Reducing Soil Nutrient Loss Research Articles

Evaluation of biological measures and multipurpose adaptive grass on soil bund in Lasta district, Ethiopia

The adverse effect of soil erosion is a major problem in Ethiopia, and soil and water conservation efforts must do to reduce the impact. Use of biological measures (grasses) combined with soil bund have numerous impact for reducing soil nutrient loss and increase soil moisture conservation, secure animal fodder for farm owners in low grass potential areas, enhance productivity of land and green biomass, but its adoption has been limited in the study area. This study explored the effect of grasses combined with soil bund on the experiment want to see the effect of stabilizer grasses on soil bund have seven treatments with randomized complete block design. Moisture content and bulk density data were collected and analyzed, taken undisturbed soil sample by gravimetric method, survival rate available plant per total planted times 100, tiller total number, plant height via meter and biomass using hanging balance data's were collected. The data analysis was done using R-Software and for mean separation, LSD at 5% significance level was used for moisture, bulk density, survival rate, biological parameters. Grass have positive impact on moisture content and bulk density to increase ease of use of water for grass and to stabilize the bund results in 2020 was 22.2%, 17.56%, and 12.3% of difference vetiver, Sudan grass, elephant and panicum in 2021 13% (1.36) in Sudan grass with comparison of the control treatment (1.57), respectively. Sudan grass and panicum have (100%) and (80%) performance on survival rate to rehabilitate and support the bund and protect direct runoff. Panicum has scored 77.2 average tillers in 0.15 m2 area on number of tiller that can affect biomass and direct runoff. Sudan grass 98.7 cm, elephant 85.4 cm and panicum 81 cm was resulted in 2021 and in 2020 Sudan grass 136.4 cm, elephant 91 cm and panicum 78.3 cm record on plant height. The green biomass that have great contribution for forage and other multipurpose use was Sudan grass, elephant and panicum yielded 20.8 t/ha in 2020 12.7 t/ha and 10.6 t/ha in 2022 respectively. Overall, in the experiment Sudan grass, Panicum Coloratum and Elephant grass have better adaptability and survival, increase farm land productivity contributing additional grass proceeds and have multipurpose use of fodder production.

Research Progress on Effects of Biochar on Soil Environment and Crop Nutrient Absorption and Utilization

As a by-product generated from the pyrolysis of biomass, biochar is extraordinary for improving the soil environment of agricultural fields, improving soil fertility, and promoting nutrient uptake and the utilization of crops. In recent years, breakthroughs in progress have been made regarding the fertility value of biochar and in investigations into the physicochemical properties of soil and into plant nutrient utilization. This review focuses on the physicochemical and biological properties of soil, on soil pollution remediation, on greenhouse gas emissions, and on the effects of biochar on the uptake and utilization of soil nutrients and plant nutrients, as well as on the preparation of biochar, and on biochar produced under different conditions. The results of the relevant studies show that the main characteristics of biochar depend on the biochemical properties and pyrolysis temperature of raw materials, which play an important role in nutrient transport and transformation in the soil. At low temperatures (≤400 ℃), the biochar prepared from manure and waste contains a large amount of nitrogen, which can be used as a nutrient source for plants. In addition, biochar enhances soil fertilizer retention by reducing soil nutrient loss, which in turn promotes nutrient uptake and utilization by crops. By controlling pyrolysis temperature and by optimizing biochar input, one can effectively reduce soil respiration, as well as reduce carbon emissions to achieve the goal of controlling carbon sources and increasing carbon sinks. Therefore, a long-term series of mapping studies on the effects of biochar application on agricultural ecosystems should be conducted, which in turn, it is hoped, will provide a theoretical reference for the physiological and ecological effects of biochar croplands.

Modelling and mapping soil nutrient depletion in humid highlands of East Africa using ensemble machine learning: A case study from Rwanda

Soil nutrient depletion is one of the major causes of high yield gaps and nutrient deficiencies in East Africa highlands, including Rwanda. This research sought to determine the current soil nutrient balance and its spatial variation in 10 Rwandan agro-ecological zones. Soil nitrogen (N), phosphorus (P) and potassium (K) depletion in croplands were calculated using data from 455 field trials of the Optimizing Fertilizer Recommendations in Africa (OFRA) project in Rwanda. Calculated soil nutrient balances (NPK) and 15 environmental covariates were used to calibrate soil nutrient depletion models using ensemble machine learning (EML) and 10-fold cross-validation. In the 2019–2020 growing season, annual N and K depletions were 33.6 kg N ha−1 yr−1 and 71.0 kg K ha−1 yr−1, with a positive P balance of 2.30 kg P ha−1 yr−1. High soil nutrient uptake and high soil nutrient loss due to erosion and leaching were two main causes of NPK depletion. Spatial variations of NPK balance were influenced by soil nutrient stocks, soil erosion, elevation, rainfall, soil texture, and soil bulk density. The 10-fold cross-validation showed that coefficients of determination (R2) of NPK models were 62%, 58%, and 58%, respectively. Compared to single models, ensemble machine learning improved NPK model accuracy up to 5%. Our research revealed that soil nutrient depletion was highest in the northwest and lowest in the southeast of the study area. We conclude that increasing soil nutrient inputs without reducing soil nutrient loss due to soil degradation will not decrease soil nutrient depletion in Rwanda and ensemble machine learning outperforms single models in predicting soil nutrient balance. Thesolutiontoreducehighsoilnutrientdepletionin all agro-ecological zones of Rwanda would be to prioritize soil and water conservation measures and increase soil nutrient inputs.

Tracking the Release of Soil Nitrate and Labile C in A Legume-Maize Rotation in Zimbabwe

This study compared the effect of a weedy fallow (5.2 t/ha biomass), a velvet bean (Mucuna pruriens) cut for hay (7.2 t/ha biomass) and a green-manured M. pruriens (6.49 t/ha biomass) on the dynamics of soil N and C in a maize crop. An on-farm, farmer participatory experiment was established on a farmer’s field in Wedza District, Zimbabwe. Soil mineral N and labile carbon were determined at intervals upto 120 cm depth, at maize planting and at 1 and 2 weeks after planting. Before planting, the soil mineral N content ranged from 28 kg N/ha after weed fallow to 107 kgN/ha following M. pruriens. Total nitrate concentration was highest in the 0-15 cm depth of the M. pruriens treatments in the pre-planting sampling, but following rainfall and maize planting, nitrate concentration declined rapidly. By 2 weeks after planting, 7.5 and 13.5 kg N/ha remained in the 0-120 cm soil depth of the weedy fallow and green-manured M. pruriens, respectively. Improving synchrony of nutrient release and uptake is critical when applying high quality residues which breakdown relatively slowly. This could result in significant inputs of C, release nutrients more slowly and reduce soil nutrient losses.

The role of arbuscular mycorrhizas in reducing soil nutrient loss.

Substantial amounts of nutrients are lost from soils via leaching and as gaseous emissions. These losses can be environmentally damaging and expensive in terms of lost agricultural production. Plants have evolved many traits to optimize nutrient acquisition, including the formation of arbuscular mycorrhizas (AM), associations of plant roots with fungi that acquire soil nutrients. There is emerging evidence that AM have the ability to reduce nutrient loss from soils by enlarging the nutrient interception zone and preventing nutrient loss after rain-induced leaching events. Until recently, this important ecosystem service of AM had been largely overlooked. Here we review the role of AM in reducing nutrient loss and conclude that this role cannot be ignored if we are to increase global food production in an environmentally sustainable manner.

Radon diffusion through thin plastics

Soil nutrient depletion is one of the major causes of high yield gaps and nutrient deficiencies in East Africa highlands, including Rwanda. This research sought to determine the current soil nutrient balance and its spatial variation in 10 Rwandan agro-ecological zones. Soil nitrogen (N), phosphorus (P) and potassium (K) depletion in croplands were calculated using data from 455 field trials of the Optimizing Fertilizer Recommendations in Africa (OFRA) project in Rwanda. Calculated soil nutrient balances (NPK) and 15 environmental covariates were used to calibrate soil nutrient depletion models using ensemble machine learning (EML) and 10-fold cross-validation. In the 2019–2020 growing season, annual N and K depletions were 33.6 kg N ha−1 yr−1 and 71.0 kg K ha−1 yr−1, with a positive P balance of 2.30 kg P ha−1 yr−1. High soil nutrient uptake and high soil nutrient loss due to erosion and leaching were two main causes of NPK depletion. Spatial variations of NPK balance were influenced by soil nutrient stocks, soil erosion, elevation, rainfall, soil texture, and soil bulk density. The 10-fold cross-validation showed that coefficients of determination (R2) of NPK models were 62%, 58%, and 58%, respectively. Compared to single models, ensemble machine learning improved NPK model accuracy up to 5%. Our research revealed that soil nutrient depletion was highest in the northwest and lowest in the southeast of the study area. We conclude that increasing soil nutrient inputs without reducing soil nutrient loss due to soil degradation will not decrease soil nutrient depletion in Rwanda and ensemble machine learning outperforms single models in predicting soil nutrient balance. The solution to reduce high soil nutrient depletion in all agro-ecological zones of Rwanda would be to prioritize soil and water conservation measures and increase soil nutrient inputs.