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      The efficacy of conservation practices in reducing floods and improving water quality

      , , ,
      Frontiers in Environmental Science
      Frontiers Media SA

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          Abstract

          Conservation practices such as crop rotation, filter strips, and constructed wetlands are nature-based approaches intended to safeguard natural resources in agricultural landscapes. In this study, we reviewed the literature on how conservation practices, both at watershed and field scales, have been proven to subdue flood peaks, surface runoff, soil erosion, sediment transport, and nutrient loss. We classified different conservation practices based on the mode of their application (i.e., in-field, edge-of-field, and structural practices) and described what prior research efforts have concluded about the efficacy of different practices. At the field scale, practices such as reduced or no-till farming, grassed waterways, and creation of wetlands significantly reduced the peak flow. Similarly, water quality was improved with implementation of conservation practices such as using cover crops, filter strips, and managing residue and tillage. The assessment of conservation practices across the literature was found to be challenging as different conservation practices showed a similar response, thus making it complex to assess the individual effect. A wide range of challenges related to the data, modeling/analysis, and management aspects of conservation practices were identified, and recommendations were provided to overcome these challenges.

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          Machine Learning in Agriculture: A Review

          Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.
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            The role of conservation agriculture in sustainable agriculture.

            The paper focuses on conservation agriculture (CA), defined as minimal soil disturbance (no-till, NT) and permanent soil cover (mulch) combined with rotations, as a more sustainable cultivation system for the future. Cultivation and tillage play an important role in agriculture. The benefits of tillage in agriculture are explored before introducing conservation tillage (CT), a practice that was borne out of the American dust bowl of the 1930s. The paper then describes the benefits of CA, a suggested improvement on CT, where NT, mulch and rotations significantly improve soil properties and other biotic factors. The paper concludes that CA is a more sustainable and environmentally friendly management system for cultivating crops. Case studies from the rice-wheat areas of the Indo-Gangetic Plains of South Asia and the irrigated maize-wheat systems of Northwest Mexico are used to describe how CA practices have been used in these two environments to raise production sustainably and profitably. Benefits in terms of greenhouse gas emissions and their effect on global warming are also discussed. The paper concludes that agriculture in the next decade will have to sustainably produce more food from less land through more efficient use of natural resources and with minimal impact on the environment in order to meet growing population demands. Promoting and adopting CA management systems can help meet this goal.
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              Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions.

              In 2011, Lake Erie experienced the largest harmful algal bloom in its recorded history, with a peak intensity over three times greater than any previously observed bloom. Here we show that long-term trends in agricultural practices are consistent with increasing phosphorus loading to the western basin of the lake, and that these trends, coupled with meteorological conditions in spring 2011, produced record-breaking nutrient loads. An extended period of weak lake circulation then led to abnormally long residence times that incubated the bloom, and warm and quiescent conditions after bloom onset allowed algae to remain near the top of the water column and prevented flushing of nutrients from the system. We further find that all of these factors are consistent with expected future conditions. If a scientifically guided management plan to mitigate these impacts is not implemented, we can therefore expect this bloom to be a harbinger of future blooms in Lake Erie.
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                Author and article information

                Journal
                Frontiers in Environmental Science
                Front. Environ. Sci.
                Frontiers Media SA
                2296-665X
                May 15 2023
                May 15 2023
                : 11
                Article
                10.3389/fenvs.2023.1136989
                98f3e540-b8be-4547-8f69-e6c8f0ca065e
                © 2023

                Free to read

                https://creativecommons.org/licenses/by/4.0/

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