Historic, archived document Do not assume content reflects current scientific knowledge, policies, or practices. I I I* Climatic Factors and Yields of Cotton, Milo, 'y— '*•■ Production Research Report No. 1 9 UNITED STATES DEPARTMENT OF AGRICULTURE in cooperation with THE TEXAS AGRICULTURAL EXPERIMENT STATION CONTENTS Page BACKGROUND OF THE STUDY 1 PROCEDURE - 1 RESULTS 2 Correlations between climatic factors 2 Correlation of climatic factors with crop yields 3 Preseasonal precipitation and soil moisture 3 Relationships between precipitation and yield 4 DISCUSSION OF RESULTS - 7 SUMMARY 9 LITERATURE CITED 10 APPENDIX 11 ACKNOWLEDGMENT The authors wish to express appreciation for the assistance of M. H. Halstead- and R, L. Smith, Jr., A, & M. College of Texas, and E. R. Lemon and A. W. Zingg, Soil arid Water Conservation Research Division, Agricultural Research Service, United States Department of Agriculture. Issued April 1958 Washington, D. C. For sale by the Superintendent of Documents, U. S. Government Printing Office, Washington, D. C. Price 10 cents RELATIONSHIPS BETWEEN CLIMATIC FACTORS AND YIELDS OF COTTON, MILO, AND KAFIR ON SANDY SOILS IN THE SOUTHERN HIGH PLAINS By W. C. Moldenhauer and F. E. Keating ‘ BACKGROUND OF THE STUDY In areas of highly variable climate such as west-central Texas, it is difficult if not impossible to predict future climatic patterns even for short periods. As an average for the area, however, the winters are very dry; May and June are the months of highest rainfall; July is usually dry and followed by a period of rainfall in late August and early September. Successful crop production must fit this climatic pattern, and the crops grown must withstand long periods of summer drought. Various workers, through correlation or regression analysis, have evaluated the importance of climatic factors in determining the variability of crop yields. Patton (^),^ working with spring wheat in Montana, found high correlations between various individual climatic factors and yield. He obtained a correlation coefficient of 0.944 between relative humidity in June and July and yield of spring wheat. Staple and Lehane (9) calculated an evapotranspiration figure from the sum of stored moisture used from the soil plus the seasonal rainfall. They found this figure to be associated closely with yield of spring wheat. Smith (J), working in Louisiana, found a correlation coefficient of 0.7 55 between cotton yield and three variables - -June rainfall, August rainfall, and August temperature. This study was undertaken to obtain a better under standing of the effects of climatic fac - tors on yields of cotton, milo, and kafir at Big Spring, Tex. It was hoped that information de - veloped from this work would be useful in determining adapted cropping practices and land use in the Southern High Plains. The study included (1) correlations between climatic fac- tors, (2) correlations of climatic factors with yields, ( 3 ) relationships between preseasonal precipitation and soil moisture, and (4) relationships between precipitation and yield. PROCEDURE Records from the Big Spring, Tex., Field Station from 1915 through 1954 were used in the study. Yield data were from 0.1 -acre plots. For cotton, the yield data on Amarillo fine sandy loam were obtained from plots used in a 3 -year rotation of cowpeas -milo- cotton because these plots were on Amarillo fine sandy loam that is typical for this soil on the station and were in a location where runoff water was not likely to collect. Keating and Mathews (5) found that cowpeas in the rotation had a very minor effect on cotton yield on these plots, especially since the forage was taken off as hay. For milo and kafir, the yield data on Amarillo fine sandy loam were obtained from a series of plots planted continuously to these crops. For cotton and milo, the yield data on Amarillo sandy clay loam were obtained from a 2 -year cotton-milo rotation because the site of this rotation best typified this soil type. The Amarillo fine sandy loam is a brown, fine sandy loam with a reddish brown, moderately permeable, sandy clay loam subsoil below the 10- to 18 -inch depth. Free calcium carbonate occurs in the B horizon from 26 to 36 inches. Below this depth is the horizon of maximum calcium carbonate accumulation. The Amarillo sandy clay loam has been changed by erosion of fine material to a fine sandy loam at the surface. This is un- derlain by a reddish brown clay loam subsoil below the 8- to 14-inch depth. Traces of free calcium carbonate occur in the B horizon from 26 to 44 inches. Below this depth ^ Soil scientist and agronomist, respectively. Soil and Water Conservation Research Division, Agricultural Research Service, United States Department of Agriculture, and Texas Agricultural Experiment Station, Big Spring, Tex. 2 Numbers in parentheses and underscored refer to Literature Cited, p. 10. is the horizon of maximunn calciuna carbonate accumulation. This soil is similar to the Amarillo loam described by Carter et al. (_2). Climatic data were available from a weather station near the plot area. Average monthly maximum, minimum, and mean temperatures were obtained by averaging the maximum, minimum, and mean temperatures, respectively, recorded each day for the month. The daily mean was obtained from an average of the daily maximum and minimum temperatur-es . Maximum wind velocity used was the highest average recorded for a day during the month. Mean wind velocity was the average of 24 -hour periods for the month. In the precipitation-soil-moisture study, amounts of soil moisture above the wilting percentage were considered to be available for plant growth. Simple correlation coefficients were obtained according to the method of Snedecor (8), and multiple regression analyses were made following the methods outlined by Ezekial (3). RESULTS Correlations between climatic factors Coefficients of correlation between various climatic factors are shown in table 1 and appendix table A. By way of explanation, factors that are associated are thought to be correlated. The correlation coefficient (x) expresses the intensity of the relationship. A correlation coefficient of 1.0 shows a perfect positive correlation; a coefficient of -1.0 shows a perfect negative correlation. Correlation coefficients of 0.8 or 0.9 are high and show that the factors measured are closely related. Coefficients of 0.3 or 0.4 are low and show much less association. High correlations mean more if a large number of observations are made. High correlation with 15 or less observations means less than the same correlation with 30 or more observations. Highly significant negative correlations were obtained between annual and seasonal pre- cipitation and temperatures in June, July, and August (table 1). Highly significant negative correlations were obtained between precipitation and temperature for each of these months individually. Correlation between preseasonal and seasonal precipitation was not significant. Correlation between precipitation at various seasons and wind velocity was generally very low (appendix table A). The exception is the highly significant correlation between April precipitation and March maximum wind velocity. This association affects the only other significant correlation involving wind velocity- -that between preseasonal pre- cipitation (October 1 to April 30) and March maximum wind velocity. Correlations between April wind velocity and preseasonal precipitation (12 months prior to April 30) approach significance. TABLE 1. --Coefficients of correlation between various climatic factors. Big Spring, Tex., 1916-54 Climatic factor Precipitation Ten^jerature Annual Presea- sonal Seasonal May June July August June mean July minimum August mean Temperature : August mean -0 . 604** -0.398* -0.553** -0.190 -0.212 -0.162 -0.541** 0.326* 0.149 — July mean - . 606** -.270 -.681** — — — — .425** .767** 0.363* July minimum -.580** -.360* - . 546** -.102 -.310 -.449** -.257 .362* — — June mean - . 568** -.323* - . 560** -.377* -.723** .125 -.112 --- --- — - Precipitation: August — — — -.060 .006 .105 — — — — July — — — -.052 -.038 — — — — — June — — — .023 — — — — — — Preseasonal (Sept 1- Apr. 30) — — .205 — — — — — — — * Significant at the 5-percent level. ** Significant at the 1-percent level. 2 Correlation of climatic factors with crop yields Appendix table A shows the coefficients of simple correlation between yields of cotton and milo on Amarillo sandy clay loam and climatic factors including precipitation, temperature, and evaporation. The data used in these correlations were from 1915 through 1953. For both cotton and milo, the correlation was higher between yield and annual precipitation calculated from September 1 to August 31 than from January 1 to December 31, or from October 1 to September 30. The highest correlated seasonal period for both crops was from April 1 to September 30 but for cotton the period from May 1 to August 31 was almost as high. The highest correlated preseasonal period for milo was from October 1 to April 30, and for cotton from September 1 to April 30. For milo, yields showed a significant correlation with precipitation by months in April, June, and August and for cotton, in August only. Coefficients of correlation between yields of cotton and milo and mean temperature in June, July, and August were all negative and were all highly significant. For milo, the correlation was higher between yield and average mean temperature than average maxi- mum or average minimum temperatures in June and July. In August, the correlations were the same for average mean and average maximum temperatures. For cotton, the highest correlation was with average mean temperature in June and August, but in July average minimum temperature was highest. In August, the correlations were practically the same for average maximum and average mean temperatures. For milo, the correlation with average minimum temperature in September was significant and positive. For both crops, yield was significantly correlated with total evaporation in June and August, but not in July. Preseasonal precipitation and soil moisture The relationship between preseasonal precipitation and available soil moisture for the cotton crop is shown in figure 1 and for the kafir crop in figure 2. As preseasonal periods differed for cotton and kafir, the period September 1 to April 30 was used as the pre- seasonal period for kafir (fig. 2). Since most of the sampling was done about June 1, there was some overlapping of soil moisture and seasonal precipitation data for cotton. A great deal of this overlapping was compensated for by omitting soil-moisture values for the first foot of soil where the period September 1 to April 30 was used. Data used in determining these relationships are given in appendix tables B and C. In these studies moisture determinations used were from plots in milo continuously. Thus they are an index of moisture available for other crops and possibly do not represent normal values for cotton and kafir. The formula for the relationship shown in figure 1 is Me = -0.59 + 0.163 Pc, where Me is available soil moisture in inches about June 1 exclusive of the first foot of soil and Pc is the preseasonal precipitation in inches for the period September 1 to April 30. The correlation coefficient for this relationship was 0.825, The formula for the relationship shown in figure 2 is Mj^ = 0.126 P^, where Me is the available soil moisture in inches contained in the entire 6-foot profile and Pj^ is the preseasonal precipitation in inches for the period October 1 to May 31. The correlation coefficient for this relationship was 0,624. 3 * SEPT. 1 -APR. 30 Figure 1. Estimated soil moisture available for the cotton crop in relation to preseasonal precipitation, Big Spring, Tex,, 1915-54. Relationships between precipitation and yield Coefficients of correlation between various periods of seasonal and preseasonal precipitation and yields of cotton and kafir on Amarillo fine sandy loam are shown in table 2. Data used in these correlations were from 1916 through 1954. Results for cotton in this table are very similar to those reported for cotton on Amarillo sandy clay loam in appendix table A. Coefficients of correlation between kafir yield and the periods of sea- sonal precipitation used are very similar (table 2). Results of multiple regression analysis of yield of all the crops on preseasonal and seasonal precipitation are summarized in table 3. By way of explanation, regression shows how changes in one variable are dependent on changes in another variable. This dependence is measured by means of a regression coefficient (b), which shows the num- ber of units the dependent variable will change, on the average, for a unit change in the independent variable. The data in table 3 were for the period 1916 through 1954. To make the data com- parable, the preseasonal periods were from September 1 to April 30 for cotton and from October 1 to May 31 for kafir and milo. Seasonal periods were from May 1 to August 31 for cotton and from June 1 to August 31 for kafir and milo. The annual period was from September 1 to August 31 for both cotton and kafir. Average cotton yields on Amarillo sandy clay loam and on Amarillo fine sandy loam were 190 and 240 poiinds per acre, respectively (appendix table B). As shown in table 3, 4 * OCT. 7 ^MAY 31 Figure 2.--EstimatEd soil moisture available for Kafir crop in relation to preseasonal precipitation, Big Spring, Tex., 1951-54. TABLE 2. --Correlation between different periods of seasonal and preseasonal precipitation and yields of cotton and kafir on Amarillo fine sandy loam. Big Spring, Tex., 1916-54 Precipitation period Correlation coefficient Precipitation period Correlation coefficient Cotton yield Kafir yield Cotton yield Kafir yield Seasonal : May 1 to August 31 May 1 to September 30.... June 1 to August 31 June 1 to September 30 . . . 0. 640** . 566** .459** .424** 0 . 643** .625** .645** .623** Preseasonal: September 1 to April 30.. October 1 to April 30..., October 1 to May 31 .489** .361* .565** .437** .469** .526** * Significant at the 5-percent level. ** Significant at the 1-percent level. the coefficients of correlation (r) between cotton yield and preseasonal precipitation for the two soils were 0.550 and 0.489, respectively, and between cotton yield and seasonal precipitation, 0.612 and 0.640, respectively. Coefficients of multiple correlation were 0.745 and 0.733, respectively. Average milo yields on Amarillo sandy clay loam and on Amarillo fine sandy loam were 12.2 and 19.7 bushels per acre, respectively (appendix table C). As shown in table 3, the coefficients of correlation (r) between milo yield and preseasonal precipitation for the two soils were 0.536 and 0.411, respectively, and between milo yield and seasonal precipitation 0.535 and 0.725, respectively. Coefficients of multiple correlation were 0.696 and 0.77 8, respectively. 5 TABLE 3. --Summary of results of multiple linear regression of yield on precipitation for cotton and milo grown on Amarillo sandy clay loam and for cotton, milo, and kafir grown on Amarillo fine sandy loam. Big Spring, Tex., 1916- 54 Coefficients of correlation (r, R), determination (R^), standard regression ( P ), partial regression (b), and equation constant (a)7 ~ Dependent (Yield) Variable (Soil type) Cotton. /Amarillo sandy cla^r loam. ^Amarillo fine sandy loam. Milo. 'Amarillo sandy clay loam. [Amarillo fine sandy loam. Kafir. .do. Independent (Precipitation) r R b a Preseasonal 0.550** 0.745** 0.555 0.436 21.9 -57 Seasonal .612** — — .515 15.0 — Preseasonal .489** .733** .537 .366 12.1 -29 Seasonal .640** — — - .537 18.2 — Preseasonal .536** .696** .485 .453 1.24 -9.8 Seasonal .535** — — .452 1.58 --- Preseasonal .411** .778** .605 .288 .89 -5.3 Seasonal .725** — - — .672 2.65 — Preseasonal .526** .763** .583 .417 1.00 -3.1 Seasonal .645** — — .564 1.75 — ** Significant at the 1-percent level. For milo and kafir grown on Amarillo fine sandy loam, the coefficient of correlation between preseasonal precipitation and yield was 0.411 and 0.526, respectively (table 3). The coefficient of correlation between seasonal precipitation and yield was 0.725 for milo and 0.645 for kafir. Average yields were 19.7 bushels per acre for milo and 17.1 for kafir (appendix table C). When years were omitted in which field notes indicated clearly that factors other than precipitation had reduced yields considerably (table 4), the coefficient of correlation between cotton yield (log values) and annual precipitation (log values) was 0.816. The formula for this relationship is Y = 0.024 where Y is the yield of lint cotton in pounds per acre, and A the annual precipitation from September 1 to August 31 in inches. The coefficient of multiple correlation between yield (log values) and precipitation, preseasonal and seasonal, was 0.825. The formula for this relationship is •\T _ occ ID 1.14q 1.86 Y = 0.355 , where Y is the yield of lint cotton in pounds per acre, is the preseasonal precipitation from September 1 to April 30 in inches, and Sc is the seasonal precipitation from May 1 to August 3 1 in inches. When the low-yield years 1923, 1929, and 1 932 were omitted, the coefficient of corre- lation between kafir yield (log values) and annual precipitation (log values) was 0.789. The formula for this relationship is Yj^ = 0.031 where Yj^ is kafir yield in bushels per acre and A is the annual precipitation from Sep- tember 1 to August 31 in inches. When preseasonal and seasonal precipitation were considered as independent variables, the coefficient of multiple correlation was 0.834. The formula for this relation- ship is Yy. = 0.211 kafir yield in bushels per acre, Pj^ is the preseasonal precipitation from October 1 to May 31 in inches, and Sj^is the seasonal precipitation from June 1 to August 31 in inches. 6 TABLE 4. — Summary of results of multiple regression of yield on precipitation for cotton and kafir grown on Amarillo fine sandy loam (with years omitted when yield was affected by factors other than precipitation). Big Spring, Tex., 1916-54 /^Coefficients of correlation (r, R), determination (R^), standard regression ( P ), psirtial and net regression (h), and equation constant (a)_/ Dependent (Log yield) Variables (Soil type) Independent (Log precipi- tation) r R r2 P b a (^Amarillo fine sandy loam.... Annual 0.816** --- 3.14 -1.62 Cotton rpreseasonal .535** 0.825** 0.680 0.367 1.14 -.45 \Seasonal .745** — — .650 1.86 --- .789** 2.12 -1.51 Kaifir do • 0 o o o o • • • • fPreseasonal .662** .834** .695 .554 1.24 -.68 (Seasonal . 634** --- .518 .87 — Note: Years omitted for cotton: 1926, 1929, 1932, 1938, 1945, and 1948; for kafir: 1923, 1929, and 1932. ** Significant at the 1-percent level. DISCUSSION OF RESULTS Since temperature and precipitation are shown to be closely interrelated, it is doubtful that both should be used in determining the relationships between climate and yield. Rain- fall figures can be used for any period without chance that one period will be closely related to another, whereas temperature figures for a particular period are affected by moisture conditions prior to as well as during the period. For this reason rainfall figures were uscid in multiple correlation and regression analyses in this study. Coefficients of correlation between precipitation and wind velocity were generally too low to have much significance. The highly significant correlation between April precipita- tion and March maximum wind velocity was due apparently to one year (1922) when April precipitation was 12.77 inches (the average is 1.64 inches) and the average wind velocity for one 24-hour period in March was 23.4 miles per hour (the average is 12.3 miles per hour). If these values are left out, the correlation coefficient drops from 0.630 to 0.228. For all crops, the correlation between yield and annual precipitation was higher for the period September 1 to August 31 than for any other period used. Correlation between yield and preseasonal precipitation, however, decreased for both milo and kafir when September values were included. This is presumably the result of the indefinite growing period of sorghum, and depends on weather conditions. When conditions are favorable during the summer, sorghum is ready for harvest in September, When conditions are unfavorable during the summer, it may continue growing in September, With milo the correlation decreases when October values are not included, as shown in appendix table A. The highly significant coefficient of correlation between April precipitation and milo yield was used by Keating and Mathews to explain the fact that fall plowing resulted in an increase in milo yield of only about 1 bushel per acre, on the average, over April 1 plow- ing, May 1 plowing, however, decreased the yield significantly below that of April 1 plowing. The reason that the correlation was higher between cotton yield and July average minimam temperature than July average mean or maximum temperature is not known. The fact that total evaporation was correlated significantly with cotton yield in both June and August but not in July may be a result of the same phenomenon. Detailed information on the effect of temperature in relation to soil moisture is needed for an understanding of this phenomenon. The fact that the effect of temperature changes from negative in August to positive in September shows that cooler temperatures in September actually retard growth of cotton and milo. Figures 1 and 2 show that the relationship between preseasonal precipitation and soil moisture is only fair. In figure 1, r^ = 0.681, which leaves about 30 percent of the 7 variability in soil moisture unaccounted for by pre seasonal precipitation. In figure 2, r^ = 0.389, which leaves about 60 percent of the variability in soil moisture unaccounted for by preseasonal precipitation. When log values of soil moisture and preseasonal precipitation were used, an r^ of 0.597 was obtained. This value was not significantly different from the values obtained with arithmetic values. The data in appendix tables A and B indicate that in a number of years soil moisture was influenced by the amount of precipitation during the growing season of the previous year. Several attempts were made to account for some of the variability in soil moisture by taking this into account. No improvement re suited from these adjustments, and further examination of the data showed that in a number of years precipitation for the previous crop season had no effect on soil moisture at the June 1 sampling date. It was concluded that the variability in soil moisture unaccounted for by preseasonal precipitation was due in part to runoff, but perhaps also to a relationship with precipitation during the preceding growing season. This relationship is complicated by the amount of moisture used by the crop grown during the preceding season and will require more study before it will be useful in accounting for more of the variability in soil moisture. The most striking difference observed between Amarillo sandy clay loam and Amarillo fine sandy loam was the difference in yield of both cotton and milo (appendix tables B and C). Yield differences were highly significant for both crops. Also for both crops, correlations between yield and preseasonal precipitation were higher on Amarillo sandy clay loam and correlations between yield and seasonal precipitation were higher on Amarillo fine sandy loam. The difference in correlation coefficients was more pro- nounced for milo than for cotton. This would be expected since the Amarillo sandy clay loam has a higher moisture -holding capacity than the Amarillo fine sandy loam and retains more of the preseasonal precipitation for use by the crops. On an even finer textured soil, Abilene clay loam, Burnett and Fisher (Jl) at Spur, Tex., obtained a corre- lation coefficient of 0.747 when cotton yields were correlated with available moisture in the second and third foot of soil. Kafir has been thought to be more tolerant to drought than milo because of its indeterminate habit of growth (4) and because it does not tiller during periods of moist weather as does milo (4, 10). Appendix table C shows that range in yield from year to year on Amarillo fine sandy loam is not as great for kafir as for milo. Kafir yields were higher than milo yields in a few years, but ordinarily milo yields were higher. This is probably due to adaptability of individual varieties. Dwarf Yellow milo has always been the highest yielding grain sorghum variety at the Big Spring Field Station. It is now impracticable to grow, however, because it cannot be harvested with a combine. The coefficients of correlation (table 3) indicate that during wet seasons tillering of milo increases the yield considerably over that of kafir. Thus, the effect of seasonal precipi- tation is more important to milo yields than to kafir yields. Since kafir does not respond as much to high seasonal precipitation, preseasonal and seasonal precipitation are nearer to equal in importance. The primary purpose of the fourth part of this study was to determine the relation- ship between precipitation and yield. It was believed that years in which yield was reduced considerably by factors other than precipitation would confuse the relationship. For this reason it was considered justifiable to omit them in this phase of the study. For example, cotton yields were reduced considerably in 1926 by leafhopper damage to squares, in 1929 and 1932 by late sandstorm damage, in 1938 by a heavy bollworm infestation, and in 1945 and 1948 by late planting and early frost. Field notes were not maintained consistently on kafir, but it was noted that there was considerable damage from fall rains in 1923 and from late sandstorms in 1929 and 1932. It must be recognized that these hazards to crop production exist, and any calculation of risk involved in farming in this area must take these into account. It is interesting to note that the relationship between cotton yields and preseasonal and seasonal precipi- tation, when considered separately, were improved very little over that obtained with annual precipitation alone. The same was true for kafir. The relationship between annual precipitation and kafir yield would probably be improved if September values were omitted. 8 SUMMARY Highly significant negative correlation coefficients were obtained between tempera- tures in June, July, and August and both annual and seasonal precipitation. Highly significant negative coefficients were obtained between temperature and precipitation in each of these months individually. Thus, low rainfall is shown to be associated with high temperature. Correlations between wind velocity and precipitation were generally very low. There was a highly significant correlation between average maximum wind velocity in March and precipitation in April because of one year in which there was extreme wind velocity in March and eight times average rainfall in April. For both cotton and milo, the correlation was higher between yield and annual pre- cipitation calculated from September 1 to August 31 than from January 1 to December 31 or from October 1 to September 30. The difference was more pronounced for cotton than for milo. Precipitation by months was significantly correlated with milo yield in April, June, and August but was significantly correlated with cotton yield only in August. Average temperatures in June, July, and August were negatively correlated with yields of both cotton and milo. This negative correlation was highest in August for cotton but was very similar in all 3 months for milo. Thus, low temperatures are shown to be associated with high yields. The coefficient of correlation between available soil moisture for the cotton crop in the 1- to 6 -foot soil zone and pre seasonal precipitation from September 1 to April 30 was 0.825, The coefficient of correlation between available soil moisture in the 6-foot profile and pre seasonal precipitation from October 1 to May 31 was 0,624. When log values were used this coefficient was 0.77 3. Average yield of cotton on Amarillo sandy clay loam and on Amarillo fine sandy loam was 190 pounds and 240 pounds per acre, respectively. The coefficient of correlation between preseasonal precipitation and yield of cotton was 0.550 and 0.489, respectively. The coefficient of correlation between seasonal precipitation and yield was 0,612 and 0,640, respectively. Average yield of milo on Amarillo sandy clay loam and on Amarillo fine sandy loam was 12.2 and 19.7 bushels per acre, respectively. The coefficient of correlation between preseasonal precipitation and yield of milo was 0.536, and 0.411, respectively. The coefficient of correlation between seasonal precipitation and yield was 0.535 and 0,725, respectively. Average yield of milo on Amarillo fine sandy loam was 19.7 bushels per acre com- pared to 17.1 bushels per acre for kafir. The coefficient of correlation between yield and preseasonal precipitation was 0.411 for milo and 0.526 for kafir. The coefficient of correlation between yield and seasonal precipitation was 0.725 for milo and 0.645 for kafi r . The formula for the relationship between cotton yield and annual precipitation (September 1 to August 31) was Y = 0.024 A c 3 . 14 The coefficient of correlation for this relationship was 0.816. The formula for the rela- tionship between cotton yield and preseasonal and seasonal precipitation was 1.14^ 1.86 = 0.355 9 The coefficient of multiple correlation for this relationship was 0.825, The formula for the relationship between kafir yield and annual precipitation (September 1 to August 31) was 2.12 Yj^ = 0.031 A The coefficient of correlation for this relationship was 0,789. The formula for the rela- tionship between kafir yield and pre seasonal and seasonal precipitation was Yj^ = 0.211 The coefficient of multiple correlation for this relationship was 0,834. LITERATURE CITED (1) Burnett, E. and Fisher, C. E. 1954. Correlation of soil moisture and cotton yields. Soil Sci. Soc. Amer, Proc, 18: 127-129. (2) Carter, W. Y., Gieb, H. V., Beck, M, W,, and others. 1922. Reconnaissance soil survey of northwest Texas. U, S. Dept. Agr., Bur, of Soils publication, 7 5 pp. (3) Ezekial, M. 1950, Methods of correlation analysis. Ed. 2, 531 pp. New York. (4) Karper, R. E,, Quinby, J. R., Jones, D. L., and Dickson, R, E. 1931, Grain sorghum date -of -planting and spacing experiments, Tex. Agr. Expt, Sta. Bui. 424, 71 pp, (5) Keating, F. E,, and Mathews, O. R. 1957. Soil and crop studies at the Big Spring (Texas) field station, 1916-53, U. S. Dept. Agr., Prod. Res. Rpt. 1, 31 pp. (6) Patton, P. 1927, The relationship of weather to crops in the plains region of Montana, Mont. Agr. Expt. Sta. Bui. 206, 66 pp. (7) Smith, B. B, 1925. Relation between weather conditions and yield of cotton in Louisiana. Jour. Agr. Res. 30: 1083-1086. (8) Snedecor, G. W. 1946. Statistical methods. Ed. 4, 485 pp. Iowa State College, Ames, (9) Staple, W, J, and Lehane, J. J. 1954. Weather condition influencing wheat yields in tanks and field plots. Canada Jour. Agr. Sci. 34: 553-564. (10) Vinall, H. N., Stephens, J. C., and Martin, J, H. 1936. Identification, history, and distribution of common sorghum varieties. U. S, Dept. Agr, Tech. Bui. 506, 102 pp. 10 APPENDIX TABLE A. --Coefficients of simple correlation between climatic factors and yields of milo and cotton on Amarillo sandy clay loam and between precipitation and wind velocity, Big Spring, Tex., 1915-53 Climatic factor Yield Wind velocity Milo ^1 Cotton ^2 March April Maximum ^3 Mean ^4 Maximum ^5 Mean ^6 Precipitation Annual : Jan. 1 to Dec. 31 0.618** 0.548** 0.137 0.064 0.145 -0.128 Oct. 1 to Sept. 30 .624** .587** .077 .031 .095 -.152 ,698** .707** Seasonal: .460** .532** .404** .454** .568** , 545** Preseasonal: Oct. 1 to Mar. 31 .468** ,432** .065 .026 -.037 to 1 — 1 1 Oct. 1 to Apr. 30 .656** .517** .320* .277 -.028 — Sept. 1 to Mar. 31 .389* .431** -.124 — — — Sept. 1 to Apr. 30 . 630** .572** — — -.005 — 12 months prior to Apr. 30 .536** . 474** .032 .000 -.252 -.298 Nov. 1 to Apr. 30 .641** .433** --- — — — During previous growing season .115 .149 — — — April .461** .295 .630** — .030 .091 -.009 .185 — — — — June .360*' .272 — — — — July .194 .170 — — — — August .400* .438** — — — — September .098 .084 — — — — October .244 .137 — — — — Temperature and evaporation May: Average maximum temperature -.066 -.236 — — — — Average mean temperature -.150 -.255 — — — — Average total evaporation -.151 - .406* — — — — June : Average maximum temperature -.510** -.410** — — — — Average mean temperature -.529** - .468** — — — — Average minimum temperature -.329* -.291 — — — — Average total evaporation -.369* -.328* — — — — July: Average maximum temperature -.343* -.384* — — — — Average mean temperature - . 506** -.432** — — — — Average minimum temperature -.488** -.504** — — — ___ Average total evaporation -.147 -.118 — — — — August: Average maximum temperature - . 541** -.691** — — — — Average mean temperature - . 541** - . 705** — — — — Average minimum temperature -.365* -.522** — — — — Average total evaporation -.349* - . 560** — — — — September : Average maximum teirperature -.045* -.175 — — — — Average mean tenperature .129 -.002 — — — — Average minimum temperature .355 .245 — — — — Average total evaporation -.163 -.293 — — — — * Significant at the 5-percent level. ** Significant at the 1-percent level. 11 TABLE B. — Cotton yields and data used in determining yield, precipitation, and soil-moisture relationships. Big Spring, Tex., 1916-54 Year Yield per acre Precipitation Soil moisture ( excluding first foot) Amarillo fine sandy loam Amarillo sandy clay loam Annual (Sept. 1- Aug. 31) Preseasonal (Sept. 1- Apr. 30) Seasonal (May 1- Aug. 31) Pounds Pounds Inches Inches Inches Inches 1916 129 103 17.46 8.94 8.52 0.90 1917 0 8 7.05 4.66 2.49 0 1918 32 53 7.55 2.43 5.12 0 1919 467 365 25.11 10.85 14.26 1.34 1920 540 578 30.84 16.98 13.86 2.38 1921 198 110 15.23 7.47 7.76 .38 1922 232 243 21.75 15.90 5.85 2.29 1923 346 300 19.05 12.54 6.51 1.41 1924 187 133 18.43 11.77 6.66 .92 1925 403 224 14.13 6.86 7.27 .40 1926 232 175 22.00 11.77 10.23 .56 1927 194 95 18.25 13.15 5.10 .71 1928 410 251 22.07 6.47 15.60 .57 1929 95 110 15.82 7.03 8.79 .68 1930 220 163 18.82 12.36 6.46 .72 1931 209 194 17.39 12.62 4.77 .94 1932 360 290 33.88 19.15 14.71 1.85 1933 320 300 20.62 13.33 7.29 1.94 1934 178 188 12.23 6.97 5.26 .86 1935 394 337 20.22 7.22 13.00 .19 1936 119 92 17.08 11.10 5.98 1.63 1937 390 280 23.64 16.30 7.34 2.11 1938 254 213 24.23 9.78 14.45 1.23 1939 200 212 14.69 5.26 9.43 .62 1940 246 174 14.75 4.80 9.95 .19 1941 468 430 26.49 12.25 14.24 1.32 1942 220 180 24.25 12.16 12.09 2.01 1943 244 144 18.47 9.95 8.52 1.91 194^ 229 154 15.58 8.20 7.38 .24 1945 280 240 26.82 9.68 17.14 .63 1946 234 110 11.50 7.32 4.18 .50 1947 240 180 15.46 8.79 6.67 1.42 1948 121 181 14.11 5.11 9.00 .16 1949 282 204 16.65 8.04 8.61 .96 1950 355 210 22.26 7.68 14.58 .81 1951 180 57 12.27 3.56 8.71 0 1952 0 0 5.58 3.34 2.24 0 1953 29 20 11.80 9.43 2.37 .37 1954 140 113 23.93 10.63 13.30 1.24 Average 240 190 18.40 9.53 8.86 0.94 IZ TABLE C. — Kafir and milo yields and data used in determining yield, precipitation, and soil-moisture relationships Big Spring, Tex., 1916-54 Year Yield per acre Precipitation Soil moisture (6-foot profile) Kafir Milo Preseasonal (Oct. 1- May 31) Seasonal (June 1- Aug.3l) Amarillo fine sandy loam Amarillo fine sandy loam Amarillo sandy clay loam Bushels Bushels Bushels Inches Inches Inches 1916 22.8 34.2 16.4 6.13 8.38 1.34 1917 .3 10.9 .3 4.30 1.88 .17 1918 0 0 0 2.83 3.93 0 1919 45.8 54.8 35.0 10.62 12.83 1.71 1920 0 27.5 31.6 38.3 14.87 8.54 3.32 1921 21.3 17.2 15.5 10.47 4.07 .39 1922 21.7 21.0 33.4 17.55 3.49 2.96 1923 13.2 20.5 29.7 13.78 5.27 1.61 1924 7.2 — 1.6 13.66 3.04 1.44 1925 12.7 8.6 9.1 8.27 5.18 .87 1926 21.7 14.0 5.7 10.67 8.27 1.03 1927 9.8 10.5 10.2 10.87 3.82 .83 1928 18.8 10.9 4.3 12.57 5.50 .97 1929 7.2 4.1 2.2 9.45 5.61 1.11 1930 8.2 7.8 1.6 8.87 4.51 1.37 1931 29.7 24.8 17.2 13.13 4.02 1.29 1932 31.5 31.6 20.2 24.28 9.53 2.28 1933 21.3 25.5 21.2 5.59 6.33 2.01 1934 15.0 21.4 7.9 6.41 5.18 .86 1935 17.2 23.8 10.3 10.91 8.40 .30 1936 18.2 11.0 .9 11.72 1.43 2.18 1937 20.0 25.5 10.9 9.14 3.98 2.69 1938 28.0 34.3 24.8 11.24 12.65 2.00 1939 28.3 23.3 6.9 8.05 6.53 .85 1940 21.3 8.3 3.6 6.62 8.13 .30 1941 32.5 49.7 43.6 16.95 9.35 1.79 1942 15.0 34.3 20.3 10.39 10.24 2.55 1943 13.8 19.5 10.9 10.13 4.08 2.39 1944 10.5 21.7 3.4 10,82 4.48 .52 1945 28.5 39.7 24.1 8.72 16.46 .92 1946 16.5 17.9 12.8 6.75 3.10 .80 1947 10.5 10.0 6.9 10.99 2.16 1.85 1948 10.5 12.6 1.0 5.35 8.06 .16 1949 17.7 16.2 7.6 12.44 4.19 1.34 1950 28.2 39.3 11.7 14.24 6.59 1.30 1951 6.0 6*6 .5 3.23 6.65 0 1952 0 0 — 3.16 1.42 0 1953 0 .2 1.9 6.92 1.66 .37 1954 7.7 13.6 3.3 17.73 5.65 1.46 Average 17.1 19.7 12.2 10.07 6.02 1.27 U. S. GOVERNMENT PRINTING OFFICE : 1958 O -463404 13