Search Results
Evaluating the potential of dietary crude protein manipulation in reducing ammonia emissions from cattle and pig manure: A meta-analysis
Dietary manipulation of animal diets by reducing crude protein (CP) intake is a strategic NH3 abatement option as it reduces the overall nitrogen input at the very beginning of the manure management chain. This study presents a comprehensive meta-analysis of scientific literature on NH3 reductions following a reduction of CP in cattle and pig diets. Results indicate higher mean NH3 reductions of 17 ± 6% per %-point CP reduction for cattle as compared to 11 ± 6% for pigs. Variability in NH3 emission reduction estimates reported for different manure management stages and pig categories did not indicate a significant influence. Statistically significant relationships exist between CP reduction, NH3 emissions and total ammoniacal nitrogen content in manure for both pigs and cattle, with cattle revealing higher NH3 reductions and a clearer trend in relationships. This is attributed to the greater attention given to feed optimization in pigs relative to cattle and also due to the specific physiology of ruminants to efficiently recycle nitrogen in situations of low protein intake. The higher NH3 reductions in cattle highlights the opportunity to extend concepts of feed optimization from pigs and poultry to cattle production systems to further reduce NH3 emissions from livestock manure. The results presented help to accurately quantify the effects of NH3 abatement following reduced CP levels in animal diets distinguishing between animal types and other physiological factors. This is useful in the development of emission factors associated with reduced CP as an NH3 abatement option. © 2017, The Author(s).
Guidelines for precise lime management based on high-resolution soil pH, texture and SOM maps generated from proximal soil sensing data
Soil acidification is caused by natural paedogenetic processes and anthropogenic impacts but can be counteracted by regular lime application. Although sensors and applicators for variable-rate liming (VRL) exist, there are no established strategies for using these tools or helping to implement VRL in practice. Therefore, this study aimed to provide guidelines for site-specific liming based on proximal soil sensing. First, high-resolution soil maps of the liming-relevant indicators (pH, soil texture and soil organic matter content) were generated using on-the-go sensors. The soil acidity was predicted by two ion-selective antimony electrodes (RMSEpH: 0.37); the soil texture was predicted by a combination of apparent electrical resistivity measurements and natural soil-borne gamma emissions (RMSEclay: 0.046 kg kg−1); and the soil organic matter (SOM) status was predicted by a combination of red (660 nm) and near-infrared (NIR, 970 nm) optical reflection measurements (RMSESOM: 6.4 g kg−1). Second, to address the high within-field soil variability (pH varied by 2.9 units, clay content by 0.44 kg kg−1 and SOM by 5.5 g kg−1), a well-established empirical lime recommendation algorithm that represents the best management practices for liming in Germany was adapted, and the lime requirements (LRs) were determined. The generated workflow was applied to a 25.6 ha test field in north-eastern Germany, and the variable LR was compared to the conventional uniform LR. The comparison showed that under the uniform liming approach, 63% of the field would be over-fertilized by approximately 12 t of lime, 6% would receive approximately 6 t too little lime and 31% would still be adequately limed. © 2020, The Author(s).
N 2 O emissions and NO 3 − leaching from two contrasting regions in Austria and influence of soil, crops and climate: a modelling approach
National emission inventories for UN FCCC reporting estimate regional soil nitrous oxide (N 2 O) fluxes by considering the amount of N input as the only influencing factor for N 2 O emissions. Our aim was to deepen the understanding of N 2 O fluxes from agricultural soils, including region specific soil and climate properties into the estimation of emission to find targeted mitigation measures for the reduction of nitrogen losses and GHG emissions. Within this project, N 2 O emissions and nitrate (NO 3 − ) leaching were modelled under spatially distinct environmental conditions in two agricultural regions in Austria taking into account region specific soil and climatic properties, management practices and crop rotations. The LandscapeDNDC ecosystem model was used to calculate N 2 O emissions and NO 3 − leaching reflecting different types of vegetation, management operations and crop rotations. In addition, N input and N fluxes were assessed and N 2 O emissions were calculated. This approach allowed identifying hot spots of N 2 O emissions. Results show that certain combinations of soil type, weather conditions, crop and management can lead to high emissions. Mean values ranged from 0.15 to 1.29 kg N 2 O–N ha −1 year −1 (Marchfeld) and 0.26 to 0.52 kg N 2 O–N ha −1 year −1 (Grieskirchen). Nitrate leaching, which strongly dominated N-losses, often reacted opposite to N 2 O emissions. Larger quantities of NO 3 − were lost during years of higher precipitation, especially if winter barley was cultivated on sandy soils. Taking into account the detected hot spots of N 2 O emissions and NO 3 − leaching most efficient measures can be addressed to mitigate environmental impacts while maximising crop production. © 2018, The Author(s).
Direct prediction of site-specific lime requirement of arable fields using the base neutralizing capacity and a multi-sensor platform for on-the-go soil mapping
Liming agricultural fields is necessary for counteracting soil acidity and is one of the oldest operations in soil fertility management. However, the best management practice for liming in Germany only insufficiently considers within-field soil variability. Thus, a site-specific variable rate liming strategy was developed and tested on nine agricultural fields in a quaternary landscape of north-east Germany. It is based on the use of a proximal soil sensing module using potentiometric, geoelectric and optical sensors that have been found to be proxies for soil pH, texture and soil organic matter (SOM), which are the most relevant lime requirement (LR) affecting soil parameters. These were compared to laboratory LR analysis of reference soil samples using the soil’s base neutralizing capacity (BNC). Sensor data fusion utilizing stepwise multi-variate linear regression (MLR) analysis was used to predict BNC-based LR (LRBNC) for each field. The MLR models achieved high adjusted R2 values between 0.70 and 0.91 and low RMSE values from 65 to 204 kg CaCO3 ha−1. In comparison to univariate modeling, MLR models improved prediction by 3 to 27% with 9% improvement on average. The relative importance of covariates in the field-specific prediction models were quantified by computing standardized regression coefficients (SRC). The importance of covariates varied between fields, which emphasizes the necessity of a field-specific calibration of proximal sensor data. However, soil pH was the most important parameter for LR determination of the soils studied. Geostatistical semivariance analysis revealed differences between fields in the spatial variability of LRBNC. The sill-to-range ratio (SRR) was used to quantify and compare spatial LRBNC variability of the nine test fields. Finally, high resolution LR maps were generated. The BNC-based LR method also produces negative LR values for soil samples with pH values above which lime is required. Hence, the LR maps additionally provide an estimate on the quantity of chemically acidifying fertilizers that can be applied to obtain an optimal soil pH value.