Growing conditions in the U.S. Midsouth allow for large soybean [Glycine max L. (Merr.)] yields under irrigation, but there is limited information on planting dates (PD) and maturity group (MG) choices to aid in cultivar selection. Analysis of variance across eight (2012) and 10 (2013) locations, four PD, and 16 cultivars (MG 3-6), revealed that the genotype by environment (G×E) interaction accounted for 38 to 22% of the total yield variability. Stability-analysis techniques and probability of low yields were used to investigate this interaction. Planting dates were grouped within early-and late-planting systems. Results showed that MG 4 and 5 cultivars in early-planting systems had the largest average yields, whereas for late-planting systems, late MG 3 to late MG 4 cultivars had the largest yields. Least square means by MG within planting systems at each environment showed that MG 4 cultivars had the greatest yields or were not signi cantly di erent from the MG with the greatest yields in 100% of the environments for both early-and late-planting systems. Yields of MG 5 cultivars were similar to those of MG 4 in 100% of the environments with an early planting but only in 20% of the environments with a late planting. e MG 3 cultivars were the best second choice for late plantings, with similar yields to MG 4 cultivars in 55 to 75% of the environments. ese results have profound implications for MG recommendations in irrigated soybean in the U.S. Midsouth and indicate the need to reconsider common MG recommendations.

Planting date is one of the main factors affecting soybean (Glycine max [L.] Merr.) yield. Environmental conditions in the US Midsouth allow for planting dates from late March through early July, and maturity groups (MGs) ranging from 3 to 6. However, the complexity of interactions among planting date, MG, and the environment makes the selection of an optimum MG cultivar difficult. A regional 3‐yr study, conducted at eight locations with latitudes ranging from 30.6 to 38.9°N, planting dates ranging from late March to early July, and MGs 3 to 6, was used to examine the relationship between relative yield and planting day. The data indicated that yield was dependent on the location and MG choice. There was a quadratic response of relative yield to planting day in six out of the eight locations studied for MG 3 cultivars, and in five locations for MG 4 cultivars. On the other hand, MG 5 and 6 cultivars were more likely to have a negative linear relationship, with a quadratic response in only two of the eight locations. Optimum planting dates that maximized yield were dependent on the location and MG combination and ranged from 22 March to 17 May. Delaying planting dates from mid May to early June reduced yields by 0.09 to 1.69% per day, with the rate of decline greatest at the southern‐most locations. Overall, MG 4 cultivars maximized yield or were not statistically different from the highest yielding MG at most locations and planting dates.

Core Ideas Intercropping cowpea with grain sorghum dramatically decreased sorghum grain yield. Clover green manure only increased sorghum grain yield 1 out of 3 yr. Fertilizer effects likely masked by mineralized organic matter from plowing of perennial pasture. Crop residue decomposition was mostly unaffected by treatments. Crop residue decomposition was largely driven by residue C/N ratios. Legumes fix N, reduce fertilizer inputs for primary crops, and contribute C to soil organic matter (SOM), which benefits soil productivity and sustainability. Objectives of this study were to (i) maintain grain sorghum [Sorghum bicolor (L.) Moench] yields while decreasing N fertilizer requirements and build SOM using a temperate legume cover crop and tropical legume intercrop; (ii) quantify soil water usage between legume treatments and N fertilizer rates; and (iii) evaluate treatment effects on residue decomposition of each crop. In Overton, TX, crimson clover (Trifolium incarnatum L.) and cowpea [Vigna unguiculata L. (Walp)] were used as green manure and an intercrop, respectively, with grain sorghum. Four N fertilizer rates of 0, 45, 90, and 135 kg ha−1 were randomly assigned within a four replicate split‐split plot design on a Lilbert loamy fine sand. From 2010 through 2012, intercropped cowpea decreased sorghum grain yield between 77 and 93% compared to monocropped sorghum. Clover green manure increased sorghum grain yield 21% over winter fallow in 2012, but not significantly (P = 0.22). After 3 yr, soil organic C (12.4 g kg−1) and soil N (1.1 g kg−1) were 18% (P = 0.01) and 21% (P = 0.01) higher, respectively, for clover green manure compared to fallow at 0 to 15‐cm depth. Crop residue decomposition was relatively stable across years and only intercropping showed small increases in decomposition of grain sorghum residue. Overall, clover green manure proved to be a more sustainable choice than intercropping cowpea from both a yield and soil perspective.

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Italian ryegrass (Lolium perenne ssp. multiflorum (Lam) Husnot) is a troublesome weed species in wheat (Triticum aestivum) production in the United States, severely affecting grain yields. Spatial mapping of ryegrass infestation in wheat fields and early prediction of its impact on yield can assist management decision making. In this study, unmanned aerial systems (UAS)-based red, green and blue (RGB) imageries acquired at an early wheat growth stage in two different experimental sites were used for developing predictive models. Deep neural networks (DNNs) coupled with an extensive feature selection method were used to detect ryegrass in wheat and estimate ryegrass canopy coverage. Predictive models were developed by regressing early-season ryegrass canopy coverage (%) with end-of-season (at wheat maturity) biomass and seed yield of ryegrass, as well as biomass and grain yield reduction (%) of wheat. Italian ryegrass was detected with high accuracy (precision = 95.44 ± 4.27%, recall = 95.48 ± 5.05%, F-score = 95.56 ± 4.11%) using the best model which included four features: hue, saturation, excess green index, and visible atmospheric resistant index. End-of-season ryegrass biomass was predicted with high accuracy (R2 = 0.87), whereas the other variables had moderate to high accuracy levels (R2 values of 0.74 for ryegrass seed yield, 0.73 for wheat biomass reduction, and 0.69 for wheat grain yield reduction). The methodology demonstrated in the current study shows great potential for mapping and quantifying ryegrass infestation and predicting its competitive response in wheat, allowing for timely management decisions.

Roots strongly influence the growth and yield of field crops. We characterized root morphological traits of 10 winter wheat varieties in order to determine the extent they were influenced by the environments and impacted grain yield under two irrigation regimes at Bushland (a cooler, drier site with clay loam soil) and Uvalde (a warmer, wetter site with clay soil) in Texas, USA, from 2015 to 2017. Major root traits, including root diameter, specific root length (SRL), root surface area (SSA), tissue mass density (TMD), root length density (RLD), and root weight density, were measured and related to one another and to grain yield. RLD of wheat decreased but SRL and SSA increased with soil depth. Irrigation was second to environment in affecting root traits. Compared with Uvalde, the environment of Bushland promoted deeper root growth, higher TMD, but reduced SRL and SSA. Water deficit inhibited RLD and root: shoot ratio at Bushland, but moderately promoted them at Uvalde. Both SRL and RLD were positively associated with grain yield, with the former relation stronger under drought. The dichotomy of "conservative" versus "acquisitive" root strategy partially explained the variations of root traits of winter wheat in contrasting environments. K E Y W O R D Sdrought stress, irrigation management, root economic spectrum, site, soil-plant interaction, varieties

Despite benefits to crop rotations and recent increases in value, the United States produces only a third of the canola (Brassica napus L.) it consumes. To encourage production expansion, an experiment in Moscow, ID, evaluated dual‐purpose winter canola in a biennial system for forage and seed production. Two winter canola cultivars were sown at three planting densities (4.5, 6.7, and 9.0 kg ha−1) over four planting dates (May through September) in 2008, 2009, 2010, and 2011. Vegetative biomass during the first year was harvested and ensiled to determine silage quality. Cultivars performed similarly over all treatments for forage yield and quality. The two highest planting densities yielded more forage, but no seed yield differences were detected. Total dry matter forage yield (DMFY) was greatest for May plantings (5.2 t DM ha−1), while August seeded canola yielded 2.4 t DM ha−1. Baldur produced higher seed yield than Athena by 352 kg ha−1, while planting dates had significant, but inconsistent effects on seed yield compared to the fall‐planted, uncut control, which averaged 2389 kg ha−1. Fiber content of canola silage (canolage) was extremely low while crude protein (CP) remained consistently high. Canolage quality was exceedingly high, however, high silage pH indicated poor ensiling, which likely led to excessive loss of organic material in the silage that increased ash content. Early‐planted winter canola withstood multiple forage harvests without having a large impact on seed yield most years and economics indicate this may be a feasible management practice.

Introduction A long planting window for soybeans leads to a large range of planting date (PD) and soybean maturity (MG) choices. Soybean cultivars are grouped by MG to reflect different time requirements until harvest.  Early planting often results in greater expected returns but increases return risk for producers (drought avoidance and early to market)  Optimization of risk-return options can be pursued using portfolio theory where the cost of risk reduction associated with a producer moving from a return-maximizing MG × PD combination to a planting portfolio with less risk can be quantified by estimating an efficient frontier where returns are maximized subject to a given level of risk  Data from planting date trials using soybean from MG III to VI across nine locations are used to show risk-return "tradeoffs" for MG and PD Objectives  Demonstrate production risk reduction by diversifying from the profitmaximizing MG × PD choice to a portfolio of several MG × PD  Illustrate similarities and differences in risk-return tradeoffs across nine locations with variation in production environment Methods  Using yield, harvest week, oil and protein concentration, irrigation amount and other production cost that did not vary by location, MG or PD, producer returns were estimated for approx. 7,250 plot obs. Sixteen MG × PD choices were aggregated across years by location (A)  Possible portfolio risk among sixteen MG × PD choices was minimized using quadratic programing and an efficient frontier was mapped (B)  A mid-variance point on the efficient frontier, V MID , was solved for to compare risk reduction costs across location Data  Seven locations in '12 and nine locations in '13 & '14  Four cultivars per MG III, IV, V and VI at each location and each year  Location latitudes ranged from 30.6°N to 38.9°N  Four PD with two middle PD spaced as evenly as possible between earliest and latest PD typical PD for a particular location  Soil water deficits calculated using weather data from each location, with soil-specific deficit thresholds trigger irrigation applied  Seed yield, oil and protein concentration are tracked to measure quantity and quality of production in conjunction with a seasonally adjusted 10 yr avg soybean, soybean oil and meal prices. Results & Discussion Observations reaching harvest maturity before the 37 th week of the year received a premium based on seasonal price effect Standard Deviation of E a or in $ ha-1 D Efficient Frontier and V MID for St. Joseph, LA 2012-2014 V MID  For the most part, early-planted MG III and IV were more profitable and had higher average oil and protein premia than later-planted combinations of later maturing MG  Selecting two to six different MG × PD to reduce risk could result in a substantial reduction of risk at relatively lower cost than choosing a less risk single MG × PD choice Future Work  Interactive decision tool utilizing simulated data with multiple constraints to make recommendations across a greater range of choices  Include effects of seed ...