Modeling Approaches to Assess Soil Erosion by Water at the Field Scale with Special Emphasis on Heterogeneity of Soils and Crops

Information on soil erosion and related sedimentation processes are very important for natural resource management and sustainable farming. Plenty of models are available for studying soil erosion but only a few are suitable for dynamic soil erosion assessments at the field-scale. To date, there are no field-scale dynamic models available considering complex agricultural systems for the simulation of soil erosion. We conducted a review of 51 different models evaluated based on their representation of the processes of soil erosion by water. Secondly, we consider their suitability for assessing soil erosion for more complex field designs, such as patch cropping, strip cropping and agroforestry (alley-cropping systems) and other land management practices. Several models allow daily soil erosion assessments at the sub-field scale, such as EPIC, PERFECT, GUEST, EPM, TCRP, SLEMSA, APSIM, RillGrow, WaNuLCAS, SCUAF, and CREAMS. However, further model development is needed with respect to the interaction of components, i.e., rainfall intensity, overland flow, crop cover, and their scaling limitations. A particular shortcoming of most of the existing field scale models is their one-dimensional nature. We further suggest that platforms with modular structure, such as SIMPLACE and APSIM, offer the possibility to integrate soil erosion as a separate module/component and link to GIS capabilities, and are more flexible to simulate fluxes of matter in the 2D/3D dimensions. Since models operating at daily scales often do not consider a horizontal transfer of matter, such modeling platforms can link erosion components with other environmental components to provide robust estimations of the three-dimensional fluxes and sedimentation processes occurring during soil erosion events.Peer reviewe

Spatial and temporal patterns of agrometeorological indicators in maize producing provinces of South Africa

Climate change impacts on maize production in South Africa, i.e., interannual yield variabilities, are still not well understood. This study is based on a recently released reanalysis of climate observations (AgERA5), i.e., temperature, precipitation, solar radiation, and wind speed data. The study assesses climate change effects by quantifying the trend of agrometeorological indicators, their correlation with maize yield, and analyzing their spatiotemporal patterns using Empirical Orthogonal Function. Thereby, the main agrometeorological factors that affected yield variability for the last 31 years (1990/91-2020/21 growing season) in major maize production provinces, namely Free State, KwaZulu-Natal, Mpumalanga, and North West are identified. Results show that there was a significant positive trend in temperature that averages 0.03-0.04 degrees C per year and 0.02-0.04 degrees C per growing season. There was a decreasing trend in precipitation in Free State with 0.01 mm per year. Solar radiation did not show a significant trend. Wind speed in Free State increased at a rate of 0.01 ms(-1) per growing season. Yield variabilities in Free State, Mpumalanga, and North West show a significant positive correlation (r > 0.43) with agrometeorological variables. Yield in KwaZulu-Natal is not influenced by climate factors. The leading mode (50-80% of total variance) of each agrometeorological variable indicates spatially homogenous pattern across the regions. The dipole patterns of the second and the third mode suggest the variabilities of agrometeorological indicators are linked to South Indian high pressure and the warm Agulhas current. The corresponding principal components were mainly associated with strong climate anomalies which are identified as El Nino and La Nina events.Peer reviewe

Anaerobic Digestate from Biogas Plants—Nuisance Waste or Valuable Product?

Biogas production in waste-to-energy plants will support the decarbonization of the energy sector and enhance the EU’s energy transformation efforts. Digestates (DG) formed during the anaerobic digestion of organic wastes contain large amounts of nutrients. Their use for plant fertilization allows for diversifying and increasing the economic efficiency of farming activities. However, to avoid regional production surpluses, processing technologies allowing the acquisition of products that can be transported over long distances are required. This study therefore aimed at determining the effect of applied methods of DG treatment on the chemical composition of the resulting products and their effect on the yields and chemical composition of plants. The following digestate-based products (DGBPs) were tested: two different digestates (DGs), their liquid (LF) and solid fractions (SF) and pellets from DGs (PDG), and pellets form SFs (PSF). Results from the experiment show that during SF/LF separation of DGs, >80% of nitrogen and 87% of potassium flows to LFs, whereas >60% of phosphorus and 70% of magnesium flows to SFs. The highest yields were obtained using untreated DGs and LFs. The application of DGs and LFs was not associated with a leaching of nutrients to the environment (apparent nutrients recovery from these products exceeded 100%). Pelletized DG and SF forms can be used as slow-release fertilizer, although their production leads to significant nitrogen losses (>95%) by ammonia volatilization

Anaerobic Digestate from Biogas Plants—Nuisance Waste or Valuable Product?

Biogas production in waste-to-energy plants will support the decarbonization of the energy sector and enhance the EU’s energy transformation efforts. Digestates (DG) formed during the anaerobic digestion of organic wastes contain large amounts of nutrients. Their use for plant fertilization allows for diversifying and increasing the economic efficiency of farming activities. However, to avoid regional production surpluses, processing technologies allowing the acquisition of products that can be transported over long distances are required. This study therefore aimed at determining the effect of applied methods of DG treatment on the chemical composition of the resulting products and their effect on the yields and chemical composition of plants. The following digestate-based products (DGBPs) were tested: two different digestates (DGs), their liquid (LF) and solid fractions (SF) and pellets from DGs (PDG), and pellets form SFs (PSF). Results from the experiment show that during SF/LF separation of DGs, >80% of nitrogen and 87% of potassium flows to LFs, whereas >60% of phosphorus and 70% of magnesium flows to SFs. The highest yields were obtained using untreated DGs and LFs. The application of DGs and LFs was not associated with a leaching of nutrients to the environment (apparent nutrients recovery from these products exceeded 100%). Pelletized DG and SF forms can be used as slow-release fertilizer, although their production leads to significant nitrogen losses (>95%) by ammonia volatilization

Aggregation of soil and climate input data can underestimate simulated biomass loss and nitrate leaching under climate change

Predicting areas of severe biomass loss and increased N leaching risk under climate change is critical for applying appropriate adaptation measures to support more sustainable agricultural systems. The frequency of annual severe biomass loss for winter wheat and its coincidence with an increase in N leaching in a temperate region in Germany was estimated including the error from using soil and climate input data at coarser spatial scales, using the soil-crop model CoupModel. We ran the model for a reference period (1980–2010) and used climate data predicted by four climate model(s) for the Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5. The annual median biomass estimations showed that for the period 2070–2100, under the RCP8.5 scenario, the entire region would suffer from severe biomass loss almost every year. Annual incidence of severe biomass loss and increased N leaching was predicted to increase from RCP4.5 to the 8.5 scenario. During 2070–2100 for RCP8.5, in more than half of the years an area of 95% of the region was projected to suffer from both severe biomass loss and increased N leaching. The SPEI3 predicted a range of 32 (P3 RCP4.5) to 55% (P3 RCP8.5) of the severe biomass loss episodes simulated in the climate change scenarios. The simulations predicted more severe biomass losses than by the SPEI index which indicates that soil water deficits are important in determining crop losses in future climate scenarios. There was a risk of overestimating the area where “no severe biomass loss + increased N leaching” occurred when using coarser aggregated input data. In contrast, underestimation of situations where “severe biomass loss + increased N leaching” occurred when using coarser aggregated input data. Larger annual differences in biomass estimations compared to the finest resolution of input data occurred when aggregating climate input data rather than soil data. The differences were even larger when aggregating both soil and climate input data. In half of the region, biomass could be erroneously estimated in a single year by more than 40% if using soil and climate coarser input data. The results suggest that a higher spatial resolution of especially climate input data would be needed to predict reliably annual estimates of severe biomass loss and N leaching under climate change scenarios.Peer reviewe

Carbon Storage Potential and Carbon Dioxide Emissions from Mineral-Fertilized and Manured Soil

Two important goals of sustainable agriculture are food production and preserving and improving soil health. The soil organic carbon content is considered an indicator of soil health. The evaluation of the methods to increase the soil organic carbon content in long-term experiments is usually carried out without considering its environmental effects, (e.g., CO2–C soil emission). This study hypothesized that sandy soils have a low carbon storage potential, and that the carbon accumulation in the soil is accompanied by increased CO2–C emissions into the atmosphere. The study was carried out as a long-term fertilization experiment in Central Poland using a rye monoculture. The changes in the soil organic carbon content (SOC), CO2–C emissions from soil, and plant yields were examined for two soil treatments: one treated only with mineral fertilizers (CaNPK) and one annually fertilized with manure (Ca + M). Over the 91 years of the experiment, the SOC content of the manure-fertilized treatment increased almost two-fold, reaching 10.625 g C kg−1 in the topsoil, while the content of the SOC in the soil fertilized with CaNPK did not change (5.685 g C kg−1 in the topsoil). Unlike mineral fertilization, soil manuring reduced the plant yields by approximately 15.5–28.3% and increased the CO2–C emissions from arable land. The CO2–C emissions of the manured soil (5365.0 and 5159.2 kg CO2–C ha−1 in the first and second year of the study, respectively) were significantly higher (by 1431.9–2174.2 kg CO2–C ha−1) than those in the soils that only received mineral fertilizers (3933.1 and 2975.0 kg CO2–C ha−1 in the first and second year of the study, respectively). The results from this experiment suggest that only long-term fertilization with manure might increase the carbon storage in the sandy soil, but it is also associated with higher CO2–C emissions into the atmosphere. The replacement of mineral fertilizers with manure, predicted as a result of rising mineral fertilizer prices, will make it challenging to achieve the ambitious European goal of carbon neutrality in agriculture. The increase in CO2–C emissions due to manure fertilization of loamy sand soil in Central Poland also suggests the need to research the emissivity of organic farming

Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed

Impact of in-field soil heterogeneity on biomass and yield of winter triticale in an intensively cropped hummocky landscape under temperate climate conditions

Crop cultivation provides ecosystem services on increasingly large fields. However, the effects of in-field spatial heterogeneity on crop yields, in particular triticale, have rarely been considered. The study assess the effects of in-field soil heterogeneity and elevation on triticale grown in an intensively cropped hummocky landscape. The field was classified into three soil classes: C1, C2, and C3, based on soil texture and available water capacity (AWC), which had high, moderate, and low yield potential, respectively. Three elevations (downslope (DS), midslope (MS), and upslope (US)) were considered as the second study factor. An unbalanced experimental design was adopted with a factorial analysis of variance for data analysis. Temporal growth analysis showed that soil classes and elevation had significant effects. Generally, better growth was observed in C1 compared to that of C3. DS had a lower yield potential than that of MS and US. In addition, the interactive effect was confirmed, as triticale had poor growth and yield in C3 on the DS, but not on US. Crop physiological parameters also confirmed the differences between soil classes and elevation. Similarly, soil moisture (SM) content in the plow layer measured at different points in time and AWC over the soil profile had a positive association with growth and yield. The results confirmed that spatial differences in AWC and SM can explain spatial variability in growth and yield. The mapping approach combining soil auguring techniques with a digital elevation model could be used to subdivide fields in hummocky landscapes for determining sub-field input intensities to guide precision farming.Peer reviewe

Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed

Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits-A Case Study for Winter Wheat

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R-2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R-2: 0.75 to 0.84). These results indicate the imaging system's potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.Peer reviewe