Using Nested Paddocks to Study Multiple-Paddock Grazing Systems

There is insufficient information to guide development of multiple paddock grazing systems. Measuring vegetation responses to grazing period/recovery period intervals is prohibitively expensive when using most grazing research designs. Nested paddock designs reduce land area, number of herds, and number of paddocks needed for comparisons. Nested paddocks permit comparisons of animal performance among whole pasture treatments but comparisons are limited for animal performance differences among grazing/recovery period lengths. Nonetheless, nested paddock designs efficiently document vegetation responses to grazing intervals, which may permit predictions of animal performance

AFTER FURTHER REVIEW: AN UPDATE ON MODELING AND DESIGN STRATEGIES FOR AGRICULTURAL DOSE-RESPONSE EXPERIMENTS

Research investigating dose-response relationships is common in agricultural science. This paper is an expansion on previous work by Guo, et al. (2006) motivated by plant nutrition research in horticulture. Plant response to level of nutrient applied is typically sigmoidal, i.e. no response at very low levels, observable response at mid-levels, point-of-diminishing returns and plateau at high levels. Plant scientists need accurate estimates of these response relationships for many reasons, including determining the lower threshold below which plants show deficiency symptoms and the point of diminishing returns, above which excessive doses are economically and environmentally costly. Guo et al. presented models and designs that address these requirements and a simulation study to assess and compare the small-sample behavior of these models and designs. This paper expands on that simulation study. In addition, a simulation study based procedure for exploring designs for experimental scenarios fitting this description is presented. This simulation study approach utilizes simulation based fit statistics in conjunction with various lack-of-fit plots to produce a design robust to multiple candidate models

A COMPARISON OF MODELS AND DESIGNS FOR EXPERIMENTS WITH NONLINEAR DOSE-RESPONSE RELATIONSHIPS

Research investigating dose-response relationship is common in agricultural science. Animal response to drug dose and plant response to amount of irrigation, pesticide, or fertilizer are familiar examples. This paper is motivated by plant nutrition research in horticulture. Plant response to level of nutrient applied is typically sigmoidal, i.e. no response at very low levels, observable response at mid-levels, point-of-diminishing returns and plateau at high levels. Plant scientists need accurate estimates of these response relationships 1) to determine lower threshold below which plants show deficiency symptoms and 2) to determine upper point-of-diminishing returns, above which excessive doses are economically and environmentally costly. Landes, at al. (1999 and Olson et al. (2001) did initial work identifying potentially useful models. Paparozzi, et al. (2005) investigated dose (micro- and macro-nutrient) response (elemental leaf and stem concentration) relationships in Poinsettia. They found that 1) nutrients must be considered as a system, hence multifactor experiments are essential, 2) resources are limited, meaning that experiments must use response-surface principles, and 3) nutrient-response relationships are rarely modeled adequately by 2nd order polynomial regression models, so standard response surface methods are inadequate. This paper presents models and designs that address these requirements and a simulation study to assess and compare the small-sample behavior of these models and designs

NONLINEAR MODELS FOR MULTI-FACTOR PLANT NUTRITION EXPERIMENTS

Plant scientists are interested in measuring plant response to quantitative treatment factors, e.g. amount of nutrient applied. Response surface methods are often used for experiments with multiple quantitative factors. However, in many plant nutrition studies, second-order response surface models result in unacceptable lack of fit. This paper explores multi-factor nonlinear models as an alternative. We have developed multi-factor extensions of Mitscherlich and Gompertz models, and fit them to data from experiments conducted at the University of Nebraska-Lincoln Horticulture department. These data are typical of experiments for which conventional response surface models perform poorly. We propose design selection strategies to facilitate economical multi-factor experiments when second-order response surface models are unlikely to fit

MODEL BUILDING IN MULTI-FACTOR PLANT NUTRITION EXPERIMENTS

Often, the goal of plant science experiments is to model plant response as a function of quantitative treatment factors, such as the amount of nutrient applied. As the number of factors increases, modeling the response becomes increasingly challenging, especially since the resources available for such experiments are usually severely limited. Typical methods of analysis, notably second-order response surface regression, often fail to accurately explain the data. Alternatives such as non-linear models and segmented regression have been used successfully with two-factor experiments (Landes, et. aI, 1999). This paper extends previous work to three-and-more factor experiments. Models are assessed to explain the relationship between the levels of nutrients applied and leaf, root, and shoot responses of Poinsettias from an experiment conducted by horticultural researchers at the University of Nebraska-Lincoln. These data illustrate problems that are representative of those that plant researchers typically face. Multiple regression using the Hoed function proved to be especially useful. These analyses suggest a feasible approach to design of experiments suitable for a wide variety of plant science applications with multiple factors and limited resources

Rodent-Agriculture Interactions in No-Tillage Crop Fields

Acreage in reduced- and no-tillage farming systems has increased markedly in recent years, a trend that is expected to continue. However, small rodent populations thrive in these fields and at times dig and consume newly planted seeds and seedlings. During 1983, no-tillage corn, wheat and grain sorghum fields in western (Red Willow Co.) and eastern (Saline and Jefferson Cos.) Nebraska were evaluated to determine the distribution and food habits of the rodent species present, the damage to crops, and the availability of alternate rodent food sources. During June (post-emergence) and August (maximum corn height), 676 rodents were captured in 11 corn fields, and during July, 105 rodents were captured in 2 wheat and 2 sorghum fields. Species captured included thirteen-lined ground squirrels (spermophilusilus tr decemlineatus), Ord\u27s kangaroo rats (Diopodomys ordii), deer mice (Peromysous m a niculatus), ndT-thern grasshopper mice (onychomys leucogaster), voles (Microtus spp.), hispid pocket mice (Pero nathus hispidus) western harvest mice (Reithrodontomys to megalotis), house mice (M= musculus and short-tailed shrews (Blarina bre i auda). Rodents were distributed throughout study fields although the sample size of several species was not great enough to determine patterns

USING PROC NLMIXED TO ANALYZE A TIME OF WEED REMOVAL STUDY

Many studies in weed science involve fitting a nonlinear model to experimental data. Examples of such studies include dose-response experiments and studies to determine the critical period of weed control. The experiments typically use block designs and often have additional complexity such as split-plot features. However, nonlinear models are typically fit using software such as SAS PROC NLIN that are limited to a single error term and whose ability to account for blocking is either awkward or lacking entirely. For example, Seefeldt et al. (1995) only proceeded in fitting the nonlinear model after establishing that the block effect was negligible. Issues such as multiple error terms in split-plot designs are simply not dealt with at all. In this paper, we examine a weed removal study carried out as a split-plot design with blocks and illustrate the use of SAS PROC NLMIXED to account for blocks and the two-level error structure

Influence of Energy Intake During Lactation on Subsequent Gestation, Lactation and Postweaning Performance of Sows

Forty-four second parity crossbred sows were used to determine (1) the effect of energy intake during their first lactation (Lac 1) on subsequent reproductive performance from re-breeding to farrowing and (2) the effect of energy intake during two successive lactations on performance during the second lactation (Lac 2) and post-weaning periods. Sows received 8 (Lo) or 16 (Hi) Meal of metabolizable energy (ME)/d during Lac 1 and 5.4 Mcal of ME/d during the subsequent gestation

Derivation, validation, and clinical relevance of a pediatric sepsis phenotype with persistent hypoxemia, encephalopathy, and shock

OBJECTIVES: Untangling the heterogeneity of sepsis in children and identifying clinically relevant phenotypes could lead to the development of targeted therapies. Our aim was to analyze the organ dysfunction trajectories of children with sepsis-associated multiple organ dysfunction syndrome (MODS) to identify reproducible and clinically relevant sepsis phenotypes and determine if they are associated with heterogeneity of treatment effect (HTE) to common therapies. DESIGN: Multicenter observational cohort study. SETTING: Thirteen PICUs in the United States. PATIENTS: Patients admitted with suspected infections to the PICU between 2012 and 2018. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We used subgraph-augmented nonnegative matrix factorization to identify candidate trajectory-based phenotypes based on the type, severity, and progression of organ dysfunction in the first 72 hours. We analyzed the candidate phenotypes to determine reproducibility as well as prognostic, therapeutic, and biological relevance. Overall, 38,732 children had suspected infection, of which 15,246 (39.4%) had sepsis-associated MODS with an in-hospital mortality of 10.1%. We identified an organ dysfunction trajectory-based phenotype (which we termed persistent hypoxemia, encephalopathy, and shock) that was highly reproducible, had features of systemic inflammation and coagulopathy, and was independently associated with higher mortality. In a propensity score-matched analysis, patients with persistent hypoxemia, encephalopathy, and shock phenotype appeared to have HTE and benefit from adjuvant therapy with hydrocortisone and albumin. When compared with other high-risk clinical syndromes, the persistent hypoxemia, encephalopathy, and shock phenotype only overlapped with 50%-60% of patients with septic shock, moderate-to-severe pediatric acute respiratory distress syndrome, or those in the top tier of organ dysfunction burden, suggesting that it represents a nonsynonymous clinical phenotype of sepsis-associated MODS. CONCLUSIONS: We derived and validated the persistent hypoxemia, encephalopathy, and shock phenotype, which is highly reproducible, clinically relevant, and associated with HTE to common adjuvant therapies in children with sepsis