Modeling atmospheric ammonia using agricultural emissions with improved spatial variability and temporal dynamics

Ammonia emissions into the atmosphere have increased substantially in Europe since 1960, primarily due to the intensification of agriculture, as illustrated by enhanced livestock and the use of fertilizers. These associated emissions of reactive nitrogen, particulate matter, and acid deposition have contributed to negative societal impacts on human health and terrestrial ecosystems. Due to the limited availability of reliable measurements, emission inventories are used to assess large-scale ammonia emissions from agriculture by creating gridded annual emission maps and emission time profiles globally and regionally. The modeled emissions are subsequently utilized in chemistry transport models to obtain ammonia concentrations and depositions. However, current emission inventories usually have relatively low spatial resolutions and coarse categorizations that do not distinguish between fertilization on various crops, grazing, animal housing, and manure storage in its spatial allocation. Furthermore, in assessing the seasonal variation of ammonia emissions, they do not consider local climatology and agricultural management, which limits the capability to reproduce observed spatial and seasonal variations in the ammonia concentrations. This paper describes a novel ammonia emission model that quantifies agricultural emissions with improved spatial details and temporal dynamics in 2010 in Germany and Benelux. The spatial allocation was achieved by embedding the agricultural emission model Integrated Nitrogen Tool across Europe for Greenhouse gases and Ammonia Targeted to Operational Responses (INTEGRATOR) into the air pollution inventory Monitoring Atmospheric Composition and Climate-III (MACC-III), thus accounting for differentiation in ammonia emissions from manure and fertilizer application, grazing, animal houses and manure storage systems. The more detailed temporal distribution came from the integration of TIMELINES, which provided predictions of the timing of key agricultural operations, including the day of fertilization across Europe. The emission maps and time profiles were imported into LOTOS-EUROS to obtain surface concentrations and total columns for validation. The comparison of surface concentration between modeled output and in situ measurements illustrated that the updated model had been improved significantly with respect to the temporal variation of ammonia emission, and its performance was more stable and robust. The comparison of total columns between remote sensing observations and model simulations showed that some spatial characteristics were smoothened. Also, there was an overestimation in southern Germany and underestimation in northern Germany. The results suggested that updating ammonia emission fractions and accounting for manure transport are the direction for further improvement, and detailed land use is needed to increase the spatial resolution of spatial allocation in ammonia emission modeling.

176 Application of growing degree day to predict diet crude protein in rangeland beef cows

We used a fecal near infrared spectroscopy (FNIRS) calibration for cattle diet crude protein (CP) to evaluate the efficacy of growing degree day (GDD) as a remotely-sensed method to monitor grazing animal nutrition. Composite fecal samples representing a herd of 24 cross bred beef cows grazing native range pastures in southwest Texas were collected along with GDD and precipitation data from April 2018 to September 2019. Regression analyses were performed to determine relationships between FNIRS-predicted diet CP and GDD within year and growing season. In 2018, FNIRS-predicted diet CP ranged from a minimum of 7.05% in August to a maximum of 9.69% in July. 2018 cumulative precipitation was 28% and 94% of the 20-year average for January-April and May-August, respectively. In 2019, FNIRS-predicted diet CP ranged from a minimum of 6.85% in September to a maximum of 12.01% in May. 2019 cumulative precipitation was 74% and 102% of the 20-year average for January-April and May-August, respectively. There were no significant (P > 0.1) simple linear relationships identified between FNIRS-predicted diet CP and GDD. There were, however, cubic exponential relationships identified in both 2018 (y = 7E-10x3 - 5E-06x2 + 0.0106x + 2.9603; R² = 0.7261; P = 0.1271) and 2019 (y = 1E-09x3 - 6E-06x2 + 0.0062x + 9.2923; R² = 0.7659; P = 0.0493). As expected, perennial range grass phenology/nutritive value (i.e. cattle diet CP) was influenced by accumulation of heat units (i.e. GDD) and precipitation. Although FNIRS is an established non-invasive method to monitor grazing animal nutrition, a remotely-sensed method to accomplish this task, such as GDD, has the potential to facilitate large-scale monitoring of grazing animal nutritional status. Our results indicate that complementary research using data from multiple locations and for more than 2 years is needed to fully evaluate these techniques.

Grassland utilization estimation method and system based on environmental sense

The utilization of natural grassland is an important part of grazing animal husbandry. Effective monitoring and accurate estimation of the utilization of natural grassland are inevitable requirements for the sustainable development of grazing animal husbandry. In order to estimate the utilization process of natural grassland effectively, this paper proposes a grassland utilization estimation method based on environmental sense. In this paper, the distribution of feed intake was obtained by the GPS trajectory data, and the distribution of grassland biomass was obtained by the satellite remote sensing estimation model of natural grassland. The feed intake and grassland biomass are classified and compared respectively. According to the value of the two, the utilization of grassland in each region was obtained. And this study designed a grassland utilization estimating system based on this estimating method and 3S technology. The results show that this method is of great significance to the rational utilization of grassland and the implementation of rotational grazing. The system can provide decision-making basis for ranchers and grassland livestock management departments to manage grazing and grassland. It enables ranchers to make reasonable and effective grazing plans, so as to make balanced utilization of grassland resources and promote the sustainable development of grazing animal husbandry.

Modeling Atmospheric Ammonia using Agricultural Emissions with Improved Spatial Variability and Temporal Dynamics

Ammonia emissions to the atmosphere have increased substantially in Europe since 1960, largely due to the intensification of agriculture as illustrated by enhanced livestock and increasing use of fertilizers. These associated emissions of reactive nitrogen, particulate matter and acid deposition have contributed to negative societal impacts on human health and terrestrial ecosystems. Due to the limited availability of measurements, emission inventories are used to assess large-scale ammonia emissions from agriculture, creating gridded annual emission maps as well as emission time profiles, both globally and regionally. The modeled emissions are in turn used in chemistry transport models to obtain ammonia concentrations and depositions. However, current emission inventories usually have relatively low spatial resolutions and coarse categorizations that do not distinguish between fertilization on various crops, grazing, animal housing, and manure storage in its spatial allocation. Furthermore, in assessing the seasonal variation of ammonia emissions, they do not take into account local climatology and agricultural management, which limits the capability to reproduce observed spatial and seasonal variations in the ammonia concentrations. This paper describes a novel ammonia emission model that quantifies agricultural emissions with improved spatial details and temporal dynamics over the year of 2010, in Germany and Benelux. The spatial allocation was achieved by embedding the agricultural emission model INTEGRATOR into MACC-III, thus accounting for differences in manure and fertilizer application on croplands and grassland, grazing, animal houses and manure storage systems. The more detailed temporal distribution comes from the integration of the TIMELINES model, which provided predictions of the timing of key agricultural operations including the day of fertilization across Europe. The emission estimates and time profiles were imported into LOTOS-EUROS to obtain surface concentrations and total columns for validation. The comparison of surface concentration time series between modeled output and in-situ measurements illustrated that the updated model has been improved significantly with respect to the temporal variation of ammonia emission, and its performance was more stable and robust. The comparison between ammonia total columns from remote sensing and simulations showed that there is an overestimation in Southern Germany and underestimation in Northern Germany, which suggested that updating ammonia emission fractions and accounting for manure transport is the direction for further improvement.

Grassland utilization estimation method and system based on environmental sense

The utilization of natural grassland is an important part of grazing animal husbandry. Effective monitoring and accurate estimation of the utilization of natural grassland is an inevitable requirement for the sustainable development of grazing animal husbandry. In order to estimate the utilization process of natural grassland effectively, this paper proposes a grassland utilization estimation method based on environmental sense. In this paper, the distribution of feed intake was obtained by the GPS track data, and the distribution of grassland biomass was obtained by the satellite remote sensing estimation model of natural grassland. The feed intake and grassland biomass are classified and compared respectively. According to the value of the two, the utilization of grassland in each region was obtained. And this study designed a grassland utilization estimating system based on this estimating method and 3S technology. The results show that this method is of great significance to the rational utilization of grassland and the implementation of rotational grazing. The system can provide decision-making basis for ranchers and grassland livestock management departments to manage grazing and grassland. It enables ranchers to make reasonable and effective grazing plans, so as to make balanced utilization of grassland resources and promote the sustainable development of grazing animal husbandry.

Preparatory studies for modelling production on protected grasslands

There is a mutual dependence between nature conservation activity and agriculture in Hungary, as the management of the protected areas cannot be achieved without ecological farming methods. Moreover, viable economic activity can be only imagined through the harmonization of agricultural and nature conservation interests. From a nature conservation point of view, grass management systems play the greatest role in domestic agricultural systems. Yet, due to the prohibition of certain management methods and the spatial and temporal restraints on grazing, nature conservation activities have priority on protected grasslands. While nature conservation activity is still of prominent importance, it is not equally suitable for the economical management of protected grasslands per se. With our examinations, we would like to emphasize the common interests of these mutually dependent activities and to promote bilateral cooperation. Our aim is to model the production of grass on the great pastureland of Hajdúbagos. Potential grass production levels are easily calculable with a computer model based on data collected through a series of test harvests, as well as by factoring in changing climatic factors and by simulating the effects of grazing animal species and stocks. This model is not only useful for determining the optimal number of the grazing animal stock and grazing method, and therefore the most suitable management strategy, but it also supports local farmers to be able to plan their activities. In this way, both nature conservation and economic aims can be easily harmonized, which would be an important factor for the sustainable development of rural areas.