Previous Crop Impacts Winter Wheat Sowing Dates, Available Water at Sowing, and Grain Yield

Cropping systems choices can directly affect the sowing date for winter wheat, which is among the most important variables that determine attainable yields in the U.S. Central Great Plains. Our objective was to investigate the effect of the previous crop on winter wheat grain yield through the modulation of sowing date and its impact on plant available water at sowing, and temperatures during the critical period for yield determination. A no-tillage rainfed field experiment was established in 2019 at Ashland Bottoms, KS. Winter wheat was sown either after summer fallow, full-season soybean, double-cropped soybean, or corn—thus, resulting in a range in sowing dates of 270–326 days of the year (September 27 to November 22). The optimum sowing date for the site based on grain yield was estimated at day of year 296 ± 5 (October 18 to 28). Winter wheat after summer fallow and after a fullseason soybean crop resulted in the greatest yields, whether sown at the optimum date or slightly later than optimum. Winter wheat yield was positively related to plant available water at sowing. Later sowing dates were most likely to reduce plant available water at sowing, and could delay wheat’s development resulting in higher temperatures occurring during the critical period for yield determination (i.e., the days surrounding anthesis). Later sowing also shortened grain filling duration due to an overall later cycle and elevated temperatures. Thus, adjusting winter wheat sowing dates is the first step that determines the crop’s yield potential through improved plant available water at sowing, and reduced temperatures during the critical period for yield determination. When following a summer crop, winter wheat should be sown as soon as the previous crop is harvested to try to mitigate these negative effects of late sowing

Preliminary Classification of Soil, Plant, and Residue Cover Using Convolutional Neural Networks

In agricultural fields, knowledge about the proportion of the soil surface covered with crop residue and vegetation canopy is key for improving soil and water conservation practices. In this study we trained a deep convolutional neural network to automate the classification of bare soil, crop stubble, and live vegetation from downward-facing images of agricultural fields. A comprehensive generic dataset, consisting of 3300 training and 645 test images, was collected from agricultural fields across Kansas State University Agricultural Experiment Stations and the Natural Resources Conservation Service Plant Material Center located near Manhattan, KS. Despite the intricate patterns and color textures resulting from different combinations of soil, canopy, and stubble the trained network showed good performance for automating the classification of land cover from images. The network achieved 87% accuracy over the training dataset and 84% accuracy over the test set

Environment and Nitrogen Rate Play Significant Roles in Winter Wheat Response to Nitrogen Management Intensification

Poor nitrogen (N) management is among the leading causes of winter wheat (Triticum aestivum L.) yield gaps in Kansas, and sowing date—which is impacted by crop rotation—is among the most important variables determining winter wheat’s attainable yields in the U.S. central Great Plains. This research aimed to investigate the relationship between N management strategies and various cropping systems in Kansas. The treatments consisted of nine combinations of three N management practices (standard, progressive, and green N) and five crop sequences (WtWt = continuous winter wheat; SyWt = winter wheat after soybean; TrSyWt = triticale (hay) – soybean – winter wheat rotation; CpWt = winter wheat after cowpea; TpDPwt = dual-purpose winter wheat after tepary bean; MoDPwt = dual-purpose winter wheat after moth bean). Standard N-management consisted of one single broadcast N application at 80 lb/a as UAN at spring greenup. Progressive N-management consisted of a split-N application at 40 and 27 lb/a each at greenup and jointing, using streamer bar nozzles and N-inhibitors added to the fertilizer. Green N management consisted of no fertilizer application except for the carryover N from the previous terminated legume crop. Crop sequences that allowed winter wheat to be sown at the optimum sowing date had the greatest yields. Green N management decreased dual-purpose winter wheat grain yield and shoot biomass. Both standard and progressive N management practices had similar results within crop sequences. Overall, our results suggested that intensive N management produced the same yields as the standard, but at lower N rates. Dual-purpose winter wheat combined with green N (i.e., relying exclusively on carryover N) was detrimental to winter wheat yield

Timing, Source, and Placement of Nitrogen Fertilizer Increases Wheat Yield and Protein Content in High Yielding Environments

The efficiency of nitrogen (N) fertilizer management depends on rate, timing, placement, and source, but the benefits of an integrated program have not been clearly quantified, to our knowledge. This study aimed to investigate the effects of integrated N management on winter wheat grain yield, grain protein content, grain test weight, and biomass in Kansas. The study consisted of two N management treatments: Normal (single N application as UAN using broadcast nozzles with the absence of urea inhibitors); and Progressive (split N application into two timings using streamer bars with urease inhibitors). Both treatments had similar results in all variables measured at Hutchinson, which was the lowest yielding location. In Ashland Bottoms, the number of heads/ft2 and total aboveground biomass did not differ significantly between the treatments. However, grain yield, grain test weight, and protein content were significantly greater in the progressive N management. These results demonstrate the enhanced N use efficiency (NUE) of progressive N management in higher-yielding environments by better N allocation in the plant. This research demonstrates that it is possible to increase both grain protein content and grain yield in high rainfall areas without extra amounts of N fertilizer

Value of Fungicide Application in Wheat Production in Southwest Kansas

During the past several years, applying fungicide to wheat has become a more common practice. The availability of cost-effective generic fungicides, as well as the positive yield responses often reported, seem to be the potential drivers for the adoption of such practices by producers. We conducted a wheat fungicide trial in Garden City, KS, to answer the following questions: 1) Do fungicide applications pay? And 2) Can remote sensing technology be used to quantify the efficacy of different fungicide products? The study consisted of two wheat varieties sown on September 29, 2015 (Oakley CL, highly resistant to stripe rust; and TAM 11, highly susceptible to stripe rust), different fungicide products and different times of application. Stripe rust was the major fungal disease impacting wheat yield in southwest Kansas in 2015-16. Fungicide application increased grain yield over the control for all fungicide products. The greatest grain yield resulted from the application of Tebustar. These results suggest that there could be some potential benefits to early season application of fungicide in southwest Kansas, although the majority of the grain yield gain comes from the flag leaf application. Additional years of data are required to make more robust, meaningful interpretations

Value of Fungicide Application in Wheat Production in Southwest Kansas, 2017 Report

During the past several years, applying fungicide to wheat has become a more common practice. The availability of cost-effective generic fungicides, as well as the positive yield responses often reported, seem to be the potential drivers for the adoption of such prac­tices by producers. A wheat fungicide trial was conducted in Garden City, KS, to answer the following questions: 1) Are fungicide applications profitable? and 2) Can remote sensing technology be used to quantify the efficacy of different fungicide products? The study consisted of two wheat varieties sown on September 30, 2016 (Oakley CL, highly resistant to stripe rust; and TAM 111, highly susceptible to stripe rust) and treated with different fungicide products. Stripe and leaf rust were the major fungal diseases impact­ing wheat yield in southwest Kansas in 2017. Wheat production in 2017 was impacted by dry planting conditions in late 2016, a winter ice storm in January, and a late snow storm on April 30, and severe wheat streak mosaic virus infestation. There were signifi­cant differences in grain yield among fungicide products for both TAM 111 and Oakley CL. The large changes in normalized difference vegetation index (NDVI) values suggest that multiple environmental factors were interacting to impact the wheat plant health. The benefit of fungicide application observed on yield was minimal under the environ­mental conditions of 2017

Manual de identificação de plantas daninhas da cultura da soja.

Família Amaranthaceae: Alternanthera tenella; Amaranthus deflexus; Amaranthus hybridus; Amaranthus spinosus; Amaranthus viridis. Família Asteraceae (Compositae): Acanthospermum hispidum; Acanthospermum australe; Ageratum conyzoides; Emilia sonchifolia; Conyza spp.; Bidens spp.; Galinsoga parviflora; Melampodium perfoliatum; Parthenium hysterophorus; Porophyllum ruderale; Senecio brasiliensis; Siegesbeckia orientalis; Sonchus oleraceus; Tridax procumbens; Família Brassicaceae: Coronopus didymus; Raphanus raphanistrum; Família Commelinaceae: Commelina benghalensis; Murdannia nudiflora. Família Convolvulaceae: Ipomoea grandifolia; Ipomoea nil; Ipomoea purpúrea; Família Euphorbiaceae: Chamaesyce hirta; Chamaesyce hyssopifolia; Croton glandulosus; Euphorbia heterophylla; Phyllanthus tenellus. Família Fabaceae; Desmodium tortuosum; Senna obtusifolia. Família Lamiaceae: Hyptys suaveolens; Leonotis nepetifolia; Leonurus sibiricus; Família Malvaceae; Sida rhombifolia. Família Poaceae: Avena fátua; Brachiaria brizantha; Brachiaria decumbens; Brachiaria plantaginea; Cenchrus echinatus; Chloris spp.; Digitaria insularis; Digitaria spp.; Echinochloa colonum; Eleusine indica; Lolium multiflorum; Panicum maximum; Pennisetum setosum; Rhynchelytrum repens; Setaria geniculata; Sorghum halepense; Família Portulacaceae: Portulaca oleracea; Talinum paniculatum. Família Rubiaceae: Richardia brasiliensis; Spermacoce latifólia; Spermacoce vertticilata; Família Sapindaceae Cardiospermum halicacabum; Família Solanaceae ; Nicandra physaloides; Solanum americanum. Plantas Daninhas resistentes a herbicidas. Espécies resistentes a herbicidas de diferentes mecanismos de ação, relatadas no Brasil. Glossário de termos botânicos.bitstream/item/126196/1/manual-de-identificacao-de-plantas-daninhas.pdf2. ed

Temperature-Driven Developmental Modulation of Yield Response to Nitrogen in Wheat and Maize

Nitrogen management is central to the economic and environmental dimensions of agricultural sustainability. Yield response to nitrogen fertilisation results from multiple interacting factors. Theoretical frameworks are lagging for the interaction between nitrogen and air temperature, the focus of this study. We analyse the relation between yield response to nitrogen fertiliser and air temperature in the critical period of yield formation for spring wheat in Australia, winter wheat in the US, and maize in both the US and Argentina. Our framework assumes (i) yield response to nitrogen fertiliser is primarily related to grain number per m2, (ii) grain number is a function of three traits: the duration of the critical period, growth rate during the critical period, and reproductive allocation, and (iii) all three traits vary non-linearly with temperature. We show that “high” nitrogen supply may be positive, neutral, or negative for yield under “high” temperature, depending on the part of the response curve captured experimentally. The relationship between yield response to nitrogen and mean temperature in the critical period was strong in wheat and weak in maize. Negative associations for both spring wheat in Australia and winter wheat with low initial soil nitrogen ( 120 kg N ha-1) that favoured grain number and compromised grain fill, the relation between yield response to nitrogen and temperature was positive for winter wheat. The framework is particularly insightful where data did not match predictions; a non-linear function integrating development, carbon assimilation and reproductive partitioning bounded the pooled data for maize in the US and Argentina, where water regime, previous crop, and soil nitrogen overrode the effect of temperature on yield response to nitrogen fertilisation.Fil: Sadras, Victor O.. University of Adelaide; Australia. South Australian Research And Development Institute; AustraliaFil: Giordano, Nicolas. Kansas State University; Estados UnidosFil: Correndo, Adrian. Kansas State University; Estados UnidosFil: Cossani, C. Mariano. University of Adelaide; Australia. South Australian Research And Development Institute; AustraliaFil: Ferreyra, Juan M.. No especifíca;Fil: Caviglia, Octavio Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Coulter, Jeffrey A.. University of Minnesota; Estados UnidosFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados UnidosFil: Lollato, Romulo P.. Kansas State University; Estados Unido

Wheat Variety-Specific Response to Seeding Rate Under Intensive Management Conditions in Western Kansas in 2021–2022

Wheat response to seeding rate is variable and depends on resource availability during the growing season (e.g., fertility, moisture, and temperature). Our objective was to evaluate winter wheat population and grain yield responses to seeding rate and its interaction with variety in a highly-managed production system where manageable stresses were limited. This study was established to evaluate the response of the wheat varieties Joe, WB-Grainfield, Langin, and LCS Revere to five seeding rates ranging from 200,000 to 1,000,000 seeds per acre. The site was managed by growers who consistently win state and national wheat yield contests near Leoti, KS. The trial was established on September 25, 2021, after a long summer fallow in sorghum residue. A total of 0.75-in. rainfall surrounding sowing ensured good stand establishment. The entire growing season was dry, limiting grain yield to the 40 to 66 bu/a range, depending on treatment. There were significant effects of seeding rate and variety on stand count, with no interaction. Main effects suggested that the stand count increased with increases in the seeding rate (from 205,795 to 658,544 plants per acre), with the 800,000 and 1,000,000 seeds/a rates attaining the highest stands. WB-Grainfield had the greatest population (522,586 plants per acre), which was statistically greater than that of Langin (412,121 plants per acre) but similar to the other two varieties with intermediate population. Final populations were closer to the target population at lower seeding rates as compared to higher seeding rates. Grain yield also depended primarily on variety and on seeding rate, with no interaction between both effects. Grain yield ranged between 56.9 and 58.2 bu/a acre for the seeding rates ranging between 600,000 and 1,000,000 seeds/a, and from 49.3 to 55.0 bu/a for lower seeding rates. Langin and WB-Grainfield were the highest yielding varieties (57.6 bu/a), and LCS Revere and Joe had the lowest yield (53.1 bu/a). These results suggest that wheat grain yield responses to seeding rate were not dependent on variety, with optimum seeding rates as low as 600,000 seeds/a. We note that increasing seeding rates beyond 600,000 seeds/a led to numerical but not statistical increases in yield

Manual de identificação de plantas daninhas da cultura da soja.

Família Amaranthaceae Altemanthera tenella; Amaranthus deflexus; Amaranthus hybridus; Amaranthus spinosus; Amaranthus viridis; Família Asteraceae (Compositae); Acanthospermum hispidum; Acanthospermum australe; Ageratum conyzoides; Bidens spp.; Conyza spp.; Emilia sonchifolia; Galinsoga parviflora; Melampodium perfoliatum; Parthenium hysterophorus; Porophyllum ruderale; Senecio brasiliensis; Siegesbeckia orientelis; Sonchus oleraceus; Tridax procumbens; Família Brassicaceae; Coronopus didymus; Raphanus raphanistrum; Família Commelinaceae; Commelina benghalensis; Família Convolvulaceae; Ipomoea grandifolia; Ipomoea nil; Ipomoea purpúrea; Família Euphorbiaceae; Chamaesyce hirta; Chamaesyce hyssopifolia; Croton glandulosus; Euphorbia heterophylla; Phyllanthus tenellus; Família Fabaceae; Desmodium tortuosum; Senna obtusifolia; Família Lamiaceae; Hyptys suaveolens; Leonotis nepetifolia; Leonurus sibiricus; Família Malvaceae; Sida rhombifolia; Família Poaceae; Brachiaria brizantha; Brachiaria decumbens; Brachiaria plantaginea; Cenchrus echinatus; Chloris spp.; Digitaria spp.; Digitaria insularis; Echinochloa colonum; Eleusine indica; Panicum maximum; Pennisetum setosum; Rhynchelytrum repens; Setaria geniculata; Sorghum halepense; Família Portulacaceae; Portulaca oleracea; Talinum paniculatum; Família Rubiaceae; Richardia brasiliensis; Spermacoce latifólia; Família Sapindaceae; Cardiospermum halicacabum; Família Solanaceae; Nicandra physaloides; Solanum americanum; Glossário de termos botânicos.bitstream/item/58161/1/Documentos-274.pd