AGRICULTURAL BEST MANAGEMENT PRACTICE SENSITIVITY TO CHANGING AIR TEMPERATURE AND PRECIPITATION

Towards implementing precision conservation practices in agricultural watersheds: A review of the use and prospects of spatial decision support systems and tools

Agricultural nonpoint source (NPS) pollution leads to water quality degradation. While agriculture is faced with the challenge of feeding a growing population in a changing climate, farmers must also strive to minimize adverse impacts of agriculture on the environment. As a result, policies, and agri-environmental programs to promote agricultural conservation practices for controlling NPS pollution have been emerging. Despite progress, reducing NPS is a complex challenge that requires ongoing innovation and investment. A major challenge is to achieve an optimal spatial trade-off between the economic costs and positive environmental outcomes of conservation practices on complex agricultural landscapes. Geospatial systems and tools can help to address this challenge and enhance the effectiveness and efficiency of conservation efforts. However, using these tools for precision conservation is underexamined. This review paper aims to address this gap through a critical exploration of spatial decision support systems and tools to provide synthesized knowledge for implementing precision conservation practices. This paper proposes a conceptual framework to guide the implementation of precision conservation and identifies areas for further development of geospatial systems and tools on planning and assessment of precision conservation efforts. All of which will be helpful for decision-makers and watershed managers in determining the most effective approaches for precision conservation. Furthermore, this review highlights the need for further research and development towards establishing an integrated spatial decision support system framework, which can improve socio-economic, environmental, and ecological outcomes.

A review of climate change effects on practices for mitigating water quality impacts

Water quality practices are commonly implemented to reduce human impacts on land and water resources. In series or parallel in a landscape, systems of practices can reduce local and downstream pollution delivery. Many practices function via physical, chemical, and biological processes that are dependent on weather and climate. Climate change will alter the function of many such systems, though effects will vary in different hydroclimatic and watershed settings. Reducing the risk of impacts will require risk-based, adaptive planning. Here, we review the literature addressing climate change effects on practices commonly used to mitigate the water quality impacts of urban stormwater, agriculture, and forestry. Information from the general literature review is used to make qualitative inferences about the resilience of different types of practices. We discuss resilience in the context of two factors: the sensitivity of practice function to changes in climatic drivers, and the adaptability, or relative ease with which a practice can be modified as change occurs. While only a first step in addressing a complex topic, our aim is to help communities incorporate consideration of resilience to climate change as an additional factor in decisions about water quality practices to meet long-term goals.

Drainage N Loads Under Climate Change with Winter Rye Cover Crop in a Northern Mississippi River Basin Corn-Soybean Rotation

To help reduce future N loads entering the Gulf of Mexico from the Mississippi River 45%, Iowa set the goal of reducing non-point source N loads 41%. Studies show that implementing winter rye cover crops into agricultural systems reduces N loads from subsurface drainage, but its effectiveness in the Mississippi River Basin under expected climate change is uncertain. We used the field-tested Root Zone Water Quality Model (RZWQM) to estimate drainage N loads, crop yield, and rye growth in central Iowa corn-soybean rotations. RZWQM scenarios included baseline (BL) observed weather (1991–2011) and ambient CO2 with cover crop and no cover crop treatments (BL_CC and BL_NCC). Scenarios also included projected future temperature and precipitation change (2065–2085) from six general circulation models (GCMs) and elevated CO2 with cover crop and no cover crop treatments (CC and NCC). Average annual drainage N loads under NCC, BL_NCC, CC and BL_CC were 63.6, 47.5, 17.0, and 18.9 kg N ha−1. Winter rye cover crop was more effective at reducing drainage N losses under climate change than under baseline conditions (73 and 60% for future and baseline climate), mostly because the projected temperatures and atmospheric CO2 resulted in greater rye growth and crop N uptake. Annual CC drainage N loads were reduced compared with BL_NCC more than the targeted 41% for 18 to 20 years of the 21-year simulation, depending on the GCM. Under projected climate change, average annual simulated crop yield differences between scenarios with and without winter rye were approximately 0.1 Mg ha−1. These results suggest that implementing winter rye cover crop in a corn-soybean rotation effectively addresses the goal of drainage N load reduction under climate change in a northern Mississippi River Basin agricultural system without affecting cash crop production.