Towards sustainable maize production in the U.S. upper Midwest with interseeded cover crops.

The incorporation of cover crops into the maize (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotation in the U.S. upper Midwest may improve sustainability. Long, cold winters in the region make identifying successful cover crop species and management practices a challenge. Two experiments were conducted in Minnesota, USA from fall 2016 through spring 2019 to examine the effect of cover crops interseeded at four- to six-leaf collar (early-interseeded) and dent to physiological maturity (late-interseeded) on biomass and grain yield of maize. Annual ryegrass (Lolium multiflorum L.) and cereal rye (Secale cereale L.) were evaluated as monocultures and in mixtures with crimson clover (Trifolium incarnatum L.) and forage radish (Raphanus sativus L.). Differences in canopy cover and biomass of late-interseeded cover crops were observed at the southernmost location in 2018. Additional accumulated growing-degree days in fall 2018 did not translate into increased cover crop canopy coverage of late-interseeded cover crops. Differences in cover crop canopy cover and biomass of early-interseeded cover crops were observed by fall frost at all locations in 2017 and at the northernmost location in 2018. Cover crop canopy cover and biomass at termination before planting maize, soil moisture at maize planting as well as maize aboveground biomass and yield were not affected by spring cereal rye regrowth of cover crops late-interseeded the previous year. Similarly, early-interseeded cover crops did not affect maize aboveground biomass or yield. We attribute these results to limited cover crop growth. This highlights the potential of a variety of cover crop strategies interseeded into maize in the U.S. upper Midwest; however, efforts to fine-tuning cover crop management and weather conditions are needed to benefit from such practice.

Perennial Grain and Oilseed Crops.

Historically, agroecosystems have been designed to produce food. Modern societies now demand more from food systems-not only food, fuel, and fiber, but also a variety of ecosystem services. And although today's farming practices are producing unprecedented yields, they are also contributing to ecosystem problems such as soil erosion, greenhouse gas emissions, and water pollution. This review highlights the potential benefits of perennial grains and oilseeds and discusses recent progress in their development. Because of perennials' extended growing season and deep root systems, they may require less fertilizer, help prevent runoff, and be more drought tolerant than annuals. Their production is expected to reduce tillage, which could positively affect biodiversity. End-use possibilities involve food, feed, fuel, and nonfood bioproducts. Fostering multidisciplinary collaborations will be essential for the successful integration of perennials into commercial cropping and food-processing systems.

Fundamentals of synchronization in chaotic systems, concepts, and applications.

The field of chaotic synchronization has grown considerably since its advent in 1990. Several subdisciplines and "cottage industries" have emerged that have taken on bona fide lives of their own. Our purpose in this paper is to collect results from these various areas in a review article format with a tutorial emphasis. Fundamentals of chaotic synchronization are reviewed first with emphases on the geometry of synchronization and stability criteria. Several widely used coupling configurations are examined and, when available, experimental demonstrations of their success (generally with chaotic circuit systems) are described. Particular focus is given to the recent notion of synchronous substitution-a method to synchronize chaotic systems using a larger class of scalar chaotic coupling signals than previously thought possible. Connections between this technique and well-known control theory results are also outlined. Extensions of the technique are presented that allow so-called hyperchaotic systems (systems with more than one positive Lyapunov exponent) to be synchronized. Several proposals for "secure" communication schemes have been advanced; major ones are reviewed and their strengths and weaknesses are touched upon. Arrays of coupled chaotic systems have received a great deal of attention lately and have spawned a host of interesting and, in some cases, counterintuitive phenomena including bursting above synchronization thresholds, destabilizing transitions as coupling increases (short-wavelength bifurcations), and riddled basins. In addition, a general mathematical framework for analyzing the stability of arrays with arbitrary coupling configurations is outlined. Finally, the topic of generalized synchronization is discussed, along with data analysis techniques that can be used to decide whether two systems satisfy the mathematical requirements of generalized synchronization. (c) 1997 American Institute of Physics.

Coregionalized single- and multiresolution spatially varying growth curve modeling with application to weed growth.

Modeling of longitudinal data from agricultural experiments using growth curves helps understand conditions conducive or unconducive to crop growth. Recent advances in Geographical Information Systems (GIS) now allow geocoding of agricultural data that help understand spatial patterns. A particularly common problem is capturing spatial variation in growth patterns over the entire experimental domain. Statistical modeling in these settings can be challenging because agricultural designs are often spatially replicated, with arrays of subplots, and interest lies in capturing spatial variation at possibly different resolutions. In this article, we develop a framework for modeling spatially varying growth curves as Gaussian processes that capture associations at single and multiple resolutions. We provide Bayesian hierarchical models for this setting, where flexible parameterization enables spatial estimation and prediction of growth curves. We illustrate using data from weed growth experiments conducted in Waseca, Minnesota, that recorded growth of the weed Setaria spp. in a spatially replicated design.