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Alfalfa (Medicago sativa L.) hectares in Michigan are declining partly due to reliance on corn (Zea mays L.) silage as a continuous feed source. By interseeding corn and alfalfa, farmers can replace the low alfalfa yield in the establishment year with corn silage while simultaneously establishing alfalfa. A randomized split-block field study was conducted in East Lansing, MI, over 3 yr (2019 to 2021) to determine the critical period of weed control (CPWC) in the interseeded corn and alfalfa system using two corn hybrids with differing leaf architecture (pendulum vs. upright). Whole plots were assigned to corn hybrids interseeded with alfalfa, and subplots were assigned to a surrogate weed, Japanese millet [Echinochloa esculenta (A. Braun) H. Scholz], for the duration of competition treatments. Weed-free and weedy plots were included as controls. At the end of the interseeding year, corn was harvested, while alfalfa was harvested the following year. The CPWC is made up of two components: the critical timing of weed removal (CTWR) and the critical weed-free period (CWFP). Corn hybrid had no impact on the CTWR or CWFP for interseeded corn or alfalfa. Averaged across hybrids, the CTWR was 303 growing degree days (GDD), and CWFP was estimated to be greater than the study duration. The CTWR in the first cutting of alfalfa was estimated to be 369 GDD. The CWFP was estimated to be 394 GDD for a 5% acceptable yield loss for the first alfalfa cutting. Identification of the CPWC in the interseeded system will increase adoption and interest in other interseeded systems that can mitigate potential negative environmental and economic impacts of monoculture agriculture.

Objectives: This release note describes the Maize G x E project datasets within the Genomes to Fields (G2F) Initiative. The Maize G x E project aims to understand genotype by environment (G × E) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available. Data description: The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the G x E coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.

Tar spot of corn, caused by the fungus Phyllachora maydis, is an economically important foliar disease recently reported in the U.S and Canada. Due to the recent introduction of Phyllachora maydis, the impacts of cultural management practices on disease development are still unknown. Separate field studies were conducted to determine effects of nitrogen (N) application rate and plant density on disease development. Field trials were conducted across six site years in Michigan with two corn hybrids of differing disease susceptibility. Relative area under the disease progress curve (rAUDPC) was used to compare disease development between N application rates and plant densities. Nitrogen application rate had no significant effect on disease at any location. Plant density and disease had a significant (P < 0.05) inverse relationship at five of six site years, with an average 41% decrease in rAUDPC for every 1000 plant per hectare increase. The economically optimal planting density ranged from 73 to 77 thousand plants per hectare for $150 to $300 USD per metric ton corn prices, demonstrating relatively low planting densities were more profitable despite greater disease. Therefore, other disease management practices including hybrid selection may be more effective at protecting yield than increasing plant density.

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each data type improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best-performing model revealed that including interactions altered the model's sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have a limited physiological basis for influencing yield—those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied towards this goal. Here we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using linear models, which are the conventional method for this task, and machine learning models We examine the usefulness of incorporating interactions between disparate data types. We find a deep learning model with interactions has the best average performance. Optimizing submodules for each datatype improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best performing model revealed that including interactions altered the model’s sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have limited physiological basis for influencing yield – those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.

Objectives Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F’s genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014–2017. Data description Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

Sustainable production in the Everglades Agricultural Area (EAA) is confounded by decreasing soil depth and high annual rainfall. Formerly a sawgrass marsh, Histosols predominate much of the EAA an...

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

The literature on harvest management in Australia, Brazil and Louisiana indicates positive effects of harvest residue on sugarcane (Saccharum spp. hybrid) growth in well-drained soils with dry climate or high slopes, but negative effects in wet climates and heavy soils with little relief. There is limited information available on harvest management effects in Florida and Costa Rica. Therefore, sugarcane harvest management effects were studied on plant cane and two ratoon crops grown on organic Histosols in Florida and plant cane and three ratoon crops grown on clay loam soils in Costa Rica. The harvest systems included burnt cane harvest (Burnt), green cane harvest (Green), and green cane harvest with residue management. Residue management included either raking residues from the cane rows into the inter-row spaces (Rake; in Florida) or manual removal from the field (Remove; in Costa Rica) immediately after harvest. Sub-treatments were early (Nov.–Dec.) or late (Feb.–Mar.) harvest in Florida. Our results showed a negative effect from maintaining the harvest residue layer on late (>120 days after harvest, DAH) tillering and mid-season (150–200 DAH) leaf area index (LAI) in early harvested cane in Florida. However, the harvest system effects on early to mid season growth were not observed in final sugarcane yield (tons of cane per hectare, TCH), sucrose concentration (commercially recoverable sucrose, CRS) and sugar yield (tons of sugar per hectare, TSH) in Florida. In Costa Rica, TCH and TSH were greater in burnt than remove treatment with no difference between burnt and green. These results indicate that keeping harvest residue on soil surface after green cane harvest may have neutral effects on sugarcane yields in Florida and Costa Rica.

The phenological and growth parameters of CERES-Maize were estimated using data from field variety trials in 2017 and 2018 to simulate hybrid maize (Zea mays L.) grown in Michigan. Multiple calibration methods used include GENCALC (Genotype Coefficient Calculator), GLUE (Generalized Likelihood Uncertainty Estimate), NMCGA (Noisy Monte Carlo Genetic Algorithm) and ensembling approach. Three irrigated sites were used for calibration while six rainfed sites for evaluation. Better results were obtained when using multiple years of data in calibration than using only a single year. Model evaluation also suggests that fixed soil root growth factor (SRGF) used in calibration (irrigated condition) tended to restrict root dynamics under rainfed condition. This resulted in substantial yield mismatch due to poorly modeled yields, although phenology was better predicted. Adjusting SRGF under rainfed condition resulted in better model evaluation for both years. Moreover, weighted averaging of genetic coefficients resulted in better predictions of phenology and yields.

Energycane has been proposed as a potential, perennial bioenergy crop for lignocellulosic-derived fuel production in the United States. Herbicides currently used in sugarcane and other crops can potentially be used in energycane if there is acceptable tolerance. Also, to limit future invasion of energycane escapes, herbicides used for perennial grass control could potentially be used for management of escapes. In container studies conducted outside, aboveground and belowground biomass of energycane was measured to evaluate energycane tolerance to 9 PRE and 19 POST herbicides used in sugarcane and other crops. PRE application of atrazine, diuron, mesotrione, metribuzin, pendimethalin, andS-metolachlor at rates labeled for sugarcane did not significantly injure (< 3%) or reduce energycane biomass compared with the nontreated plants 28 and 56 d after treatment (DAT). Injury from clomazone (54%), flumioxazin (7%), and hexazinone (29%) was observed 28 DAT. Injury from flumioxazin was transient and was not observed at 56 DAT. At 56 DAT, energycane injury increased to 71 and 98%, respectively, for clomazone and hexazinone. Hexazinone and clomazone applied PRE significantly reduced biomass compared with the nontreated plants. At 28 DAT, POST application of 2,4-D amine, ametryn, asulam, atrazine, carfentrazone, dicamba, halosulfuron, mesotrione, metribuzin, and trifloxysulfuron at labeled rates for sugarcane did not injure or significantly reduce energycane biomass compared with the nontreated plants. Injury was observed when clethodim (99%), clomazone (51%), diuron (51%), flumioxazin (21%), glufosinate (84%), glyphosate (100%), hexazinone (100%), paraquat (66%), and sethoxydim (100%) were applied POST, and each of these treatments reduced energycane biomass compared with the nontreated plants. These results show that several PRE and POST herbicides used for weed management in sugarcane may potentially be used in energycane for weed control. Also, based on our results, clethodim, glyphosate, and sethoxydim would be effective for management of energycane escapes.

Sweet sorghum [Sorghum bicolor (L.) Moench] is a promising crop for bioenergy production but is susceptible to lodging, which has the potential to significantly affect biomass composition and total harvestable yield. Despite known susceptibility to lodging, there are limited data available on the effects of root lodging on biomass and carbohydrate yield and partitioning in lodged sweet sorghum. Therefore, weather-induced lodging of field-grown sweet sorghum in 2012 and 2013 in North Central Florida was exploited to provide a better understanding of the effects of root lodging on biomass and carbohydrate yield and partitioning to leaf, stem, and panicle. Fresh biomass yields were not different between lodged and non-lodged plots in 2012 or 2013, but dry biomass yield was lower during 2012. Grain yield was lower during both years of the study, as a result of lodging. However, no difference in leaf or stem biomass yield was seen due to lodging. Lodging reduced biomass recovered by flail harvester at harvest by 40% during 2012. Juice Brix showed a 32 and 17% decline in lodged compared to non-lodged sorghum at harvest during 2012 and 2013, respectively. Total nonstructural carbohydrates (starch and water soluble carbohydrates) showed a 27% reduction on a whole plant basis as a result of lodging during 2013, with panicles showing a greater percent reduction (53 vs. 18%) in total nonstructural carbohydrates (mostly starch) as a result of lodging compared with stems (mostly water soluble carbohydrates). Although sweet sorghum is susceptible to root lodging in many environments, it is likely to have only a relatively modest effect on water-soluble carbohydrates in the stem.

Optimizing nutrient management practices in cropping systems is essential to improve economic and environmentally sustainability. Sweet sorghum [Sorghum bicolor (L.) Moench] is a crop that is capable of high sugar, grain, and lignocellulosic biomass yields, but a synthesis of published nutrient response curves reveals gaps in our knowledge. We therefore evaluated responses of sweet sorghum on a marginal North Florida sandy soil to ranges in nitrogen and potassium application rates and combined the nitrogen data with literature datasets to provide a broadly useful assessment of the relationship between nitrogen uptake and yield. There were no significant effects of the application of potassium observed. In response to nitrogen, leaf SPAD and photosynthesis were less with 20 kg N ha−1 than with more nitrogen (60, 100, and 140 kg ha−1), reflecting end-of-season crop responses in total biomass, estimated sugar, and grain yield. A linear plateau model indicated that application of 70 kg N ha−1 was sufficient to optimize total biomass yield at 20.8 Mg ha−1, although aboveground nitrogen uptake reached 120 kg N ha−1. These results are aligned with other reports on this topic showing similarly small optimal nitrogen fertilization rates and a strong capacity to extract soil nitrogen. In a combined analysis of this and three other published studies, crop nitrogen uptake reached approximately 127 kg ha−1 at the plateau in biomass production (19.7 Mg ha−1) with a slope of 0.13 Mg kg−1. Sustainable management plans for sweet sorghum should include strategies to replenish the often considerable soil nitrogen removed by the crop.

Published in Agron. J. 107:466–474 (2015) doi:10.2134/agronj14.0339 Copyright © 2015 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. ABSTRACT

Late leaf spot (LLS) caused by Cercosporidium personatum (Berk. and Curt.) Deighton leads to significant reductions in peanut (Arachis hypogaea L.) yield worldwide. This study was conducted to improve the mechanisms and methods by which LLS effects on defoliation and photosynthesis are linked to the CSM-CROPGRO-Peanut model for simulating growth and yield reductions in peanut cultivars. Field experiments were conducted in 2008 and 2009 to collect data on the effects of LLS on biomass accumulation and partitioning, leaf necrosis and defoliation, and total canopy photosynthesis (TCP) in peanut cultivars with more (York) and less (Carver) quantitative resistance to LLS. After incorporating LLS damage as defoliation percentage and necrotic area, the model accurately simulated crop growth and development for both cultivars despite different disease dynamics. Simulated TCP and leaf, total crop, and pod yield values were in good agreement with measured data. A modification in the model code to directly reduce leaf photosynthesis and quantum efficiency according to empirical observations resulted in improved simulations of LLS effects on growth and yield. Correlations among measured defoliation and necrotic area with disease ratings indicated that visual disease ratings could be successfully used to estimate necrosis and defoliation for model inputs. Results indicated that the CSM-CROPGRO-Peanut model has adequate capability to simulate LLS effects on growth and yield in peanut cultivars with differing levels of resistance to LLS when inputs on canopy necrotic area and defoliation are provided.

Biomass yield and tissue mineral composition can affect total energy yield potential, conversion efficiencies and environmental impacts, but relatively few data are available for sweet sorghum [Sorghum bicolor (L.) Moench] grown in the southeastern USA. Therefore, a study was conducted at two locations in North and Central Florida on marginal sand soils comparing the effects of planting date (PD) on dry biomass yield and mineral composition of leaf, stem, and grain heads for ‘M-81E’ and ‘Dale’ sweet sorghum cultivars. Overall tissue mineral concentrations were relatively low for sweet sorghum, attributable to low K and Ca concentrations. Ash and mineral concentrations were generally greater for Dale, especially for the early PD. Leaf and grain heads were greater in mineral concentrations compared to stems. Dry biomass yield averaged 19.4 Mg ha−1 and was greater for M-81E and the early PD. Stems accounted for 73% of the total biomass compared to leaves (13%) across all treatments. Total N, P, and K removals averaged 136, 27.6, and 81.4 kg ha−1, respectively. Overall, leaves removed 30, 23, and 19% of total N, P, and K compared to 34, 34, and 61% by stem, respectively. Considering lower biomass but greater mineral concentrations in leaf and grain heads compared to stems, returning leaf residues and possibly grain heads to the soil have the potential to offset nutrient and energy inputs needed on these marginal soils and enhance the sustainability of sweet sorghum cropping systems.

Late leaf spot (LLS) caused by Cercosporidium personatum (Berk. & Curt.) Deighton reduces leaf CO 2 assimilation rate (A sat ) and accelerates leaf defoliation, which together lead to major reduc- tions in peanut (Arachis hypogaea L.) yield world- wide. This study was conducted to determine whether differences in photosynthetic response to LLS severity exist among peanut cultivars of differing resistance. Field experiments were con- ducted in 2008 and 2009 to study the effects of LLS on A sat of tagged leaf cohorts, and photo- synthetic response of similar age leaves to LLS in peanut cultivars with more (York) and less (Carver) quantitative resistance. A nonlinear model, y = (1 − x) β was used to analyze A sat data, where y is relative A sat , x is measured visual lesion area, and β represents the relationship between virtual and visual lesion area. Progression of LLS severity on leaf cohorts was slower in York than in Carver. However, the reduction in A sat with leaf cohort age was similar across the cultivars. This paradox could be explained by a higher β value in York (4.6) than in Carver (3.6), indicating a greater relative reduction in A sat beyond the necrotic lesion area in York. This greater reduction in A sat in York compared to Carver was most closely related to a reduction in maximum carboxylation velocity. Results indicated that future efforts to improve LLS resistance should include sustain- ing A sat in response to LLS infection along with slower disease progress.

Cercosporidium personatum (Berk. & Curt.) Deighton causes late leaf spot (LLS) in peanut (Arachis hypogaea L.), which leads to necrotic lesions, early leaf senescence and yield losses. Detailed physiological analyses can contribute to an improved understanding of peanut-disease interactions and cultivar improvement. A study was conducted evaluating two peanut cultivars with more (York) and less (Carver) quantitative resistance to C. personatum grown under fungicide-sprayed and nonsprayed conditions in the field at Citra, FL over 2 yr. Data were collected on disease severity using the Florida 1 to 10 visual rating scale and by direct measurement of percent canopy lesion area. Leaflifespan, total canopy photosynthesis (TCP), plant growth, and pod yield were also measured. Disease severity based on canopy lesion area was reduced by 30% in York compared to Carver. No additive effects of combining the resistant cultivar with fungicide were seen, as fungicide use increased yield by 364 kg ha -1 for both cultivars. Yield was more strongly related to disease severity based on canopy lesion area than to the Florida scale. Yield improvement with York was not as closely related to disease severity with only a 6% gain in pod yield in York compared to Carver. In addition, reduction in TCP was greater in York compared to Carver given their respective disease severity. These results indicated that combining resistance with the maintenance of physiological function during LLS infection could result in improved peanut yields under diseased conditions.