Crop Management Practices in Indiana Soybean Production Systems?

To meet the current and future needs of today\u27s soybean producer it is vital that agricultural researchers and Extension specialists clearly understand the production concerns of our clientele. The objective of this research was to characterize the current management practices of Indiana soybean (Glycine max (L.) Merr.) growers, to identify specific educational needs, and to provide a framework for directing applied soybean research efforts. This assessment was conducted through a direct-mail survey. The results of this survey define distinct similarities and differences among growers of different farm operation size. Large acreage growers (\u3e1000 acres) were more likely to plant soybeans in rows spaced 11 to 20 inches, reduce seeding rates, plant earlier, and have higher yields. Large acreage growers were also more likely to own a yield monitor, conduct on-farm research, use a computer, and routinely use the Internet. Our research also identified different research and educational needs based on farm operation size. By specifically targeting these needs, agricultural researchers and Extension specialists may improve the economic and environmental sustainability of each clientele group

Marketing Practices of Indiana Soybean Producers

Soybean marketing decisions play a critical role in maximizing farm income. The objective of the project described here was to identify market related educational needs and to provide benchmark information for producers. The assessment was conducted through a detailed direct-mail survey. The results of the survey demonstrate differences in market access among grower operation sizes and regions, and differences in forward pricing among grower operation sizes. Farmers with large operations generally have access to more markets and are more likely to manage price risk

Pest Management in Indiana Soybean Production Systems

As the disparity in farm size continues to increase and university Extension budgets tighten, it is imperative that Extension correctly identifies the specific needs of our clientele. Our objective was to identify clientele educational needs and to provide a framework for directing applied soybean research efforts. This assessment was conducted through a detailed direct-mail survey that was sent to 5,000 (1,330 respondents) Indiana soybean growers. The results of the survey demonstrate differences among grower operation sizes with respect to scouting and pest management practices. Farmers with large operations generally scout and manage pests more intensively than small or mid-size farmers

Advancing agricultural research using machine learning algorithms

Rising global population and climate change realities dictate that agricultural productivity must be accelerated. Results from current traditional research approaches are difficult to extrapolate to all possible fields because they are dependent on specific soil types, weather conditions, and background management combinations that are not applicable nor translatable to all farms. A method that accurately evaluates the effectiveness of infinite cropping system interactions (involving multiple management practices) to increase maize and soybean yield across the US does not exist. Here, we utilize extensive databases and artificial intelligence algorithms and show that complex interactions, which cannot be evaluated in replicated trials, are associated with large crop yield variability and thus, potential for substantial yield increases. Our approach can accelerate agricultural research, identify sustainable practices, and help overcome future food demands

Modeling the Relationship Between Estimated Fungicide Use and Disease-Associated Yield Losses of Soybean in the United States I: Foliar Fungicides vs Foliar Diseases

Fungicide use in the United States to manage soybean diseases has increased in recent years. The ability of fungicides to reduce disease-associated yield losses varies greatly depending on multiple factors. Nonetheless, historical data are useful to understand the broad sense and long-term trends related to fungicide use practices. In the current study, the relationship between estimated soybean yield losses due to selected foliar diseases and foliar fungicide use was investigated using annual data from 28 soybean growing states over the period of 2005 to 2015. For national and regional (southern and northern United States) scale data, mixed effects modeling was performed considering fungicide use as a fixed and state and year as random factors to generate generalized R2 values for marginal (R2GLMM(m); contains only fixed effects) and conditional (R2GLMM(c); contains fixed and random effects) models. Similar analyses were performed considering soybean production data to see how fungicide use affected production. Analyses at both national and regional scales showed that R2GLMM(m) values were significantly smaller compared to R2GLMM(c) values. The large difference between R2 values for conditional and marginal models indicated that the variation of yield loss as well as production were predominantly explained by the state and year rather than the fungicide use, revealing the general lack of fit between fungicide use and yield loss/production at national and regional scales. Therefore, regression models were fitted across states and years to examine their importance in combination with fungicide use on yield loss or yield. In the majority of cases, the relationship was nonsignificant. However, the relationship between soybean yield and fungicide use was significant and positive for majority of the years in the study. Results suggest that foliar fungicides conferred yield benefits in most of the years in the study. Furthermore, the year-dependent usefulness of foliar fungicides in mitigating soybean yield losses suggested the possible influence of temporally fluctuating abiotic factors on the effectiveness of foliar fungicides and/or target disease occurrence and associated loss magnitudes

Field validation of a farmer supplied data approach to close soybean yield gaps in the US North Central region

CONTEXT: Producer-reported data can be used to identify suites of management practices that lead to higher yield and profit. However, a rigorous validation of the approach in relation to its potential impact on farmer yield and profit is lacking. OBJECTIVE: This study aimed to validate a producer-data approach on its capability to guide on-farm evaluation of management practices with greatest potential for increasing producer yield and profit. We show proof of concept using soybean in the North Central US region as a case study. METHODS: We used a combination of regression tree analysis and a spatial framework to determine practices with highest influence on yield for specific climate domains across the region. These practices were used as a basis for designing an ‘improved’ management package for each domain. The impact associated with adoption of the ‘improved’ management package on producer yield, seed constituents, and profit was evaluated against a ‘reference’ treatment that follows farmer management via replicated on-farm trials across 100 sites over two crop seasons. RESULTS AND CONCLUSIONS: Average yield was 278 kg ha-1 higher in the improved versus reference management, equivalent to a closure of the current exploitable yield gap by 40%. In turn, adoption of the improved management led to an average increase of $76 ha-1 in net profit. Sensitivity analysis showed that adoption of the improved management packages should increase farmer profit across a wide range of grain price scenarios, with very small downside risk. Seed protein concentration was negatively associated with the positive yield advantage of the improved management, whereas seed oil concentration tended to increase. SIGNIFICANCE: Analysis of producer data can accelerate discovery, evaluation, and adoption of suites of management practices that consistently lead to higher farmer yield and profit, which, in turn, would help speed up current rates of yield gain

Seedbank Persistence of Palmer Amaranth (\u3ci\u3eAmaranthus palmeri\u3c/i\u3e) and Waterhemp (\u3ci\u3eAmaranthus tuberculatus\u3c/i\u3e) across Diverse Geographical Regions in the United States

Knowledge of the effects of burial depth and burial duration on seed viability and, consequently, seedbank persistence of Palmer amaranth (Amaranthus palmeri S. Watson) and waterhemp [Amaranthus tuberculatus (Moq.) J. D. Sauer] ecotypes can be used for the development of efficient weed management programs. This is of particular interest, given the great fecundity of both species and, consequently, their high seedbank replenishment potential. Seeds of both species collected from five different locations across the United States were investigated in seven states (sites) with different soil and climatic conditions. Seeds were placed at two depths (0 and 15cm) for 3 yr. Each year, seeds were retrieved, and seed damage (shrunken, malformed, or broken) plus losses (deteriorated and futile germination) and viability were evaluated. Greater seed damage plus loss averaged across seed origin, burial depth, and year was recorded for lots tested at Illinois (51.3% and 51.8%) followed by Tennessee (40.5% and 45.1%) and Missouri (39.2% and 42%) for A. palmeri and A. tuberculatus, respectively. The site differences for seed persistence were probably due to higher volumetric water content at these sites. Rates of seed demise were directly proportional to burial depth (α=0.001), whereas the percentage of viable seeds recovered after 36 mo on the soil surface ranged from 4.1% to 4.3% compared with 5% to 5.3% at the 15-cm depth for A. palmeri and A. tuberculatus, respectively. Seed viability loss was greater in the seeds placed on the soil surface compared with the buried seeds. The greatest influences on seed viability were burial conditions and time and site-specific soil conditions, more so than geographical location. Thus, management of these weed species should focus on reducing seed shattering, enhancing seed removal from the soil surface, or adjusting tillage systems

High-Input Management Systems Effect on Soybean Seed Yield, Yield Components, and Economic Break-Even Probabilities

Elevated soybean [Glycine max (L.) Merr.] prices have spurred interest in maximizing soybean seed yield and has led growers to increase the number of inputs in their production systems. However, little information exists about the effects of high-input management on soybean yield and profitability. The purpose of this study was to investigate the effects of individual inputs, as well as combinations of inputs marketed to protect or increase soybean seed yield, yield components, and economic break-even probabilities. Studies were established in nine states and three soybean growing regions (North, Central, and South) between 2012 and 2014. In each site-year both individual inputs and combination high-input (SOYA) management systems were tested. When averaged between 2012 and 2014, regional results showed no seed yield responses in the South region, but multiple inputs affected seed yield in the North region. In general, the combination SOYA inputs resulted in the greatest yield increases (up to 12%) compared to standard management, but Bayesian economic analysis indicated SOYA had low break-even probabilities. Foliar insecticide had the greatest break-even probabilities across all environments, although insect pressure was generally low across all site-years. Soybean producers in North region are likely to realize a greater response from increased inputs, but producers across all regions should carefully evaluate adding inputs to their soybean management systems and ensure that they continue to follow the principles of integrated pest management

Characterizing Genotype X Management Interactions on Soybean Seed Yield

Increased soybean [Glycine max (L.) Merr.] commodity prices in recent years have generated interest in high-input systems to increase yield. The objective of this study was to evaluate the effects of current, high-yielding cultivars under high- and low-input systems on soybean yield and yield components. Research trials were conducted at 19 locations spanning nine states from 2012 to 2014. At each location, six high-yielding cultivars were grown under three input systems: (i) standard practice (SP, current recommended practices), (ii) high-input treatment consisting of a seed treatment fungicide, insecticide, nematistat, inoculant, and lipo-chitooligosaccharide (LCO); soil-applied N fertilizer; foliar LCO, fertilizer, antioxidant, fungicide and insecticide (SOYA), and (iii) SOYA minus foliar fungicide (SOYA-FF). An individual site-year yield analysis found only 3 of 53 (5.7%) site-years examined had a significant cultivar × input system interaction, suggesting cultivar selection and input system decisions can remain independent. Across all site-years, the SOYA and SOYA-FF treatments yielded 231 (5.5%) and 147 kg ha–1 (3.5%) more than the SP, and input system differences were found among maturity groups. Yield component measurements (seeds m–2, seed mass, early-season and final plant stand, pods plant–1, and seeds pod–1) indicated positive yield responses were due to increased seeds m–2 and seed mass. While both high-input systems increased yield on average, grower return on investment (ROI) would be negative given today’s commodity prices. These results further support the use of integrated pest management principles for making input decisions instead of using prophylactic applications to maximize soybean yield and profitability

Defining optimal soybean seeding rates and associated risk across North America

Soybean [Glycine max (L.) Merr.] seeding rate research across North America is typically conducted in small geo-political regions where environmental effects on the seeding rate × yield relationship are minimized. Data from 211 individual field studies (∼21,000 data points, 2007–2017) were combined from across North America ranging in yield from 1,000– 7,500 kg ha−1. Cluster analysis was used to stratify each individual field study into similar environmental (soil × climate) clusters and into high (HYL), medium (MYL), and low (LYL) yield levels. Agronomically optimal seeding rates (AOSR) were calculated and Monte Carlo risk analysis was implemented. Within the two northern most clusters the AOSR was higher in the LYL followed by the MYL and then HYL. Within the farthest south cluster, a relatively small (±15,000 seeds ha−1) change in seeding rate from the MYL was required to reach the AOSR of the LYL and HYL, respectively. The increase in seeding rate to reach the LYL AOSR was relatively greater (5x) than the decrease to reach the HYL AOSR within the northern most cluster. Regardless, seeding rates below the AOSR presented substantial risk and potential yield loss, while seeding rates above provided slight risk reduction and yield increases. Specific to LYLs and MYLs, establishing and maintaining an adequate plant stand until harvest maximized yield regardless of the seeding rate, while maximizing seed number was important with lower seeding rates. These findings will help growers manage their soybean seed investment by adjusting seeding rates based upon the productivity of the environment.Fil: Gaspar, Adam P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mourtzinis, Spyridon. University of Wisconsin; Estados UnidosFil: Kyle, Don. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Galdi, Eric. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Lindsey, Laura E.. Ohio State University; Estados UnidosFil: Hamman, William P.. Ohio State University; Estados UnidosFil: Matcham, Emma G. University of Wisconsin; Estados UnidosFil: Kandel, Hans J.. North Dakota State University; Estados UnidosFil: Schmitz, Peder. North Dakota State University; Estados UnidosFil: Stanley, Jordan D.. North Dakota State University; Estados UnidosFil: Schmidt, John P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mueller, Daren S.. University of Iowa; Estados UnidosFil: Nafziger, Emerson D.. University of Illinois; Estados UnidosFil: Ross, Jeremy. University of Arkansas for Medical Sciences; Estados UnidosFil: Carter, Paul R.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Varenhorst, Adam J.. University of South Dakota; Estados UnidosFil: Wise, Kiersten A.. University of Kentucky; Estados UnidosFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados UnidosFil: Carciochi, Walter Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Kansas State University; Estados UnidosFil: Chilvers, Martin I.. Michigan State University; Estados UnidosFil: Hauswedell, Brady. University of South Dakota; Estados UnidosFil: Tenuta, Albert U.. University of Guelph; CanadáFil: Conley, Shawn P.. University of Wisconsin; Estados Unido