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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 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

Li et al nitrification inhibition review


Li et al nitrification inhibition review.This dataset is associated with the following publication: Kapoor, V., X. Li, C. Impellitteri , K. Chandran, and J. Santodomingo. Use of Functional Gene Expression and Respirometry to Study Wastewater Nitrification Activity after Exposure to Low Doses of Copper. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH. Ecomed Verlagsgesellschaft AG, Landsberg, GERMANY, 23(7): 6443-6450, (2016).

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Cost modeling data


The associated excel files hold the cost predictions for nitrate and perchlorate treatment based on a series of assumptions outlined in the paper. No experimental data was generated in this project.This dataset is associated with the following publication: Latham , M. SSWR FY14 Output Summary Report: Performance information and design tools are developed for innovative technologies and approaches for Small Drinking Water and Wastewater Systems. U.S. Environmental Protection Agency, Washington, DC, USA.

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Data corresponding to graphs in paper.This dataset is associated with the following publication: Oster, C., M. Kaminski, J. Jerden, and Y. Franchini. Evaluating Solid Sorbents for Recycling Wash Waters Containing Strontium and Calcium. Journal of Hazardous, Toxic, and Radioactive Waste. American Society of Civil Engineers (ASCE), Reston, VA, USA, 23(1): ., (2019).

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Arsenic speciation results


The dataset is a table that shows soil samples with corresponding total arsenic concentrations, arsenic bioavailability values, and linear combination fitting results of synchrotron speciation results.This dataset is associated with the following publication: Stevens, B.N., A.R. Betts, B.W. Miller, K.G. Scheckel, R.H. Anderson, K.D. Bradham, S.W. Casteel, D.J. Thomas, and N.T. Basta. Arsenic Speciation of Contaminated Soils/Solid Wastes and Relative Oral Bioavailability in Swine and Mice. Soil Systems. MDPI AG, Basel, SWITZERLAND, 2(2): 27, (2018).

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Life cycle inventory data of various unit processes in water and wastewater treatment trains and the life cycle impact assessments of different environmental performance categories.


LCI and LCIA for water and wastewater treatment plants.This dataset is associated with the following publications: Xue, X., S. Cashman, A. Gaglione, J. Mosley, L. Weiss, C. Ma, J. Cashdollar, and J. Garland. Holistic Analysis of Urban Water Systems in the Greater Cincinnati Region: (1) Life Cycle Assessment and Cost Implications. Water Research X. Elsevier B.V., Amsterdam, NETHERLANDS, 2: 100015, (2019). Cashman, S., A. Gaglione, J. Mosley, L. Weiss, T. Hawkins, N. Ashbolt, J. Cashdollar , X. Xue, C. Ma , and S. Arden. Environmental and cost life cycle assessment of disinfection options for municipal drinking water treatment. U.S. Environmental Protection Agency, Washington, DC, USA, 2014. Cashman, S., A. Gaglione, J. Mosley, L. Weiss, N. Ashbolt, T. Hawkins, J. Cashdollar , X. Xue, C. Ma

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