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This paper establishes five new facts about instructional costs in higher education using department-level data from a broad range of institutions. Costs vary widely across fields, ranging from electrical engineering (90% higher than English) to math (25% lower). This pattern is largely explained by differences in class size and faculty pay. Some STEM fields experienced steep declines in expenditures over the past 17 years, while others saw increases. Changes in class size and teaching loads alongside a shift toward contingent faculty explain these trends. Finally, the association between online instruction and instructional costs is statistically indistinguishable from zero.

How expensive is a college degree? Usually, the answer is based on what students pay in tuition and fees compared to what they earn after graduation. Very little is known about the economic cost of running an electrical engineering program compared to, say, a history department, or the resource consequences of steering more students into these fields. To fill this gap, the authors examine department-level data on expenditures, outputs, and factors of production for undergraduate, graduate, and professional degree programs at nearly 600 four-year institutions across the United States from 2000 to 2017. The analysis compares the instructional costs per student credit hour at more than 8,000 departments in 20 disciplines, including both in-person and online study. The authors establish five new facts about college costs: (1) substantial cost differences across fields of study; (2) most of these patterns can be explained by differences in class size and, to a lesser extent, differences in average faculty pay; (3) cost differences have evolved over time; (4) these trends are explained in part by a growing number of adjunct faculty as well as changes in class size and faculty teaching loads; and (5) online instruction is not a cost-saver. The results underscore that the social return to investment in high-earning fields may be lower than wage premiums suggest, because high-return fields also tend to be more costly to teach. In addition, the work suggests that while differences in production technology enable some departments to take different approaches to cost management, from changing the mix of faculty to increasing class size, online instruction does not have a meaningful association with college costs, at least in its current form. Any one-discipline-fits-all approach to addressing cost escalation in higher education, including moving more classes online, is likely to be ineffective.

Ranking the contribution of genotype, environment, and management (G × E × M) on maize's economic optimum nitrogen fertilizer rate (EONR) variability could improve understanding and predictability of EONR. We performed a simulation experiment using the Agricultural Production Systems sIMulator model with the objectives to (1) rank the effects of 24 individual G × E × M factors on the magnitude and interannual variability of the EONR across the US Midwest and (2) investigate the impact of G × M factors on the EONR variability under present and future climate scenarios. Results indicate that genetics (27%), management (31%), and environmental conditions (41%) each influence the EONR variability. Within these broad categories, the top three individual factors impacting the EONR were interannual weather variability, crop radiation use efficiency, and the soil inorganic N carryover from the previous year. The G × E × M factors influenced the yield response to N fertilizer in different ways. Soil‐related factors (e.g., organic matter and residual nitrate) influenced grain yields at the low N rates, while management factors (e.g., planting date and density) influenced yield at all N rates. Combining increases in plant density and changes in genetics synergistically increased the EONR by 15% from baseline. Future climate scenarios without adaptation decreased the EONR and yield loss, but crop adaptation was buffered against the negative climate change impacts. We concluded that 59% of the annual EONR variability is manageable (due to genetics and management) and that G × M factors could buffer climate change's negative effects on crop production. Present results can inform experimental research on N fertilizer and N rate decisions. Core Ideas: Genotype, management, and environment explained 27%, 31%, and 41% of the economic optimum N rate (EONR) variability.Weather variability, crop radiation use efficiency, and inorganic nitrogen were the most influential factors on the EONR.Soil nitrogen‐related factors mostly affected the grain yield at low N‐fertilizer rates.Increases in plant density and changes in genetics synergistically increased EONR. [ABSTRACT FROM AUTHOR]

Research on knowledge transfer in organizations has been hampered by the lack of tools yielding valid scores for studying critical constructs in concert. The authors developed survey measures of absorptive capacity (the ability to transform new knowledge into usable knowledge) and experienced community of practice (the extent to which a person is engaged with the given practice community) to provide tools appropriate for field research. A holdout sample of 1,971 engineers in a Fortune 100 science/technology company yielded 583 responses. Confirmatory factor analysis was used to assess internal structure, and convergent and discriminant evidence of validity. Path analysis was used to assess criterion-related validity. Results demonstrate that the new measures are internally consistent, are related in meaningful ways to other organizational variables, and provide distinct explanatory power. An additional 231 responses from a second Fortune 100 science/technology company provides cross-validation. (Contains 6 tables and 2 figures.)

A computer simulation used in teams by 151 business students tested their ability to translate strategy into decisions. Over eight weeks, the experiential learning activity encouraged strategic decision making and group behavior consistent with long-term strategy. (SK)

Describes Texas program that allows school districts to access Medicaid funds as partial reimbursement for medically related services that are provided to Medicaid-eligible children who are identified for special-education services and who have specific eligible services written into their individualized educational plans. (Author/PKP)

Nitrogen fertilizer recommendations in corn (Zea mays L.) that match the economically optimal nitrogen fertilizer rate (EONR) are imperative for profitability and minimizing environmental degradation. However, the amount of soil N available for the crop depends on soil and weather factors, making it difficult to know the EONR from year‐to‐year and from field‐to‐field. Our objective was to explore, within the framework of hydrologic soil groups and drainage classifications (HGDC), which site‐specific soil and weather properties best estimated corn N needs (i.e., EONR) for two application timings (at‐planting and side‐dress). Included in this investigation was a validation step using an independent dataset. Forty‐nine N response trials conducted across the U.S. Midwest Corn Belt over three growing seasons (2014–2016) were used for recommendation model development, and 181 independent site‐years were used for validation. For HGDC models, soil organic matter (SOM), clay content, and evenness of rainfall distribution before side‐dress N application were the properties generally most helpful in predicting EONR. Using the validation data, model recommendations were within 34 kg N ha–1 of EONR for 37 and 42% of the sites with a root mean square error (RMSE) of 70 and 68 kg N ha–1 for at‐planting and side‐dress applications, respectively. Compared to state‐specific recommendations, sites needing <100 kg N ha–1 or no N were better estimated with HGDC models. In contrast, for sites where EONR was >150 kg N ha–1, HGDC models underestimated N needs compared to state specific. These results show HGDC groupings could aid in developing tools for N fertilizer recommendations. Core Ideas: Soil hydrologic classifications aid in determining corn N fertilizer rates.Economically optimal N rate was best predicted by different soil and weather properties for each hydrologic group.Soil organic matter, clay, and rainfall evenness generally helped in estimating economically optimal N rate.Compared to state N recommendations, developed models were better when economically optimal N rate <100 kg ha−1. [ABSTRACT FROM AUTHOR]

Improving corn (Zeamays L.) N managementis pertinent to economic and environmental objectives. However, there are limited comprehensive data sources to develop and test N fertilizer decision aid tools across a wide geographic range of soil and weather scenarios. Therefore, a public-industry partnership was formed to conduct standardized corn N rate response field studies throughout the U.S. Midwest. This research was conducted using a standardized protocol at 49 site-years across eight states over the 2014-2016 growing seasons with many soil, plant, and weather related measurements. This note provides the data (found in supplemental files), outlines the data, summarizes key findings, and highlights the strengths and weakness for those who wish to use this dataset. [ABSTRACT FROM AUTHOR]

Improving corn (Zea mays L.) N fertilizer rate recommendation tools is necessary for improving farmers' profits and minimizing N pollution. Research has repeatedly shown that weather and soil factors influence available N and crop N need. Adjusting available corn N recommendation tools with soil and weather measurements could improve farmers' ability to manage N. The aim of this research was to improve publicly available N recommendation tools with site-specific soil and weather measurements. Information from 49 site-years of N response trials in the U.S. Midwest was used to evaluate 21 rate recommendation tools for a single (at-planting) and split (at-planting + sidedress) N application. Using elastic net and decision tree algorithms, the difference between each tool's N recommendation and the economically optimum nitrogen rate (EONR) was modeled against soil and weather measurements. The model's predicted values were used to adjust the tools. Unadjusted the best performing tool had r² = .24; after adjustment, the best performing tool had r² = .57. Overall tool improvement was modest and sometimes required many additional inputs. Using weather measurements (e.g., evenness of rainfall or abundant and well-distributed rainfall) helped increase N recommendations by accounting for N loss while soil measurements (e.g., pH and total C) helped decrease N recommendations when there was sufficient available soil N. This investigation showed that incorporating soil and weather measurements is a viable approach for improving corn N recommendation tools regionally; but even with adjustments, tools still have room for additional improvement. [ABSTRACT FROM AUTHOR]

Splitting the N application into two or more timings may improve corn (Zea mays L.) grain yield and N recovery relative to a single‐N application. A 49 site‐year study across eight U.S. Midwestern states compared the effect of an at‐planting (single‐N application) and two split‐N applications [45 (45+SD) or 90 kg N ha−1 (90+SD) at planting with the remainder of the total rate (180 or 270 kg N ha−1) applied at V9]. For split‐N applications, soil and plant responses were similar between 45+SD and 90+SD 93–98% of the time, indicating the at‐planting N rate of 45 kg N ha−1 may be all that is needed in most cropping scenarios. Splitting the N application compared to a single‐N application changed soil NO3–N at VT and post‐harvest <35% of the time and plant N uptake and grain yield <15% of the time. Split‐N applications had greater grain yield in areas with uniform precipitation around the sidedress timing (Shannon Diversity Index >0.56–0.59) to incorporate N in the root zone, and in coarse‐textured soil (sand content >4–10%) that had greater potential for N loss. Single‐N applications produced greater grain yield in soils with more total N (>2.1–2.4 g kg−1) to support N mineralization and greater cation exchange capacity (CEC) (> 27–31 cmolc kg−1), silt content (>66–74%), or clay content (>24–37%) to improve nutrient and water retention. Decisions on nitrogen application timing should be made based on soil parameters and typical weather conditions around the sidedress timing. [ABSTRACT FROM AUTHOR]

Anaerobic potentially mineralizable nitrogen (PMN) combined with preplant nitrate test (PPNT) or pre‐sidedress nitrate test (PSNT) may improve corn (Zea mays L.) N management. Forty‐nine corn N response studies were conducted across the U.S. Midwest to evaluate the capacity of PPNT and PSNT to predict grain yield, N uptake, and economic optimal N rate (EONR) when adjusted by soil sampling depth, soil texture, temperature, PMN, and initial NH4–N from PMN analysis. Pre‐plant soil samples were obtained for PPNT (0‐ to 30‐, 30‐ to 60‐, 60‐ to 90‐cm depths) and PMN (0‐ to 30‐cm depth) before corn planting and N fertilization. In‐season soil samples were obtained at the V5 corn development stage for PSNT (0‐ to 30‐, 30‐ to 60‐cm depths) at 0 kg N ha−1 at‐planting rate and for PMN when 0 and 180 kg N ha−1 was applied at planting. Grain yield, N uptake, and EONR were best predicted when separating soils by texture or sites by annual growing degree‐days and including PMN and initial NH4–N with either NO3–N test. Using PSNT (mean R2 =.30)‐instead of PPNT (mean R2 =.19)‐based models normally increased predictability of corn agronomic variables by a mean of 11%. Including PMN and initial NH4–N with PPNT or PSNT only marginally improved predictability of grain yield, N uptake, and EONR (R2 increase ≤.33; mean R2 =.35). Therefore, including PMN with PPNT or PSNT is not suggested as a tool to improve N fertilizer management in the U.S. Midwest. [ABSTRACT FROM AUTHOR]

The anaerobic potentially mineralizable N (PMN) test combined with the preplant (PPNT) and presidedress (PSNT) nitrate tests may improve corn (Zea mays L.) N fertilization predictions. Forty‐nine corn N response experiments (mostly corn following soybean [Glycine max (L.) Merr.]) were conducted in the U.S. Midwest from 2014–2016 to evaluate the ability of the PPNT and PSNT to predict corn relative yield (RY) and N fertilizer over‐ and under‐application rates when adjusted by PMN. Before planting and N fertilization, PPNT (0–30, 30–60, and 60–90 cm) and PMN (0–30 cm) samples were obtained. In‐season soil samples were obtained at the V5 development stage for PSNT (0–30, 30–60 cm) in all N rate treatments and PMN (0–30 cm) in only the 0 and 180 kg N ha−1 preplant N treatments. Increasing NO3–N sampling depths beyond 30 cm with or without PMN improved RY predictability marginally (R2 increase up to 0.20) and reduced over‐ and under‐application frequencies up to 14%. Including PMN (preplant only) with PPNT or PSNT improved RY predictability minimally (R2 increase up to 0.10) only for coarse‐ and medium‐textured soils, but N fertilizer over‐ and under‐application frequencies were not substantially reduced (≤12%). These marginal improvements in RY predictability and N fertilizer over‐ and under‐application frequencies, regardless of the variables used (e.g., fertilization, sampling depth, soil texture, and growing degree‐day categories), demonstrate that including PMN with soil NO3–N alone does not improve corn N fertilization need predictions enough to recommend their use. [ABSTRACT FROM AUTHOR]

Determining which corn (Zea mays L.) N fertilizer rate recommendation tools best predict crop N need would be valuable for maximizing profits and minimizing environmental consequences. Simultaneous comparisons of multiple tools across various environmental conditions have been limited. The objectives of this research were to evaluate the performance of publicly‐available N fertilizer recommendation tools across diverse soil and weather conditions for: (i) prescribing N rates for planting and split‐fertilizer applications, and (ii) economic and environmental effects. Corn N‐response trials using standardized methods were conducted at 49 sites, spanning eight US Midwest states and three growing seasons. Nitrogen applications included eight rates in 45 kg N ha−1 increments all at‐planting and matching rates with 45 kg N ha−1 at‐planting plus at the V9 development stage. Tool performances were compared to the economically optimal N rate (EONR). Over this large geographic region, only 10 of 31 recommendation tools (mainly soil nitrate tests) produced N rate recommendations that weakly correlated to EONR (P ≤.10; r2 ≤.20). With other metrics of performance, the Maximum Return to N (MRTN) soil nitrate tests, and canopy reflectance sensing came close to matching EONR. Economically, all tools but the Maize‐N crop growth model had similar returns compared to EONR. Environmentally, yield goal based tools resulted in the highest environmental costs. Results show that no tool was universally reliable over this study's diverse growing environments, suggesting that additional tool development is needed to better represent N inputs and crop utilization at a larger regional level. [ABSTRACT FROM AUTHOR]

Chronic callers (N=67) are reviewed; 51 percent were diagnosed as drug or alcohol dependent. Four were known to have committed suicide and 37 had made suicide attempts; 47 percent of the group was eventually referred to an ongoing treatment resource. Agency strategies for dealing with the chronic caller are discussed. (Author)

Describes the TREE (Toronto Research and Education Environment) System, an interactive computer system developed as a teaching and research tool using simulation models for MBA students. The appendix includes selected calculations from a printout. (CHC)

Image 1 • Sustainable agriculture is complex and includes multiple interdependent goals. • Sound science and innovation are the keys to sustainable agriculture, and are already delivering results. • Greater investment is needed in the basics of agronomy, biotechnology, and plant breeding to ensure that farmers and consumers everywhere benefit from science and technology. • Farmers must receive support for implementing sustainability measures, especially in light of the downward trend of commodity prices. [ABSTRACT FROM AUTHOR]

Estimates of mineralizable N with the anaerobic potentially mineralizable N (PMNan) test could improve predictions of corn (Zea mays L.) economic optimal N rate (EONR). A study across eight US midwestern states was conducted to quantify the predictability of EONR for single and split N applications by PMNan. Treatment factors included different soil sample timings (pre-plant and V5 development stage), planting N rates (0 and 180 kg N ha-1), and incubation lengths (7, 14, and 28 d) with and without initial soil NH4--N included with PMNan. Soil was sampled (0-30 cm depth) before planting and N application and at V5 where 0 or 180 kg N ha-1 were applied at planting. Evaluating across all soils, PMNan was a weak predictor of EONR (R² ≤ 0.08; RMSE, ≥67 kg N ha-1), but the predictability improved (15%) when soils were grouped by texture. Using PMNan and initial soil NH4--N as separate explanatory variables improved EONR predictability (11-20%) in fine-textured soils only. Delaying PMNan sampling from pre-plant to V5 regardless of N fertilization improved EONR predictability by 25% in only coarse-textured soils. Increasing PMNan incubations beyond 7 d modestly improved EONR predictability (R² increased ≤0.18, and RMSE was reduced ≤7 kg N ha-1). Alone, PMNan predicts EONR poorly, and the improvements from partitioning soils by texture and including initial soil NH4--N were relatively low (R² ≤ 0.33; RMSE ≥ 68 kg N ha-1) compared with other tools for N fertilizer recommendations. [ABSTRACT FROM AUTHOR]

Cereal rye (Secale cereale L.) cover crops (RCC) have good potential to take up residual NO3 between grain crops and reduce loss to surface waters. However, studies in Iowa have shown a 6% corn (Zea mays L.) yield reduction in no-till when grown following a RCC. The objective of this research was to study tillage and starter N agronomic practices that have potential to improve corn yield in a RCC system. This study was conducted at four sites from 2014-2016 with corn grown in rotation with soybean [Glycine max. (L.) Merr.]. Treatments included rye aerial broadcast into soybean before leaf drop and no RCC, tillage or no-till, and starter N fertilizer (34 kg N ha-1) or no starter. The main fertilizer N was sidedressed. The aerial rye sowing, especially with dry fall conditions and planned RCC termination in the spring at 15-20 cm height, resulted in non-uniform RCC stand with low biomass and N content (range 6-36 kg N ha-1). However, the profile soil NO3-N at rye termination was decreased, approximately the same amount as the RCC aboveground N content. The V6 stage corn height and V10 stage canopy sensing indexes increased with tillage and starter. Overall, corn yield was reduced with the RCC (0.2 Mg ha-1), tillage increased yield (3%), but starter N did not increase yield despite consistent increased early corn growth. If soil erosion potential is low, springtime tillage would be a practice to help improve corn early growth and yield in a RCC system. [ABSTRACT FROM AUTHOR]

Studies are limited that focus on change in concentration and accumulation of secondary and micronutrients in corn (Zea mays L.) plant fractions and across corn hybrid development periods. This research was conducted in 2007 and 2008 to evaluate the partitioning of secondary and micronutrients across vegetative and reproductive stages at the plant-fraction level for 1960- and 2000-era hybrids. Two popular hybrids for each era were grown, with measurement of nutrient concentration and content in several plant and grain fractions. Secondary and micronutrient concentrations in plant fractions were lower in 2000- than 1960-era hybrids with most nutrients, except ear shoots and tassels for certain nutrients. However, nutrient content was consistently greater in 2000- compared to 1960-era hybrids in the whole plant and fractions at most development stages, except tassels and ear shoots. In tassels, nutrient content was mostly smaller in 2000-era hybrids, but in ear shoots content was similar. The accumulation rates of most nutrients per growing degree day (GDD) were greater in the reproductive period for 2000-era hybrids, but similar among eras in the vegetative period. Remobilized nutrients from vegetative to reproductive components were similar between era hybrids, except Ca and Fe, and positive except Fe, Mn, and B. It is apparent that greater nutrient content in newer hybrids was driven mainly by associated nutrient uptake rates and greater dry matter (DM). Despite the greater nutrient content with the modern hybrids, removal with grain or stover harvest would still be small for S and micronutrients. [ABSTRACT FROM AUTHOR]

Determination of in-season N requirement for corn (Zea mays L.) is challenging due to interactions of genotype, environment, and management. Machine learning (ML), with its predictive power to tackle complex systems, may solve this barrier in the development of locally based N recommendations. The objective of this study was to explore application of ML methodologies to predict economic optimum nitrogen rate (EONR) for corn using data from 47 experiments across the US Corn Belt. Two features, a water table adjusted available water capacity (AWCwt) and a ratio of in-season rainfall to AWCwt (RAWCwt), were created to capture the impact of soil hydrology on N dynamics. Four ML models--linear regression (LR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO) regression, and gradient boost regression trees (GBRT)--were assessed and validated using "leave-one-location-out" (LOLO) and "leave-one-year-out" (LOYO) approaches. Generally, RR outperformed other models in predicting both at planting and split EONR times. Among the 47 tested sites, for 33 sites the predicted split EONR using RR fell within the 95% confidence interval, suggesting the chance of using the RR model to make an acceptable prediction of split EONR is ~70%. When RR was used to test split EONR prediction with input weather features surrogated with 10 yr of historical weather data, the model demonstrated robustness (MAE, 33.6 kg ha-1; R² = 0.46). Incorporating mechanistically derived hydrological features significantly enhanced the ability of the ML procedures to model EONR. Improvement in estimating in-season soil hydrological status seems essential for success in modeling N demand. [ABSTRACT FROM AUTHOR]