There are many pathogens that can cause disease in field crops grown in Illinois. However, diseases are not problematic in every field in every year. This is because several factors are required for a disease to occur. The plant disease triangle outlines the three conditions that are needed in order for disease to occur: a virulent pathogen, a susceptible host, and a conducive environment. The longer these three conditions remain together, the more disease will occur. Disease will not occur when these components are not simultaneously present. Read the chapter.
Water quality can be evaluated in many ways: color, odor, temperature, turbidity, and the presence or absence of bacteria. Pharmaceuticals and personal care products have also been identified in many of the lakes, rivers, and streams in the United States. Current water quality issues in Illinois generally relate to drinking water safety and the need to reduce nutrient loss from agricultural fields. This chapter is organized around those two themes. Read the chapter
agronomy of field crops pdf 11
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Nitrogen often is applied to crops in the north-central region of the U.S. before planting. During the first four to six weeks after planting, corn requires only about 5 percent of the N applied. The following two to four weeks of growth require a large proportion of the total seasonal N requirement because the rate of N uptake dramatically increases past the V6 growth stage (Bender et al., 2013).
The lack of field performance with Instinct could be related to the formulation of the microencapsulation. N-Serve used in anhydrous ammonia performs well due to the complete availability of the nitrapyrin a.i. to affect nitrification at the time of application, as well as the a.i. concentration in the ammonia injection band. Instinct releases more slowly, and the concentration at any one time may be less than that with N-Serve, especially with broadcast application.
Considerable inconsistencies regarding the effectiveness of Instinct were found in laboratory and field trials. The soils in each experiment are known, the rate of a.i. used is known, the conditions of the experiments are known, but the microencapsulation thickness of the material used in the experiments is not known except for the data within the patent release. The Instinct label has no information regarding microencapsulation thickness, so the user must decide whether the release rate after soil application will lead to effectiveness of use.
The results of one study showed that the use of nitrapyrin increased corn plant and grain N concentrations but did not translate into a yield increase (Sawyer et al., 1991). In another study, the use of nitrapyrin was useful in lowering soil nitrite levels in the liquid manure band, which was one reason why poor corn growth was observed in the banded liquid manure fields (Sawyer et al., 1990).
The second step of nitrification (nitrite to nitrate) is usually instantaneous due to the presence of Mn-oxides (Bartlett et al., 2008). The reactions of ATS with Mn-oxides may be the reason for its nitrification inhibition and the increase of nitrite. Adding ATS to UAN has been shown to reduce ammonia volatilization from surface-applied UAN under greenhouse (Goos, 2013, Figure 12) and field (Grant et al., 1996, Figure 13) conditions.
Goos and Johnson (1992) compared the nitrification rates of banded UAN amended with emulsified nitrapyrin (N-Serve 24E), DCD, ATS and ATS+DCD at three field sites. The results are summarized in Figure 14. The nitrapyrin was most effective at slowing nitrification and ATS was least effective.
Products containing calcium heteropolysaccharide, such as N-Zone (AgXplore, Parma, Mo.), claim to reduce loss of N by processes such as leaching. In an incubation study, this product was not shown to slow nitrification when applied to urea granules (Figure 15). NZONE Max addition to urea did not result in a corn yield benefit in a 2015 North Dakota field experiment (Chatterjee, 2015, unpublished report). Although some labels for these products claim to keep N in the ammonium form longer, careful laboratory experiments show that they do not perform as nitrification inhibitors.
Using N extenders and additives has two potential economic benefits: increase yield or decrease the optimum fertilizer N required. Before deciding whether to use an alternative N product or amendment, producers must consider the major N loss mechanism in a particular soil and field.
These products add additional cost to a farming operation, so producers need to give careful thought to selecting the product appropriate for a specific field. In addition, when considering products not explained in this publication, look for independent field trials, preferably conducted by land-grant university researchers, to verify effectiveness and mode of action.
Marburger, David A., Muthusubramanian Venkateshwaran, Shawn P. Conley, Paul D. Esker, Joseph G. Lauer, and Jean-Michel Ané. 2015. Crop rotation and management effect on Fusarium spp. populations. Crop Science 55:365-376. doi: 10.2135/cropsci2014.03.0199
The development of remote sensing has provided opportunities to quantitatively describe agronomic parameter changes across all growth stages of crops. The application of remote sensing to agronomic problems has created new methods to effectively improve field crop management. Many authors have provided detailed information about the relationships between spectral indices and agronomic parameters, including the leaf area index (LAI), nitrogen (N) uptake, total chlorophyll (Chl) content, and so on.
A straight linear relationships between biomass and LAI before heading stages, but this relationship between biomass and LAI have changed after heading stages. The rate of increase in the LAI is less than in the biomass (stem and spike weights increase more than leaf weight) before and after flowering. The LAI arrived at the highest values at flag leaf fully expanded, but the biomass dry weight (BDW) is still increased (it is mainly increased from spike weight). Therefore, a curvilinear relationship between wheat BDW and LAI should be better after heading stages. Metabolic physiology exists different before and after flowering. The wheat metabolic physiology was dominated by nitrogen and supplemented by carbon before flowering, canopy leaf nitrogen content change was relatively small, canopy leaf nitrogen content was accurately estimated by more accurately monitoring LAI. However, the wheat metabolic physiology was dominated by carbon and supplemented by nitrogen after flowering, the differences are significant in canopy leaf nitrogen content, nitrogen transfers from lower leaves to the upper leaves and spike in wheat. The nitrogen was not estimated accurately by monitoring LAI because of the lower leaves nitrogen was moved away by nitrogen transfer, resulting in a relative large error will be generated. If biomass factors are taken into account, thereby the transfer of nitrogen will also be included in the monitoring results, therefore it could be used to improve the nitrogen estimation accuracy. These results indicated that the biomass dry weight was highly related to LAI, nitrogen content and total chlorophyll content because biomass dry weight had a close relationship with LAI, nitrogen content and total chlorophyll content in crops physiological mechanism. These results also suggested that the biomass dry weight could be used to estimate the LAI, nitrogen content and total chlorophyll content for wheat. The results showed that a good relationships among the LAI, nitrogen content, total chlorophyll content and OSAVIBDW (Figs. 1c, 2c and 3c). It provided a basis for BDW was replaced by spectral indices to establish new spectral indices.
To validate the model accuracy, we compared the predicted values (the predicted values were gained by the LAI, N uptake and total Chl content of regression equations in 2009) with the actual values (the actual values were gained by the LAI, N uptake and total Chl content field measurement data in 2010). A good correlation between the predicted values and the actual values was observed for the following indices: OSAVI, biomass dry weight, and OSAVIbiomass dry weight (BDW). The corresponding root mean square errors (RMSEs) were 1.42, 1.02, and 1.12 for LAI; 7.93 g/m2, 4.45 g/m2, and 4.23 g/m2 for nitrogen uptake; 3.42 g/m2, 1.85 g/m2, and 2.23 g/m2 for leaf chlorophyll content, respectively (Table 5). These data indicated that OSAVIbiomass dry weight could be used to improve the estimation accuracy of LAI, nitrogen uptake, and leaf chlorophyll content. The new spectral indices were proposed by replacing BDW with spectral indices, and then obtained OSAVIOSAVI, OSAVISIPI, OSAVICIred edge, OSAVICIgreen model and OSAVIEVI2. The results showed that the new spectral indices were better than OSAVI alone for estimating LAI, nitrogen uptake and total chlorophyll content (Tables 2, 3, 4 and 5 and Figs. 1a, 2a and 3a). The results indicated that the products of spectral indices and OSAVI could be used to improve the LAI, N uptake and total Chl content estimation accuracy.
Taken together, the results indicated that it was feasible to use new methods to improve agronomic parameters (LAI, N uptake and total Chl content) assessment accuracy. This paper evaluated the estimation accuracy of agronomy parameters by multiplying the spectral indices in OSAVI, it showed the OSAVI is unnecessary and able to be replaced by others spectral indices. For example, if you want to better estimate total Chl content or N uptake, you could be selected to the product of two spectral indices are highly related to chlorophyll. Further, we used these indices to improve the accuracy of these predictions for all crop growth stages. In future studies, we will try to multiply two spectral indices that are highly related to LAI, total Chl content or N uptake to estimate agronomic parameters of different crops.
Although farmers have been using cover crops for centuries, today's producers are part of a generation that has little experience with them. As they rediscover the role that cover crops can play in sustainable farming systems, many growers find they lack the experience and information necessary to take advantage of all the potential benefits cover crops can offer. That inexperience can lead to costly mistakes. This guide will help you effectively select, grow, and use cover crops in your farming systems. While this guide isn't the final word on cover crops, it is meant to be a useful reference. 2ff7e9595c
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