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Grain Sorghum (Sorghum bicolor) is one of the most drought and stress tolerant crops grown in Kansas. For this reason, much of the sorghum is grown in high risk environments where other crops are more likely to fail or be unprofitable. Efficient sorghum cropping systems should not only produce high yields and use inputs such as nitrogen efficiently, but they should also remove as much risk as possible for a successful crop, and give farmers more flexibility in making input decisions. The price of nitrogen (N) fertilizer has increased substantially in recent years. Current retail prices for commonly used N fertilizers range from $0.88 to $1.50 per kilogram of N in Kansas. Thus, a farmer could easily invest $50-$100 per hectare in N, depending on the rate of N needed and the source used. Practices which allow farmers to assess crop potential as late as possible after planting before applying costly inputs like fertilizer, can increase the potential for a profitable return on those inputs in risky environments. Currently, most sorghum growers routinely apply all the N fertilizer prior to planting, sometimes as much as 6 months prior. The current Kansas State University (KSU) nitrogen recommendation is yield goal based and performs well when the grower is able to predict yield six months or more in advance of harvest. However, yield is quite variable and difficult to predict. Because long range weather and yield predictions are not very reliable, could deferring making N application decisions until later in the season when yield can be more accurately predicted reduce risk? Can the use of active sensors provide a better estimate of yield potential and nitrogen needs sometime after planting? If they can, how late can the decision be made and how best should the fertilizer N be applied? Several studies were conducted throughout Kansas to look at the effect of N rate, N application timing (pre-plant, side dress, or combinations of the two) and method of application on sorghum yield and N use efficiency. The studies were also designed to examine the potential of using optical sensors to predict optimum N rate for post-planting applications as a means of avoiding the use of soil tests to estimate soil N contributions. The objectives of this research were: a. to validate the KSU N fertilizer recommendations for grain sorghum grown in rotation with crops such as soybeans and wheat, b. to determine the effect of both preplant and midseason N applications on the growth and yield potential of grain sorghum, and to determine the optimal timing and method for midseason N applications on grain sorghum, and, c. to assess the potential of optical sensing of the growing crop to refine N recommendations using in-season applications during the growing season. This thesis will summarize the results from the various experiments we completed to achieve these objectives. The KSU N fertilizer recommendations for grain sorghum may need some revisions. This research suggests that including coefficients relating to N use efficiency may be necessary to get more accurate N recommendations. Both pre-plant and midseason N applications increased the yield of grain sorghum whenever a response to N was observed. There was no negative effect of applying all the nitrogen midseason at 30-40 days after planting when compared to pre-plant applications. Injecting nitrogen fertilizer below the soil surface had higher yields than other methods of midseason N applications such as surface banding or surface broadcasting, especially when a significant rainfall event did not occur within a few days of application. The optical sensors used in this study were very effective at making N recommendations 30-40 days after planting. These sensors will provide for more accurate N recommendations compared to the current soil test and yield goal method.
Cover crops slow erosion, improve soil, smother weeds, enhance nutrient and moisture availability, help control many pests and bring a host of other benefits to your farm. At the same time, they can reduce costs, increase profits and even create new sources of income. You¿ll reap dividends on your cover crop investments for years, since their benefits accumulate over the long term. This book will help you find which ones are right for you. Captures farmer and other research results from the past ten years. The authors verified the info. from the 2nd ed., added new results and updated farmer profiles and research data, and added 2 chap. Includes maps and charts, detailed narratives about individual cover crop species, and chap. about aspects of cover cropping.
The objectives of this study were 1) to describe the yield of continuous grain sorghum in the Texas Blackland Prairie as a function of applied nitrogen (N) and degree of water deficit under field conditions and 2) to use this information to obtain a more accurate N fertilizer recommendation in the Texas Blackland Prairie based on a knowledge of available soil water at the time of fertilizer application. An economic analysis described by Heady (1956) was used to compute optimum N rates under different degreed of soil water deficit. Water deficit was defined in terms of stress days, computed by summing the term (1-E/E) over daly intervals, where E is daily evapotranspiration rate an E is the daily potential evapotranspiration rate above the plant canopy. Cumputer simulation of soil water (...).
Variable-rate (VR) nitrogen (N) applications have the potential to improve efficiency of grain sorghum production. Field experiments were conducted in 2010 and 2011 in Stockton and Manhattan, KS. Four VR-N prescriptions were generated using various combinations of grid soil sampling data, soil electrical conductivity (EC) data, and yield maps, and were compared in the field with a uniform application based on a composite soil sample and whole field average yield goal. Soil EC data were used to create management zones that were individually soil sampled. Prescriptions were applied before planting and grain sorghum was harvested and recorded with a yield monitor in the fall. Grain sorghum yields responded to N at both sites with a higher response in 2010 due to more precipitation during the growing season. At Stockton in both years, greatest yields and returns were realized with prescription 4, a combination of management zone soil data and spatially-variable yield goal, while the smallest yields were realized with prescription 2 based on management zone soil data and field average yield goal. Prescription 5, which used grid-soil sampling and a spatially-variable yield goal, and prescription 2 resulted in the lowest returns in both years. At Manhattan in both years, greatest yields and returns were realized with prescription 3, combining a composite soil sample with spatially-variable yield goal. Prescription 5 was among the lowest returning treatments in both years. At Stockton, there was no correlation between yield and soil EC during the 2010 growing season, however there was a significant correlation between yield and shallow EC during the drier 2011 season. At Manhattan, yield was correlated to deep EC in 2010 and to shallow EC in 2011. Overall, increasing spatial intensity of data to develop the prescriptions did not necessarily result in an increased yield response to the application. Prescriptions that included a variable yield goal component tended to perform better across both sites and years.