Agronomy Journal最新文献

Globally, agricultural scientists are challenged with creating, testing, and validating climate-smart nutrient strategies that reduce greenhouse gas emissions while increasing food security. This study determined maize (Zea mays L.) N recommendations and bias for N-rate studies conducted in South Dakota using models created for western Minnesota, Iowa, Eastern North Dakota, Nebraska, and South Dakota. From 2019 to 2021, 16 N rate studies were conducted in long-term no-tillage (>6 years) fields located in South Dakota. In the randomized block replicated study, the soils were mollisols that were derived in a semi-arid frigid environment. The economic optimum N rates were calculated using four fertilizer-to-maize grain price ratios (4.11, 5.48, 6.85, and 8.23 [$ (kg N)⁻¹] [$ (kg grain)⁻¹]⁻¹). Analysis showed that reducing the yield goal coefficient used in the South Dakota model from 21.4 to 17.9 kg N (Mg grain)⁻¹ reduced the recommended N rate but did not reduce yield. The reduced yield goal coefficient that considered the fertilizer-to-maize price ratio also reduced model root mean square error (RMSE), bias, and the estimated partial carbon dioxide equivalence (CO_2e) by at least 18%. Nitrogen recommendation models developed for western Minnesota, Iowa, and South Dakota had similar RMSE, bias, and fertilizer recommendations, and adjusting the recommendation based on expected fertilizer cost and maize selling price improved accuracy. This study suggests that yield was not sacrificed by reducing the coefficients from 21.4 to 17.9 kg N (Mg grain)⁻¹ and that recommendations are improved by considering the fertilizer-to-maize grain price ratio.

The amount of high-resolution agricultural data has increased rapidly in the current decade. Integration of satellite multispectral imagery, combine harvester yield monitoring data, and soil moisture mapping allows managing for within-field variation and better interpreting on-farm experimentation. In this study, we investigated the effect of cover crops on yield in Finland by integrating Sentinel-2 satellite imagery (normalized difference vegetation index), topographic soil moisture indexes, and high-resolution yield data. The experiment was run by three farmers over 4 years and serves as an example for low-cost on-farm experimentation. Our results confirmed earlier findings that undersown cover crops result in approximately 5% yield loss. We also found that the effect is highly variable across farms and within fields. The highest yield losses were found in areas of the field, which were wetter in the spring seeding time. The competition between crop and cover crop could be observed in the vegetation maps for autumn and early summer. Combining NDVI and soil moisture maps allows delineating field zones, which require extra management to reduce the risk of yield loss from cover crop resource competition. Evaluating the overall effect of cover crops on yield would require replication on more farms. The within-field variation results and workflow investigated in this study can guide placement of sampling areas within those fields.

This manuscript is a follow-up of previously published results that presented findings on pennycress (Thlaspi arvense L.) establishment and agronomics in response to previous corn (Zea mays L.) relative maturity (CRM) hybrids grown for grain and silage in the corn–pennycress–soybean [Glycine max (L.)] rotations. In this manuscript, we compared the economics of grain corn–pennycress–soybean rotations (grain rotation) with silage corn–pennycress–soybean rotations (silage rotation). The treatments were CRM hybrids ranging from 76 to 95 days (full season) at northern sites (Morris and Rosemount, MN) and 95 to 113 days (full season) at southern sites (Lexington, IL, and Custar, OH). Full-season corn harvested for silage was included as a control treatment representing optimum conditions for sowing pennycress. A partial budget procedure was used for economic analysis. The results showed that the annualized net benefits (ANBs) ranged from $315 to $945 ha⁻¹. The silage rotation produced greater ANBs than the grain rotation at all sites due to increased pennycress seed yield. In the grain rotation, the 105 days in the south, 95 days corn at Morris, and 86 days corn at Rosemount resulted in minimal ANB losses compared with silage rotation. Among grain corn treatments, some of the early CRM hybrids resulted in greater ANBs (up to 40%) than the full season hybrid. Results demonstrate potential to integrate pennycress into a grain rotation using early CRM hybrids. In addition, valuing the diverse ecosystem benefits that pennycress offers as a cash cover crop during the offseason between corn and soybean rotation may help to attract growers.

Safflower is a multipurpose crop grown in different regions, mainly for its high oil quality. Crop wild relatives serve as a valuable reservoir of genes that have been depleted due to evolutionary bottlenecks, which are poorly applied in safflower. During the last decade, we developed three populations from hybridization of safflower with its wild relatives and selected the superior lines to develop new varieties. From each of three different interspecific populations (TP: Carthamus tinctorius × Carthamus palaestinus, PO: C. palaestinus × Carthamus oxyacantha, and TO: C. tinctorius × C. oxyacantha), 10 genotypes were selected (a total of 30 lines) in the “F8” generation and were evaluated along with their parents (T, P, and O) and one control cultivar (Golsfid) at the field during 2019–2022 to assess genetic variation, estimate genetic parameters, and evaluate the stability. Considerable genetic variability for oil, seed yield, and other agronomic traits between and within the interspecific populations suggests the high potential of these new recombinant lines for introducing beneficial alleles. Our results indicated that recombinant inbred lines derived from the hybridization of TP were superior in terms of seed yield, oil content, and stability parameters. The use of stability indices of Wricke, Lin and Binns, Eberhart and Russell, and HMRPGVi, along with the biplot analysis, allowed the identification of preferable and stable safflower genotypes. Moderately high values of heritability were found for yield-related traits. New recombinant lines can be introduced to the safflower gene pool to improve the genetic base of this valuable oil seed crop.

Synthetic auxin herbicide movement onto sensitive cotton (Gossypium hirsutum L.) cultivars has impacted many US cotton hectares. The spatial scope and severity of auxin damage in-season is typically estimated by an agronomist. The use of remote sensing technology has the potential to objectively quantify the spatial scope and severity of auxin damage. Experiments were conducted in 2019, 2020, and 2021 in Grand Junction, TN, to determine: (1) the effect of reflectance data collection timing; (2) the effect of auxin exposure timing; (3) the value of near infrared and red-edge (RE) reflectance versus reflectance within the visible spectrum data; and (4) if/how visual injury relates to aerial reflectance data. Applications of 2,4-D or dicamba were made to susceptible cotton cultivars at 1X, 1/4X, 1/16X, 1/64X, 1/256X, and 1/1024X rates at either matchhead square (MHS) or 2 weeks after first bloom (FB+2WK). Non-treated controls were also included for each application timing. Aerial reflectance data were collected 7, 14, 21, and 28 days after application. Unsupervised classification of images into pixels with and without vegetation did not increase correlations between vegetation indices (VIs) and application rate. Although Vis, which generated the strongest correlations with application rate, visual injury, and relative lint yield, were generally RE based, similar correlations were also noted with visible spectrum VIs. Correlations were greater when auxin injury occurred at MHS than FB+2WK. Results suggest reflectance measured within the visible spectrum can quantify the scope and severity of auxin injury if the injury occurs early during the growing season.

Plant cover and biochemical composition are essential parameters for evaluating cover crop management. Destructive sampling or estimates with aerial imagery require substantial labor, time, expertise, or instrumentation cost. Using low-cost consumer and mobile phone cameras to estimate plant canopy coverage and biochemical composition could broaden the use of high-throughput technologies in research and crop management. Here, we estimated canopy development, tissue nitrogen, and biomass of medium red clover (Trifolium pratense L.), a perennial forage legume and common cover crop, using red-green-blue (RGB) indices collected with standard settings in non-standardized field conditions. Pixels were classified as plant or background using combinations of four RGB indices with both unsupervised machine learning and preset thresholds. The excess green minus red (ExGR) index with a preset threshold of zero was the best index and threshold combination. It correctly identified pixels as plant or background 86.25% of the time. This combination also provided accurate estimates of crop growth and quality: Canopy coverage correlated with red clover biomass (R² = 0.554, root mean square error [RMSE] = 219.29 kg ha⁻¹), and ExGR index values of vegetation pixels were highly correlated with clover nitrogen content (R² = 0.573, RMSE = 3.5 g kg⁻¹) and carbon:nitrogen ratio (R² = 0.574, RMSE = 1.29 g g⁻¹). Data collection were simple to implement and stable across imaging conditions. Pending testing across different sensors, sites, and crop species, this method contributes to a growing and open set of decision support tools for agricultural research and management.

Yield monitoring technology (YM) is a valuable tool to evaluate crop performance in on-farm research (OFR). However, limited information exists on utilizing this technology to accurately inform OFR. The objectives of this study were to evaluate the ability of grain yield monitor mass flow sensors to detect changes in corn (Zea mays L.) yield for different plot lengths, provide a recommended minimum plot length to utilize YM in OFR, and determine if differences in estimating yield existed between YMs. Six treatment lengths that varied in distance of intentional yield differences (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) were created by alternating high-yield (202 kg N/ha application) and low-yield (0 kg N/ha application) plots. A total of four grain YMs with impact-style mass flow sensors were used within two commercially available combines. Yield comparisons were made between the plot combine and YMs to evaluate the accuracy of each technology for detecting the magnitude of yield change across lengths using analysis of variance and exponential regression curves. Results indicated that the mass flow sensors were not sensitive enough to detect quickly changing flow rates for alternating yield changes in small plot lengths. Minimum plot lengths ranged from 43 to 107 m depending on YM. Significant differences were observed between grain YMs from different manufacturers. Future work could evaluate the influence additional crops or smaller yield differences have on the optimum OFR plot length.

Wheat has been an important part of the human diet for millennia. The increase in demand for wheat grown organically in the United States and globally reflects the growing interest in organic food and food products. A symposium on organic wheat production was held during the annual meeting of the American Society of Agronomy in Baltimore, MD, during 2021. Presenters discussed the state-of-the-science on organic wheat research. Papers were solicited following the symposium for inclusion in this special section. As a result, five papers are included in this special section: four focus on organic wheat research in North America while one discusses results of a European study.

In times of climate change and global population growth, agricultural yield forecasts play an increasingly important role. For example, predicting yields as early as possible in the event of a drought is crucial for decision-makers in politics, government, and business. The aim of this study was to provide precise yield predictions at agricultural regions as early as possible with a minimum amount of weather data. Random forest models were used for this purpose. Although more than 290,000 datasets were available for analysis, all models tended to be heavily overfitting, which can be explained by the strong fragmentation of the input data by crop, region, and prediction time. The models reacted very differently to unknown datasets. It was found that the regionally trained models achieved lower (≥10%) relative root mean square errors (RRMSEs) than the supra-regionally trained models. Rapeseed and barley achieved good predictions. Wheat had good potential, too. Corn, potatoes, and sugar beet achieved often too high RRMSEs. The results showed that targeted model selection for each region and an extension of the training time series could enable very good regional yield forecasts for rapeseed and cereals in the future.

In the United States, grain sorghum [Sorghum bicolor (L.) Moench] production is concentrated in the US Great Plains region, with the state of Kansas accounting for ∼50% of the planted area. In Kansas, state-level grain yields steadily increased at a rate of 0.07 Mg ha⁻¹ year⁻¹ from 1957 to 1990. However, since 1990, sorghum yield trends across the United States and Kansas have been exhibiting signs of yield stagnation. The objectives of this study were to (1) quantify the magnitude of the yield gap and (2) identify possible reasons for yield stagnation of rainfed sorghum in Kansas. Current yield (Y_c) was estimated as the average yield of the most recently reported 10 years. Maximum attainable yield (Y_a) and water-limited potential yield (Y_w) were estimated with a frontier yield function using an extensive dataset of crop performance trials, yield contest data, and county-level survey yield data totaling 2997 site-years. State-level Y_c was 4.7 Mg ha⁻¹, which represents 77% of Y_a and 49% of Y_w. At a regional level, there is a trend of increasing yield gap in central and western Kansas sorghum-producing regions. Sorghum yield in Kansas appears to be stagnant due to a small exploitable yield gap relative to Y_a rather than Y_w, a statewide shift in planting area to environments more vulnerable to water deficits, and cultivation in soils with moderate to severe limitations.