From Seers to Sen: The Meaning of Economic Development

inequality, poverty, employment, growth, neoclassicism, entitlement, famine

Regional Approach to Making Nitrogen Fertilizer Rate Decisions for Corn

Nitrogen fertilizer is one of the largest input costs for growing corn. Across the Corn Belt, N is typically the most yield-limiting nutrient. Facing record high N fertilizer prices and potential supply problems, producers are concerned about N fertilization rates. Soil fertility researchers and extension specialists from seven states across the Corn Belt (see list in acknowledgements section) have been discussing com N fertilization needs and evaluating N rate recommendation systems for approximately the past two years. These discussions could not have been timelier considering the current N fertilizer issues. In recent years N recommendation systems have become more diverse across states in the Com Belt. Of particular significance has been the movement away from yield goal as a basis of N rate decisions in some states to other methods such as cropping system (Iowa) or soil specific yield potential (Wisconsin). Research from across the Com Belt has also been indicating that economic optimum N rate (EONR) does not vary according to yield level. At the same time, corn yields have been at historic high levels, leading to increases in yield goal. This has added to concerns that increasing yield-based N rates are often found to be substantially greater than EONR observed in N rate trials. Also, watersheds being targeted to receive incentive and cost­ share funds for N rate management sometimes cross state boundaries, which causes problems if suggested rates are not consistent. These issues have increased uncertainty regarding current N rate recommendations. An outcome of the multi-state discussions has been development of a consistent approach for N rate guideline development that can be utilized on a regional basis. This does not necessarily mean that fertilizer N rates will be the same across states. Rather, there is a common approach to guideline development. Depending upon the research database, rates could be the same or quite different Another outcome of this approach has been an improved ability to evaluate the economic returns to N, and the ability to estimate the most profitable fertilizer N rates. This has become very valuable information for dealing with today\u27s high N fertilizer prices and water quality issues

Maize Leaf Appearance Rates: A Synthesis From the United States Corn Belt

The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Biophysical crop models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf−1) or leaf appearance rate (LAR; leaf oC-day−1). However, such important parameter values are rarely estimated for modern maize hybrids. To fill this gap, we sourced and analyzed experimental datasets from the United States Corn Belt with the objective to (i) determine phyllochron values for two types of models: linear (1-parameter) and bilinear (3-parameters; phase I and II phyllochron, and transition point) and (ii) explore whether environmental factors such as photoperiod and radiation, and physiological variables such as plant growth rate can explain variability in phyllochron and improve predictability of maize phenology. The datasets included different locations (latitudes between 48° N and 41° N), years (2009–2019), hybrids, and management settings. Results indicated that the bilinear model represented the leaf number vs. GDD relationship more accurately than the linear model (R2 = 0.99 vs. 0.95, n = 4,694). Across datasets, first phase phyllochron, transition leaf number, and second phase phyllochron averaged 57.9 ± 7.5°C-day, 9.8 ± 1.2 leaves, and 30.9 ± 5.7°C-day, respectively. Correlation analysis revealed that radiation from the V3 to the V9 developmental stages had a positive relationship with phyllochron (r = 0.69), while photoperiod was positively related to days to flowering or total leaf number (r = 0.89). Additionally, a positive nonlinear relationship between maize LAR and plant growth rate was found. Present findings provide important parameter values for calibration and optimization of maize crop models in the United States Corn Belt, as well as new insights to enhance mechanisms in crop models

Relative abundance of organochlorine pesticides and polychlorinated biphenyls in adipose tissue and serum of women

Some organochlorine pesticides (OCPs) and PCBs are under investigation as possible risk factors for breast cancer because of their estrogenic properties and widespread presence in the environment. It is important to know whether adipose tissue used by some investigators and serum assays used by others can provide comparable information on body burden. Concentrations of seven OCPs or their breakdown products as well as 14 PCB congeners were measured in the adipose tissue and serum of 293 women enrolled as controls in a case-control study of environmental factors for breast cancer in Long Island, New York, a high-risk region. Adipose OCP/PCB levels were measured using a supercritical fluid extraction method developed by the authors. 1,1-Dichloro-2,2-di(4-chlorophenyl)ethylene (p,p’- DDE) was detected in all adipose and serum samples; two chlordane derivatives, β-hexachlorocyclohexane (a lindane isomer) and hexachlorobenzene, were detected in at least 92% of adipose samples. The di-ortho hexachlorinated PCB congeners 2,4,5,2’,4’,5’- hexachlorobiphenyl and 2,3,4,2’,4’,5’-hexachlorobiphenyl were detected in all adipose and over 98% of serum samples. 1,1-Dichloro-2,2-di(4-chlorophenyl)ethylene comprised 77% of total pesticide residues in adipose and 71% in serum. 2,4,5,2’,4’,5’-Hexachlorobiphenyl comprised 24% of adipose and 21 % of serum PCBs. The relative concentration patterns of the 14 PCB congeners were similar to those reported in other human studies and were also typical of patterns reported in environmental samples from various biota, including mammals and birds, but differed substantially from patterns reported in occupationally exposed workers. All adipose-serum correlations for pesticides and most PCBs were statistically significant. Either serum or adipose OCP/PCB levels of a variety of environmental organochlorine compounds may serve as useful biomarkers of body burden

Active-Optical Reflectance Sensing Corn Algorithms Evaluated over the United States Midwest Corn Belt

Uncertainty exists with corn (Zea mays L.) N management due to year-to-year variation in crop N need, soil N supply, and N loss from leaching, volatilization, and denitrification. Activeoptical reflectance sensing (AORS) has proven effective in some fields for generating N fertilizer recommendations that improve N use efficiency, but locally derived (e.g., within a US state) AORS algorithms have not been tested simultaneously across a broad region. The objective of this research was to evaluate locally developed AORS algorithms across the US Midwest Corn Belt region for making in-season corn N recommendations. Forty-nine N response trials were conducted across eight states and three growing seasons. Reflectance measurements were collected and sidedress N rates (45–270 kg N ha–1 on 45 kg ha–1 increments) applied at approximately V9 corn development stage. Nitrogen recommendation rates from AORS algorithms were compared with the end-of-season calculated economic optimal N rate (EONR). No algorithm was within 34 kg N ha–1 of EONR \u3e 50% of the time. Average recommendations differed from EONR 81 to 147 kg N ha–1 with no N applied at planting and 74 to 118 kg N ha–1 with 45 kg of N ha–1 at planting, indicating algorithms performed worse with no N applied at planting. With some algorithms, utilizing red edge instead of the red reflectance improved N recommendations. Results demonstrate AORS algorithms developed under a particular set of conditions may not, at least without modification, perform very well in regions outside those within which they were developed

Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information

Active-optical reflectance sensors (AORS) use light reflectance characteristics from a crop canopy as an indicator of the plant’s N health. However, studies have shown AORS algorithms used in conjunction with measured reflectance characteristics for corn (Zea mays L.) N fertilizer rate recommendations are not consistently accurate. Our objective was to determine if soil and weather information could be utilized with an AORS algorithm developed at the University of Missouri (ALGMU) to improve in-season (~V9 corn development stage) N fertilizer recommendations. Nitrogen response trials were conducted across eight states over three growing seasons, totaling 49 sites with soils ranging in productivity. Nitrogen fertilizer rates according to the ALGMU were compared to economic optimal nitrogen rate (EONR). Without soil and weather information included, the root mean square error (RMSE) of the difference between ALGMU and EONR (MUDIFF) was 81 and 74 kg N ha–1 for treatments receiving 0 and 45 kg N ha–1 applied at planting, respectively. When ALGMU was adjusted using weather (seasonal precipitation and distribution prior to sidedress) and soil clay content, the RMSE was reduced by 24 to 26 kg N ha–1. Without adjustment, 20 and 29% of sites were within 34 kg N ha–1 of EONR with 0 and 45 kg N ha–1 at planting, respectively. But with adjustment for soil and weather data, 45 and 51% of sites were within 34 kg N ha–1 of EONR. These results show that weather and soil information could be used to improve ALGMU N recommendation performance

Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information

Active-optical reflectance sensors (AORS) use light reflectance characteristics from a crop canopy as an indicator of the plant’s N health. However, studies have shown AORS algorithms used in conjunction with measured reflectance characteristics for corn (Zea maysL.) N fertilizer rate recommendations are not consistently accurate. Our objective was to determine if soil and weather information could be utilized with an AORS algorithm developed at the University of Missouri (ALGMU) to improve in-season (∼V9 corn development stage) N fertilizer recommendations. Nitrogen response trials were conducted across eight states over three growing seasons, totaling 49 sites with soils ranging in productivity. Nitrogen fertilizer rates according to the ALGMU were compared to economic optimal nitrogen rate (EONR). Without soil and weather information included, the root mean square error (RMSE) of the difference between ALGMU and EONR (MUDIFF) was 81 and 74 kg N ha–1 for treatments receiving 0 and 45 kg N ha–1 applied at planting, respectively. When ALGMU was adjusted using weather (seasonal precipitation and distribution prior to sidedress) and soil clay content, the RMSE was reduced by 24 to 26 kg N ha–1. Without adjustment, 20 and 29% of sites were within 34 kg N ha–1 of EONR with 0 and 45 kg N ha–1 at planting, respectively. But with adjustment for soil and weather data, 45 and 51% of sites were within 34 kg N ha–1 of EONR. These results show that weather and soil information could be used to improve ALGMU N recommendation performance

Incorporating lessons from high-input research into a low-margin year

Increased soybean commodity prices in recent years have generated interest in developing high-input systems to increase yield. However, little information exists about the effects of input-intensive, high-yield management on soybean yield and profitability, as well as interactions with basic agronomic practices

Active-Optical Reflectance Sensing Corn Algorithms Evaluated over the United States Midwest Corn Belt

Uncertainty exists with corn (Zea mays L.) N management due to year-to-year variation in crop N need, soil N supply, and N loss from leaching, volatilization, and denitrification. Active-optical reflectance sensing (AORS) has proven effective in some fields for generating N fertilizer recommendations that improve N use efficiency, but locally derived (e.g., within a US state) AORS algorithms have not been tested simultaneously across a broad region. The objective of this research was to evaluate locally developed AORS algorithms across the US Midwest Corn Belt region for making in-season corn N recommendations. Forty-nine N response trials were conducted across eight states and three growing seasons. Reflectance measurements were collected and sidedress N rates (45–270 kg N ha–1 on 45 kg ha–1increments) applied at approximately V9 corn development stage. Nitrogen recommendation rates from AORS algorithms were compared with the end-of-season calculated economic optimal N rate (EONR). No algorithm was within 34 kg N ha–1 of EONR \u3e 50% of the time. Average recommendations differed from EONR 81 to 147 kg N ha–1 with no N applied at planting and 74 to 118 kg N ha–1 with 45 kg of N ha–1 at planting, indicating algorithms performed worse with no N applied at planting. With some algorithms, utilizing red edge instead of the red reflectance improved N recommendations. Results demonstrate AORS algorithms developed under a particular set of conditions may not, at least without modification, perform very well in regions outside those within which they were developed