Progress 12/15/01 to 12/31/04
Outputs
Given the large number of options available for each agronomic decision, producers must be very careful to select management strategies that increase profitability and do not adversely affect the environment. The decision making process is complicated because tillage, rotations, nutrient and pest management strategies, and spatially dependent soil chemical, biological, and physical properties interact to influence yield. Failure to account for interactions can result in less than optimal recommendations. The hypotheses of this project was that through improved understanding of the causes of nutrient, pest and crop yield spatial and temporal variability, agricultural management strategies can be better implemented. Field and simulation experiments were conducted. In simulation experiments, CROPGRO-soybean was used to measure optimum yields and predict causes of yields reduction. Optimum yields in 1999 and 2000 were approximately 3.9 Mg/ha, and water stress reduced
yields on average 1.3 and 0.8 Mg/ha, respectively. Measured and predicted yields were highly correlated to each other (r=0.89). To assist in evaluating the causes of yield variability two new techniques were developed. First to quantify N and water stress over landscapes an approach based on C-13 isotopic discrimination in C3 and C4 plants was developed. Second, to assess microbial activity and C mineralization at different landscape positions, an approach that uses changes in the C-13 discrimination in soil organic C (SOC) to determine SOC turnover and amount of new C incorporated into SOC was developed. Using these technques, field studies showed that: (i) from 1995 to 2003, 14.6 % and 9.0 % of SOC measure in 1995 (SOC95) was mineralized in footslope and summit areas, respectively; (ii) weed shifts occurred after roundup-ready soybeans were introduced; (iii) from year to year the same weeds showed up at the same locations; (iv) remote sensing can be used to detect weeds, predict
yields, distinguish between N and water stress in corn; (iv) N and P recommendation could be improved 59% by sampling old homesteads separately from the rest of the field, using landscape specific yield goals, and grid-cell (4 ha cells) soil sampling; and (v) weeds (barnyardgrass, redroot pig weed, and velvet leaf) that emerged after V1 in soybean did not reduce yields and had low fecundity. Findings from this study can be used to improve agronomic recommendation and reduce the impacts of agriculture on the environment.
Impacts
Producers attending training sessions typically ask, what is the best approach for identifying management zone for nutrient management. This project developed an approach for assessing management zone boundaries. This approach can be used to assess zone boundaries for a variety of situations including, identifying zone boundaries for weeds, insects, diseases, and nutrients.
Publications
- Clay, S.A., J. Chang, D.E. Clay, C. Reese, and K. Dalsted. 2004. SSMG-41. Using Remote Sensing to Develop Weed Management Zones in Soybeans. Clay et al. (Ed) Site Specific Management Guidelines. Potash and Phosphate Institute. Norcross, GA.
- Ellsbury, M.M., S.A. Clay, D.E. Clay, and D.D. Malo. 2005. Within-field spatial variation of northern corn rootworm distributions. P 145-154, S. Videl et al. (ed.). Western Corn Rootworm: Ecology and Management. CAB International. Oxfordshire UK.
- Chang, J., S.A. Clay, and D.E. Clay. 2004. Detecting weed free and weed infested areas of a soybean (Glycine max) field using NIR reflectance data. Weed Sci. 52:642-648.
- Clay, D.E., S.A. Clay, D.J. Lyon, and J.M. Blumenthal. 2005. Can 13C discrimination in corn (Zea mays) grain be used to characterize intra-plant competition for water and nitrogen? Weed Sci. 53:23-29.
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Progress 01/01/03 to 12/31/03
Outputs
Three general approaches (explain the variability of weed, insect,or nutrients in the field; explain yield variability, and minimize recommendation errors) have been used to identify weed, insect,and nutrient management zone boundaries. In whole fields it is very difficult to quantitatively evaluate the impact of management zone boundaries on recommendations. The objective of this example was to determine the influence of different approaches to define nutrient management zones and yield goals on minimizing yield variability and fertilizer recommendation errors. This study used soil nutrient and yield information collected from two east central South Dakota fields between 1995 and 2000. The crop rotation was corn (Zea mays L.) followed by soybean (Glycine max L.). The four management zone delineation approaches tested were to: (i) sample areas impacted by old homesteads separately from the rest of the field; (ii) separate the field into grid-cells; (iii) use of
geographic information systems (GIS) or cluster analysis of apparent electrical conductivity (ECa), elevation, aspect, and connectedness to identify zones; and (iv) use of the Order 1 soil survey. South Dakota fertilizer N and P recommendations were used to calculate fertilizer requirements. This study showed that: (i) when compared to whole field sampling soil, sampling areas impacted by prior management separately from the rest of the field reduced P sampling variability 35%; (ii) when compared to whole field soil sampling, using 10-ha grid-cell reduced nitrate-N sampling variability 48%; and (iii) when compared to conventional N and P fertilizer recommendations, using landscape specific yield goals and 10-ha grid-cell soil sampling improved N and P recommendations 59 and 35%, respectively.
Impacts
Producers attending training sessions typically ask, what is the best approach for identifying management zone for nutrient management. This project developed an approach for assessing management zone boundaries. This approach can be used to assess zone boundaries for a variety of situations including, identifying zone boundaries for weeds, insects, diseases, and nutrients.
Publications
- Clay, D.E., J. Chang, C. Reese, and S. Christopherson. 2002. Landscape position influence on soybean quality. 2002. In Robert et al (eds.). Proceedings of the 6th International Conference of Precision Agriculture July 14-17 2002, Minneapolis MN.
- Chang, J., D.E. Clay, C.G. Carlson, S.A. Clay, and D.D. Malo. 2002. The influence of different classification approaches on N an P fertilizer recommendations. Proceedings of the 6th International Conference of Precision Agriculture July 14-17 2002, Minneapolis MN.
- Chang, J. 2002. Identifying management zones using soil, crop, and remote sensing information. Ph.D. Thesis, South Dakota State University, Brookings, SD
- Chang, J. D. E. Clay, C. G. Carlson, C. L. Reese, S. A. Clay, and M.M. Ellsbury 2004. The Influence of different approaches to define yield goals and management zones on N and P fertilizer recommendations errors. Agron. J. (In press).
- Gaspar, P., C.G. Carlson, and D.E. Clay. 2003. A Cookbook approach for determining the point of maximum economic return. SSMG 39. Clay et al. (Ed) Site Specific Management Guidelines. Potash and Phosphate Institute. Norcross, GA.
- Paz, J.O., W.D. Batchelor, D.E. Clay, S.A. Clay, and C. Reese. 2003. Characterization of Soybean Yield Variability Using Crop Growth Models and 13C Discrimination. ASAE meeting presentation # 033044.
- Clay, D.E., S.A. Clay, J. Jackson, K. Dalsted, C. Reese, Z. Liu, D.D. Malo, and C.G. Carlson. 2003. C13 discrimination can be used to evaluate soybean yield variability. Agron. J. 95:430-435.
- Clay, D. E., Kitchen, N., Carlson, C. G., Kleinjan, J. L., and Tjentland, W. A. 2002. Collecting representative soil samples for N and P fertilizer recommendations. Online. Crop Management doi:10.1094/CM-2002-12XX-01-MA.
- Jackson, J., S.A. Clay, and D.E. Clay. 2002. Influence of landscape position and weeds on water stress in soybean. In Robert et al (eds.). Proceeding of 6th International Conference of Precision Agriculture July 14-17 2002, Minneapolis MN
- Clay, D.E., S.A. Clay, C. Reese, and C.G. Carlson. 2002. Using remote sensing and C13 discrimination to understand yield variability. In Robert et al (eds.). Proceeding of the 6th International Conference of Precision Agriculture July 14-17 2002, Minneapolis MN.
- Sudduth, K.A., N.R. Kitchen, W.D. Batchelor, G.A. Bollero, D.G. Bullock, D.E. Clay, H.L. Palm, F.J. Pierce, R.T. Schuler, K. Thelen, and W.J. Wiebold. 2002. In Robert et al (eds.). Characterizing field-scale soil variability across the Midwest with electrical conductivity. Proceedings of the 6th International Conference of Precision Agriculture July 14-17 2002, Minneapolis MN.
- Drummond, S.T., K.A. Sudduth, N.R. Kitchen, W.D. Batchelor, G.A. Bollero, D.G. Bullock, D.E. Clay, H.L. Palm, F.J. Pierce, R.T. Schuler, K. Thelen, and W.J. Wiebold. 2002. In Robert et al (eds.). Neural network analysis of site-specific soil, landscape and yield data. p. 59. Proceedings of the 6th International Conference of Precision Agriculture July 14-17 2002, Minneapolis MN..
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Progress 01/01/02 to 12/31/02
Outputs
We believe that production efficiency and perhaps absolute production can be increased through improved management. Traditional management targets the field average for nutrient and pesticide recommendations. However, using traditional management, less than optimal crop management may occur in many field areas. Technology is available where agrichemical treatments can be targeted to specific areas without providing excesses or deficiencies. What is missing is the knowledge to rapidly convert spatial information into decisions. The objective of this study was to develop knowledge that can be used to convert information into improved decisions. In the first year of this we investigated the feasibility of use the simulation model to model soybean yields in South Dakota and we initiated studies that were designed to evaluate the factors responsible for weed spatial variability. The research showed that the simulation model could explain between 72 to 81% of the measured
soybean variability in production fields. The model identified that the primary factor responsible for soybean yield variability was either too little or two much water. These results were consistent with experimental results which showed that yield reductions in summit/shoulder areas of up to 40% resulted from water stress.
Impacts
Through an improved understanding of weed biology and soil fertility the mechanisms responsible for weed- induced yield losses in crop plants can be identified and with this knowledge integrated weed and soil nutrient management strategies can be better implemented
Publications
- Sadler, E. J., E. M. Barnes, W. D. Batchelor, J. Paz, and A. Irmak. 2002. Addressing spatial and temporal variability in crop model applications. In L. R. Ahuja, L. Ma and T. Howell (ed.) Agricultural System Models in Field Research and Technology Transfer. Lewis Publishers, Inc., Boca Raton, FL. pp 253-263.
- Irmak, A. W.D. Batchelor, J.W.Jones, S. Irmak, J.O. Paz and H. Beck. 2002. Relationship between plant available soil water and yield for explaining within-field soybean yield variability. Applied Engineering in Agriculture 18(4):471-482.
- Liu, Z., S.A. Clay, and D.E. Clay. 2002. Spatial variability of atrazine and alachlor efficacy and mineralization in an eastern South Dakota field. Weed Sci. 50:662-671.
- Retrum, J.A. and F. Forcella. 2002. Giant foxtail (Setaria faberi) seedling bioassay for resistance to sethoxydim. Weed Technology 16: 464-466.
- Irmak, A., J.W. Jones, W. D. Batchelor and J. O. Paz. 2002. Linking Multiple Layers of Information for Diagnosing Causes of Spatial Yield Variability in Soybean. Transactions of the ASAE 45(3):839-849.
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