The concept of thermal time, measured in degree-days, is widely used among the agricultural community in Nebraska to make decisions in corn (
Zea mays
L.) production. Instead of the real-time ...temperatures that are experienced by corn plants, most of the widely available temperature data are limited to daily timescale observations from standard meteorological stations. And a variety of equations are used by different agricultural groups (e.g., researchers, advisors, farmers, and seed companies) to estimate thermal time for corn. Two problems could arise: (a) the estimation method is lacking in accuracy; and (b) different estimation methods are used for the same purpose by different groups. Consequently, citing these inaccurate and maybe inherently different thermal time results could lead to biased decisions in corn production. The goal of this study is to evaluate six commonly used estimation methods by comparing the estimated thermal time with the hourly temperature approximated thermal time. We analyzed the root mean square error and mean absolute error for six metrics of total growing season (from May through September) degree-days based on the temperature data from a total of 14 long-term observing locations in Nebraska. In particular, we selected four location-extreme year cases to demonstrate the six methods’ estimation performance on a daily timescale. We found that the most commonly used adjusted
T
max
and
T
min
rectangle method provided poor estimation in the study area. Instead, single-sine, double-sine, or
T
avg
-based method was more superior depending on the metric of degree-days.
•We evaluated ability of a crop model to predict local and regional maize yield and production without calibration of internal parameters.•Yield potential was simulated for a wide range of ...environments in the US Corn Belt using a well-validated maize simulation model.•Simulated yield and total production were compared against actual yield and production at four different spatial scales.•We developed an approach to estimate actual yields based on year-specific simulated yield and long-term mean simulated and actual yields.•The proposed approach was robust at reproducing actual yield and total production.
Crop simulation models are used at the field scale to estimate crop yield potential, optimize current management, and benchmark input-use efficiency. At issue is the ability of crop models to predict local and regional actual yield and total production without need of site-year specific calibration of internal parameters associated with fundamental physiological processes. In this study, a well-validated maize simulation model was used to estimate yield potential for 45 locations across the U.S. Corn Belt, including both irrigated and rainfed environments, during four years (2011–2014) that encompassed diverse weather conditions. Simulations were based on measured weather data, dominant soil properties, and key management practices at each location (including sowing date, hybrid maturity, and plant density). The same set of internal model parameters were used across all site-years. Simulated yields were upscaled from locations to larger spatial domains (county, agricultural district, state, and region), following a bottom-up approach based on a climate zone scheme and distribution of maize harvested area. Simulated yields were compared against actual yields reported at each spatial level, both in absolute terms as well as deviations from long-term averages. Similar comparisons were performed for total maize production, estimated as the product of simulated yields and official statistics on maize harvested area in each year. At county-level, the relationship between simulated and actual yield was better described by a curvilinear model, with decreasing agreement at higher yields (>12Mgha−1). Comparison of actual and simulated yield anomalies, as estimated from the yearly yield deviations from the long-term actual and simulated average yield, indicated a linear relationship at county-level. In both cases (absolute yields and yield anomalies comparisons), the agreement increased with increasing spatial aggregation (from county to region). An approach based on long-term actual and simulated yields and year-specific simulated yield allowed estimation of actual yield with a high degree of accuracy at county level (RMSE≤18%), even in years with highly favorable weather or severe drought. Estimates of total production, which are of greatest interest to buyers and sellers in the market, were also in close agreement with actual production (RMSE≤22%). The approach proposed here to estimate yield and production can complement other approaches that rely on surveys, field crop cuttings, and empirical statistical methods and serve as basis for in-season yield and production forecasts.
On‐farm research is a method for transferring technology to farmers, validating small plot research, generating new discovery, and evaluating field‐scale, site‐specific management techniques. Little ...has been done to understand what motivates farmers to participate in on‐farm research and what the impact of their participation is on their decision making and farm profitability. This study evaluated the University of Nebraska‐Lincoln's over 25 yr‐old on‐farm research program using a semi‐structured, in‐depth interview tool to complete interviews with a stratified sample of 40 of the 140 past‐participants. The focus of this qualitative study was obtaining rich descriptions of farmer participants to better understand motivation for involvement in an on‐farm research program, participant research experiences, and the economic impacts of an on‐farm research program. Quantitative statistical analyses of the qualitative data are included for informational purposes; however, our conclusions focus on the qualitative analysis. Farmers participated in an on‐farm research program for a variety of reasons, most commonly for economic gain. Positive experiences were largely credited to the interactions with university faculty and other farmers. The participating farmers’ perception regarding whether or not the project took too much time was unrelated to whether weigh wagons or yield monitors were used. Farmers were interested in many aspects of research, including project ideation, experimental design, and statistical analysis. Participating farmers’ satisfaction in their experience and implementation of research results was not dependent on the farmers independently generating their research topic. The most impassioned suggestion from the farmer research participants was to develop innovative research projects.
Core Ideas
In‐depth interviews with participants in an on‐farm research program generated insights into motivation for participation and program impact.
Farmers participated in the program for a variety of reasons including economic gain, seeking answers to a specific questions, general curiosity, and a desire for reliable and unbiased research results.
Positive experiences in the on‐farm research program were largely credited to interactions with university faculty and other farmers.
The origin of the research topic or idea did not influence participant satisfaction in their experience or implementation of the results.
Seventy‐five percent of interviewees had put their research results into practice in their farm operation, either by making a change based on results or by not making a change as the research confirmed their current practice.
Collecting soil, topography, and yield information has become more feasible and reliable with advancements in precision technologies. Combined with the accessibility of precision technologies and ...services to farmers, there has been increased interest and ability to make site-specific crop management decisions. The objective of this research was to develop procedures to optimize corn seeding rates and maximize yield using soil and topographic parameters. Experimental treatments included five seeding rates (61 750; 74 100; 86 450; 98 800; and 111 150 seeds ha
−1
) in a randomized complete block design in three central Iowa fields from 2012 to 2014 (nine site-years). Soil samples were analyzed for available phosphorus (Olsen method), exchangeable potassium (ammonium-acetate method), pH, soil organic matter (SOM), cation exchange capacity (CEC), and texture. Topographic data (in-field elevation, slope, aspect, and curvature) were determined from publically available light detection and ranging data. In four site-years, no interaction occurred between seeding rate and the descriptive variables. Three of the site-years resulted in a negative linear seeding rate response which made it impossible to determine an optimum seeding rate above the lowest seeding rate treatment. The seeding rate optimization process in five site-years resulted in seeding rate by variable interactions; four site-years had a single seeding rate by variable interaction (pH, in-field elevation, or curvature) and one site-year had three seeding rate by variable interactions (pH, CEC, and SOM). Meaningful seeding rate optimizations occurred in only three of nine site-years. There was not a consistent descriptive variable interaction with seeding rate as a result of weather variability.
Broadcast interseeding cover crops into corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) instead of drill-planting after harvest extends the cover crop season and improves productivity, but ...establishment can be insufficient. Our objectives were to find broadcast seeding rates that result in maximum spring biomass and N uptake. We tested cereal rye (Secale cereale L.) and hairy vetch (Vicia villosa Roth) in south-central and eastern Nebraska in 2016–2017 and 2017–2018. Seeding rates for rye were 341, 512, and 682 seeds∙m−2, and 119, 178, and 238 seeds∙m−2 for vetch. We broadcast in late September and terminated by early May. Fall emergence was between 3 and 54% of broadcast seeds, and greater for vetch. When broadcast into corn, rye spring biomass was 1472 kg∙ha−1 with N uptake of 38 kg∙ha−1. Vetch biomass was 361 kg∙ha−1 with 13 kg∙ha−1 N uptake. In soybean, rye produced 2318 kg∙ha−1 with 59 kg N∙ha−1 and vetch produced 535 kg∙ha−1 with 21 kg N∙ha−1. Higher seeding rates increased biomass and N uptake only for rye broadcast into corn. Year and site effects and possibly differences in main crops influenced cover crop productivity.
Abnormal ear development in corn: A field survey Ortez, Osler A.; McMechan, Anthony J.; Hoegemeyer, Thomas ...
Agrosystems, geosciences & environment,
2022, Letnik:
5, Številka:
1
Journal Article
Recenzirano
Odprti dostop
In July of 2016, abnormal ear development in corn (Zea mays L.) (barbell‐ears, multiple ears per node herein termed as multi‐ears, and short‐husks) was reported in several cornfields that extended ...from the Texas Panhandle to eastern Colorado and East through Kansas, Nebraska, Iowa, and Illinois. Field surveys were conducted to study these ear abnormalities. Affected and unaffected plants were sampled from 15 farmer fields located in central and eastern Nebraska. Each plant was evaluated for ear type, ear placement, internode length, and grain yield. Along with plant evaluations, management practices and weather information were collected from the surveyed fields. Of the 15 surveyed fields, nine were grouped as affected (more than 10% abnormalities), and six were grouped as checks (<10% abnormalities). Affected fields averaged 26% of abnormalities, whereas check fields averaged only 4%. Ear abnormalities occurred on ears that seemed to be placed lower on plants relative to normal ears. Plants with abnormal ears had yield reductions between 35 and 91%, compared to plants with normal ears. Findings suggested that ear abnormalities may be a cumulative result from the classic genetic (hybrid‐specific), environmental (stress factors), and management interactions. The study of underlying causes for abnormal ear development in corn is imperative for understanding the likelihood of future events occurring and providing critical information to potentially manage and mitigate these issues.
Core Ideas
Ear development issues in corn were reported in Nebraska and the region in 2016.
Affected fields averaged 26% abnormal ears, up to 49% in a given field.
Abnormal ears reduced yield, between 35 and 91% relative to normal ears.
Abnormal ear placement seemed to be lower compared to normal ears.
Ear abnormalities may be the cumulative result of classic G × E × M interactions.
The response of corn (Zea mays L.) grain yield (GY) to plant population or seeding rate is well studied. Population recommendations have been previously made by amassing numerous data points to find ...the optimum plant population. However, the response has not yet been linked with the corn suitability rating (CSR), a measure of soil productivity. We evaluated the effect of seeding rate with respect to the CSR system on corn GY. The study was performed across 33 site‐years in Iowa from 2006 to 2009. Seeding rates ranged from 49,400 to 118,560 seeds ha−1. Two versions of CSR were examined—the original CSR and the revised CSR2. Averaged across all site‐years, corn GY showed a quadratic response to the seeding rate. Predicted corn GY was maximized at 13.4 Mg ha−1 with the seeding rate of 96,000 ha−1. Corn GY plateaued for the highest CSR class, had significant quadratic responses to second and third CSR classes, but did not respond to seeding rate at the lowest CSR class. However, corn GY did not plateau for the highest CSR2 class as in the CSR. Although all three lower CSR2 classes responded quadratically, there was no clear pattern. Generally, the predicted corn GY also responded quadratically to seeding rate with respect to the parameters used for calculating CSR2 values. Because the original CSR values incorporate climatic conditions and predicted more realistic corn GY responses to seeding rate in this study, we recommend using CSR values when estimating the optimum seeding rate to maximize corn GY.
Cover crop (CC) roots are critical for soil ecosystem service delivery including soil stabilization, C and nutrient cycling, soil health improvement, and others. However, most CC studies only ...evaluate CC aboveground biomass yield, neglecting the belowground portion of the plant. The objectives of this study were to quantify the impacts of (a) CC planting (pre‐ and post‐harvest) dates and (b) early (2–4 wk before main crop planting) and late (at main crop planting) CC termination with and without corn (Zea mays L.) residue removal on root biomass yield. We assessed the effects of CC planting or termination dates on root biomass yield for surface 10 cm of soil at four sites through sampling at CC termination and separating roots from soil with a hydropneumatic elutriation system. Pre‐harvest CC planting had limited and variable impacts on root biomass yield compared with post‐harvest planting. Corn residue removal had no impact on root biomass yield. However, CC termination date had effects at the Irrigated but not at the Rainfed site. At the Irrigated site, late‐terminated CCs doubled root biomass yield in both years compared to early terminated and no CC. At this site, under late‐terminated CCs, root biomass yield was 2.8 Mg ha–1 attributed to their higher aboveground biomass yield and later termination. At the Rainfed site, root biomass yield was 1.6 Mg ha–1. Overall, late termination of CCs can increase root biomass yield; however, early planting into the cash crop did not consistently increase root biomass yield under the conditions of this study.
Cover crops (CCs) can provide multiple soil, agricultural production, and environmental benefits. However, a better understanding of such potential ecosystem services is needed. We summarized the ...current state of knowledge of CC effects on soil C stocks, soil erosion, physical properties, soil water, nutrients, microbial properties, weed control, crop yields, expanded uses, and economics and highlighted research needs. Our review indicates that CCs are multifunctional. Cover crops increase soil organic C stocks (0.1–1 Mg ha−1 yr−1) with the magnitude depending on biomass amount, years in CCs, and initial soil C level. Runoff loss can decrease by up to 80% and sediment loss from 40 to 96% with CCs. Wind erosion potential also decreases with CCs, but studies are few. Cover crops alleviate soil compaction, improve soil structural and hydraulic properties, moderate soil temperature, improve microbial properties, recycle nutrients, and suppress weeds. Cover crops increase or have no effect on crop yields but reduce yields in water‐limited regions by reducing available water for the subsequent crops. The few available studies indicate that grazing and haying of CCs do not adversely affect soil and crop production, which suggests that CC biomass removal for livestock or biofuel production can be another benefit from CCs. Overall, CCs provide numerous ecosystem services (i.e., soil, crop–livestock systems, and environment), although the magnitude of benefits is highly site specific. More research data are needed on the (i) multi‐functionality of CCs for different climates and management scenarios and (ii) short‐ and long‐term economic return from CCs.
PDF
