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Agricultural Waste Management System

What Is Agricultural Waste Management?

From crop or livestock production to the consumer, agricultural waste is generated. However, a good agricultural waste management system enables people to prevent, reduce, or reuse this waste.

Why Is It Important to Have a Good Agricultural Waste Management System?

Wherever there is waste, there is missed opportunity to increase food production, decrease hunger, or create fuel.

The first and second steps in a good agricultural waste management system—prevention and reduction—lead to greater food supply. This in turn decreases poverty and hunger.

The final step, reuse, involves taking existing agricultural waste and converting it. Alternative safe uses of waste include fertilizer, biofuel, heavy metal adsorption, and animal feed.

Finally, inefficient animal waste and supply chain management increases pollution and greenhouse gas emissions. Therefore, agricultural waste management is crucial for conservation and environmental sustainability.

What Internal Data Should I Have for a Good Agricultural Waste Management System?

Internal data to incorporate into your waste management system depends on your organization. As a farmer, for example, you have the greatest amount of internal data needs. First, of course, you need detailed information on the lifecycle of your crops and the diseases and animals that prey on them. Second are economic and legal data that impacts what you can sell and where. Finally, if you have the capability, a data feed of live updates from your fields.

Similarly, a supply-chain operator will need data on the type of crop or livestock they transport. Ideally, they will also have vehicles equipped with sensors that monitor the cargo to ensure a stable temperature and humidity level.

If, however, you are on the consumer end of the supply chain—for example, if you are a grocer or restaurateur—you will only need data on your own company or customer waste and transactions.

What External Data Is Essential for a Good System?

External data that you must have for your agricultural waste management system again depends on your role in the food supply chain. Farmers and supply chain operators need to know weather conditions more than grocers, for example.

However, everyone needs to incorporate agricultural yields and economic factors into their system.

What External Data May Prove Useful for a Good System?

Other external data that ought to be incorporated into a good agricultural waste management system includes trade regulations or market trends. Additionally, it would be good to stay abreast of new research studies in the field, insofar as they impact your work.

What Are the Main Challenges of this Use Case?

The main problem with this use case is that it is predictive: You can take in all the data available in the world to predict and mitigate crop waste or food waste but still fail. There may be uncharacteristically heavy rainfall, drowning crops. There may be an outbreak of mad cow disease. Or, there may be an outbreak of a pandemic that forces supply chains to cease functioning, leaving good food to rot.

Interesting Case Studies and Blogs to Look Into

Science Direct: Agricultural waste: Review of the evolution, approaches and perspectives on alternative uses
ResearchGate: AGRICULTURAL WASTE CONCEPT, GENERATION, UTILIZATION AND MANAGEMENT
ResearchGate: Agricultural Waste Management: Case Study of a Waste Treatment Plant for Animal Manure

Tangible Examples of Impact

Agrismart – Managing Cultivation … is an integrated IoT (Internet of Things) system that contains several sensors, all in one smart box, to collect and measure data of extreme interest for farms.

“A wide feedback has arrived from the use of the system in vineyards, in particular in the Apulian territory, but it is also used for citrus groves and orchards. Depending on the crop, there are different factors involved in monitoring. As for the vine, we are at an advanced stage and have developed specific algorithms for diseases such as botrytis, powdery mildew and downy mildew. For other crops, we are currently working on it . We must also bear in mind the climatic conditions of the reference area, with ad hoc algorithms for this case.”

Hortidaily: Agrismart, the IoT smart box dedicated to agriculture 4.0

Connected Datasets

Dataset from “Regional Ozone-Temperature Relationships Across the U.S. Under Multiple Climate and Emissions Scenarios”, by Nolte et al.

This file describes the dataset used in the following article:Nolte, C. G., Spero, T. L., Bowden, J. H., Sarofim, M. C., Martinich, J., Mallard, M. S., Fann, N., “Regional Temperature-Ozone Relationships Across the U.S. Under Multiple Climate and Emissions Scenarios,” 2020.MODEL VERSION AND CONFIGURATIONThe Community Multiscale Air Quality (CMAQ) model was used. The model is open source and can be freely downloaded at http://github.com/USEPA/CMAQ. The specific code version used in this study was based on a pre-release version of CMAQ 5.3, with minor modifications to accommodate the USGS28 land-use scheme used in WRF. The model source code is included in the “src” directory.The meteorological input data for CMAQ were derived from outputs of the Community Earth System Model (CESM) and the Coupled Model version 3

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Datasets for manuscript: Valuing Economic Impact Reductions of Nutrient Pollution from Livestock Waste

https://github.com/zavalab/JuliaBox/tree/master/HiddenImpacts This folder provides supporting codes for the paper “Valuing Economic Impact Reductions of Nutrient Pollution from Livestock Waste”. * The folder “sensitivity_analysis” contains the code and data files for different values of the economic impact/value of service (vos). * The folder “GIS_data” contains the code and data files used to generate the maps of the Upper Yahara watershed region presented in the paper. * In each case, we have three Julia scripts: “market_model.jl”, “market_Run.jl”, and “market_print.jl”. One should run “market_Run.jl” first, this script will automatically read the “market_model.jl” script, establish the model, and solve the model. Then, the “market_print.jl” should be run in order to print out all the result files. * If a sensitivity analysis on the VOS needs to be conducted (similar

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Dataset from “Regional Ozone-Temperature Relationships Across the U.S. Under Multiple Climate and Emissions Scenarios”, by Nolte et al.

This file describes the dataset used in the following article:Nolte, C. G., Spero, T. L., Bowden, J. H., Sarofim, M. C., Martinich, J., Mallard, M. S., Fann, N., “Regional Temperature-Ozone Relationships Across the U.S. Under Multiple Climate and Emissions Scenarios,” 2020.MODEL VERSION AND CONFIGURATIONThe Community Multiscale Air Quality (CMAQ) model was used. The model is open source and can be freely downloaded at http://github.com/USEPA/CMAQ. The specific code version used in this study was based on a pre-release version of CMAQ 5.3, with minor modifications to accommodate the USGS28 land-use scheme used in WRF. The model source code is included in the “src” directory.The meteorological input data for CMAQ were derived from outputs of the Community Earth System Model (CESM) and the Coupled Model version 3

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Cesium Emissions from Laboratory Fires

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The data sets contain the raw and reduced data from the instrument measurements including continuous emission monitoring and stack sampling procedure.This dataset is associated with the following publication: Hao, W.M., S. Baker, E. Lincoln, S. Hudson, S. Lee, and P. Lemieux. Cesium Emissions from Laboratory Fires Article. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION. Air & Waste Management Association, Pittsburgh, PA, USA, 49, (2018).

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Inhibitory effect of cyanide on wastewater nitrification determined using SOUR and RNA-based gene-specific assays

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SOUR and qPCR data.This dataset is associated with the following publication: Kapoor, V., M. Elk, and X. Li. Inhibitory effect of cyanide on wastewater nitrification determined using SOUR and RNA-based gene-specific assays. Letters in Applied Microbiology. Blackwell Publishing, Malden, MA, USA, 63(2): 155-161, (2016).

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