Recently Greenhouse Gas emissions account to major concern owning its beings a potential cause of Global warning. There are various sources / sinks for GHG emissions be it due to use of energy consumption or practices of agriculture methods or other anthropogenic sources. GHG emissions are one the significant contribution of agriculture, though it depends on the sector. The major gases produced by agriculture include Methane and nitrous oxide. The concern for such releases is being the potential to cause greater greenhouse effect compared to carbon dioxide alone. For example a kilogram of methane has almost 25 times the global warming effect as of carbon dioxide, similarly the Nitrous Oxide accounts to almost 298 times. The major source of such emissions from agriculture include the usage of microbiological processing of nitrogenous fertilizers in the soil. Some even gets released from the digestive processes of farm animals.
One of the factors or difficulties in assessing the complete inventories / emissions of greenhouse gases include limited availability of data and number of variables of the data which is historically collected or calculated, the running or models or accessiblity of computing resources or data to research community. For example if you intend to analyze how farm practices like agriculture practices at micro level impact the greenhouse gas emissions along with landuse / land cover mapping or use of fertilizer type and use per acreage, it would be difficult to get the details. It is not that there are insufficient efforts are invested to compile such informaiton, there have been many research papers or modeling communities which work on GHG emissions and their impact, however it can achieve greater benefit by using the advances made in commerical IT systems and software tools .
Many ERP systems have emission Monitoring or GHG calculation modules which help in calculating sector specific GHG emissions. These contain details emission factor database like AP11 emission factors for calculation of emissions. These factors along with energy consumption details can be applied to calculate the emissions. These ERP tools however are used at industry level, similar tools can be created for calculation of GHG emisisons based on farm practices, though it would be seggregated limited area, but such calculation at the industry who is selling the agriculture practices would be helpful. Like calcualtion of each product level GHG emission potential and the country to which it is reaching, such details will help in formulating the GHG management appraoches.
Real time GHG emission calculation and analysis using advanced data analysis tools will also help a lot in GHG management. Real time streamining of data from multiple enterprise systems or inventory applications in a consolidated way and their web deployment will benefit. Such collected data with advanced analytics capability along with Machine Learning / exploratory data anlaysis integration will help a lot for any GHG management practices. Tools like Python and Django for web deployment and real time analysis of emissions would be great option besides many others.
There are many climate tools and Enegy simulation models which rely heavily on availability of disk space and processing power to run. Besies these requirements many times the compilers or software librairies which are used are not compatiable with the hardware the team is using and require changes in the code. Timely support of industries / software organizations to research communities or consortia working on the modeling will help a lot.
Besides that options for cloud deployment of these advanced numerical weather or emissions modeling softwares will help rapidly implementing the modeling and processing huge geosptial data, emission factors calcualtion or prediction of possible emissions potential for agriculture products. There are already cloud support is available for some of the numerical models like Weather Research Forecasting tools for mesoscale weather predictions and efforts are underway to pull massive computing power from crowdpulling.
There is a long way to go to curb the GHG emissions, but tight colloboration of industry bodies, Software organizations along with research / industry community will help properly managing the GHG emissions. The Kyoto protocol and carbon credit management drove some level of economic value in GHG management projects but the increased level of consiousness among industry bodies, government agencies, and general public along with IT will help managing the GHG in a far better manner. I still remember the challenges we faced during my post doctoral research in setting up the Weather modeling tools (WRF & CMAQ) in cluster environment. We run into issues for want of disk space, computing power, compiler compatiability and timely hardware delivery, it could have saved a lot of our efforts with the model we have today !