C and N concentrations of the mineral soil were best predicted by the combination of MAT, texture, and tree species, whereas the OP concentration was best predicted by the combination of MAT, texture and the P concentration of the parent material in the mineral soil. In the organic layer, the P concentration was best predicted by the organic layer stock. Taken together, the results show that the N : P ratio of the organic layer was most strongly related to MAT. Further, the C : N ratio was most strongly related to dominant tree species, even in the mineral subsoil. In contrast, the C : P ratio was only affected by dominant tree species in the organic layer, but the C : OP ratio in the mineral soil was hardly affected by tree species due to the strong effect of soil texture on the OP concentration.