Soybean Yields in Illinois - Case Study Example

The aim of this paper is to estimate the factors that contribute to soybean yields in Illinois, United s based on five different locations through a five-year timespan. Illinois crop performance tests are arranged to provide farmers, companies and the government with accurate and valuable agronomic information each year. The results of performance tests can help farmers in choosing their favourable varieties considering their farming conditions. Illinois became US’s leading soybean state in soybean production, which has passed North Carolina since 1924.

The five locations that have been chosen from five different regions in Illinois includes Erie (region 1), Monmouth (region 2), Urbana (region 3), Belleville (region 4), and Harrisburg (region 5). Some soybean seeds have been developed that are high in protein and some varieties have been developed that are high in oil. Soybeans can produce much more protein per acre than any other grain crops (National Soybean Research Laboratory). There are several factors that can be used to identify soybean yields that can be found from Illinois commercial soybeans performance test results, which include: rainfalls, temperature, planting date, maturity date, row spacing, soil type, fertilizer applied, genetic traits, soybean cyst nematode level, and growing degree days etc (Variety Testing Department of Crop Sciences).

Four models have been chosen here for illustrating the factors that influence soybean yields the most. Data Both balanced and unbalanced panel data have been applied in this paper. The two important components are the fixed effects model and the random effects model. Based on this paper, the fixed effects model is sufficient enough to perform the regressions because most of the factors are unchanged over time: yit = α + βXit + uit, uit = μi + νit. Yield is the dependent variable, which stands for the amount of soybean output during the harvest season.

It was measured in bushels at a 13% moisture level per acre. GM traits is one of the independent variables that affect the dependent variable a lot. Among 5951 different variaties through 2005-2009, most of them are roundup-ready (RR) soybeans (active ingredient: glyphosate). Only 11 out of 5951 are Liberty Link (LL) soybeans (active ingredient: glufosinate). Currently, RR and LL are the most commonly used types of genetic traits soybeans (GMO Compass). This information can be found through the varietal information program for soybeans website (VIPS).

The Illinois Variety Comparison Center provides accurate information for other independent variables such as maturity group, planting date, soybean cyst nematode (SCN), and row spacing. There are 3 levels of SCN: high (number of cysts larger than 25 per 100cc), medium (number of systs between 6-25 per 100cc), and low (number of cysts less than 6 per 100cc). Based on the table, nearly 83% of variaties have systs between 1 to 25 per 100cc. Soil type, insecticide usage, and rainfall levels are available directly from the performance test reports.

The maximum and minimum temperatures have been collected through the PRISM website. Maturity group and date of planting may affect soybean growth stages and development. Yield potential will be maximized if knowing the planting date and harvest stages for irrigation of soybeans (Heatherly, 2005). Finally, growing degree days can be calculated using the following formula: the average of maximum and minimum temperatures minus soybean base reference number, which is 50 in this case, and then multiple by number of days in that month (Davis, 2010).

Unfortunately, some of the information cannot be found in the performance test report or other websites, such as fertilizer applied, irrigation, and information about rotation. It only stated in the report that all the locations were at a high level of fertility. If those data could be found, it’ll make the regression analysis more accurate. Like most crops, soybeans need mineral nutrients N P K and Mo, etc. The amount applied could affect soybean yield. It is also noted that long term rotation is a key practice in soybean production (Kelley,2003).

MODELS In model 1, independent variables are rainfall level from May to September, GM traits, and maturity group. The regression equation for this model is: Y=55.27467+1.043597mayrain-0.1244473junerain-0.4496658julyrain+1.248396augrain+0.2966854+1.831295+0.9148783+1.450767rr2y-1.760565nogm-2.127109mat01-1.590642mat03 The R2 value of about 0.1040 means that about 10.4 percent of the soybean yield is explained by the independent variables (gm traits, rainfalls, maturity group). The average value of yield is 61.

202, though it seems that the coefficient of the constant variable 55.27467 is very close to the average yield 61.202. P-values for ll, rr2y and nogm are 0.48,0.418 and 0.31 are all less than 0.5, which means that ll, rr2y and nogm have a significant relationship. The p-value for gm traits rr is 0.596, which means rr does not make many contributions in explaining soybean yield in this case. Model 2: In model 2: Independent variables are gm traits and the growing degree days from May to Oct.

The regression equation for this models is: Yield= 72.40461+3.084ll+0.742rr+1.29rr2y-2.422nogm-0.01266maygdd-0.016julygdd-0.0075auggdd+0.024sepgdd-0.01octgdd The R2 value of about 0.1836 means that about 18.36 percent of the soybean yield is explained by the independent variables (gm traits, gdd). The average value of yield is 61.202, though it seems that the coefficient of the constant variable 72.404 is very close to the average yield 61.202. P-values for ll, rr2y and nogm are 0.223,0.462 and 0.

155 are all less than 0.5, which means that ll, rr2y and nogm have a significant relationship. The p-value for gm traits rr is 0.662, which means rr does not make many contributions in explaining soybean yield in this case. Model 3: In model 3: Independent variables are SCN, minimum temperature from May to October and gm traits. The regression equayion for this model is: Yield=6.0526-3.839scnlow+3.67scnmedium-0.7788maymin+1.8196junemin-0.043julymin-0.028augmin-0.087sepmin-0.216octmin+3.4ll+1.077rr-2.185nogm In this model we find that scnhigh may cause correlation problems in the estimation, therefore we only used csnlow and csnmedium in this model.

The R2 value of about 0.2818, which means that about 28.18 percent of the soybean yield is explained by the independent variables (gm traits, min temp, and SCN) compared to the previous 2 models. The average value of yield is 61.202, so the coefficient of the constant variable 6.05 is very close to the average yield 61.202. P-values for ll, rr, rr2y and nogm are 0.152, 0.498, 0.279 and 0.171, and are all less than 0.5, which means that they all have a significant relationship. Also, p-values for julymin, augmin, sepmin and octmin are all significant.

Results and Conclusion From viewing the results of all three models, it appears that the third model is the best as it shows that there are significant relationships between the various factors and soybean production. Though there may be correlation problems, it is model three that shows any significance involving the purpose of the study. The first two models that were detailed in this report, while accurate, did not give enough information to fully understand any relationship between the independent variables and soybean production.

The third model, on the other hand, and though it has some flaws, gives me information to allow a better understanding of the purpose of this report. It is important to note that all of the models lacked a vital independent variable or other. This missing variable is due to holes and missing information in the reports. If this variable was present when it should have been, the results of all three models would have been different, especially the third model, the best of the three. Furthermore, there could be an increase in the amount of notable relationships if these missing variables were to be where they should have been.

This simply opens up new doors for further research and studies to help determine all possible relationships between various variables and soybean production. References National Soybean Research Laboratory http://www.nsrl.illinois.edu/general.html Variety Testing Department of Crop Sciences Soybeans Variety Test Results in Illinois http://vt.cropsci.illinois.edu/soybean.html VIPS http://www.vipsoybeans.org/v4/vphome/vipshome.cfm?CFID=6879269&CFTOKEN=64265145 Illinois Variety Comparison Center http://www.

vipsoybeans.org/v4/vpCompare/cvCS1.cfm?b=y PRISM http://gisdev.nacse.org/prism/nn/index.phtml?vartype=ppt&month=04&year0=1971_2000&year1=1971_2000 Davis, V (2010). University of Illinoi Extention Blog http://web.extension.uiuc.edu/soybean/blogs/eb198/ Kelley, K. W., J. H. Long, et al. (2003). "Long-term crop rotations affect soybean yield, seed weight, and soil chemical properties." Field Crops Research 83(1): 41-50. GMO Compass http://www.gmo-compass.org/eng/glossary/ Heatherly, L (2005).

Soybean maturity group, planting date and development related. Delta Farm Press. http://deltafarmpress.com/soybean-maturity-group-planting-date-and-development-related