• precipitation;
  • temperature;
  • yields


  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and methods
  5. 3. Results
  6. 4. Conclusions
  7. Acknowledgements
  8. References

An important goal of this work is to study the variability of corn and soya bean crop yields in four countries with large production and substantial commercial trade in these commodities. This problem can be investigated in terms of the role that these two crops play in food programmes and in terms of the use of both crops for energy production. Four countries were chosen and divided into six production areas. A climatic summary was made of the annual cycles of extreme temperatures and precipitation. Their assessment in agriculture programmes was likewise summarized. It is seen that the variability range of the temperatures and precipitation are broad and different for each region. This finding indicates the high adaptability of these crops. This concept of adaptability is used to compare the coefficients for precipitation and crop yield. Results of the study show that corn crops show less year-to-year variability than do soya bean crops. The United States and the northern part of China are the regions that best use the rain supply with respect to crop yield. Soya bean crops show a greater year-to-year variability in the ratio of precipitation to crop yields. Argentina, the United States and northern China are the areas that best use the rain supply. To compare crop yields with climatic variables in the different regions, three types of regression model were used. The best fit is obtained by using the maximum temperatures and accumulated precipitation for each growth stage over the growing season. Copyright © 2011 Royal Meteorological Society

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and methods
  5. 3. Results
  6. 4. Conclusions
  7. Acknowledgements
  8. References

One of the concerns of applied climatology is to find the relationship between specific crop yields and climatic variables. This approach is based on the hypothesis that climatic variability introduces a component of variability in crop yield that is independent of those components that arise because of technology.

The influence of climate on a specific crop depends on the characteristics of the geographical location and of the production conditions. Extensive plains located in middle and subtropical latitudes are the main areas that produce cereals worldwide. Within these areas, some countries are mainly producers and consumers of the crops whereas other countries produce large amounts of these commodities and are also large exporters.

Since the beginning of the twentieth century, research has addressed the factors that can affect the yields of these crops. Among these factors, soil fertility is somewhat constant but is affected strongly by climate fluctuations (Rose, 1936). Early research indicates that temperature is the prevailing factor in this relationship. One of these studies (Smith, 1914) mentions that the geographical distribution of crops in the United States is determined by the climate, with temperature and precipitation being two of the climate variables that most affect crop development. Smith (1914) studied the relationship between crop yield and climate variables in the different months that make up the annual cycle, especially during summer. In a follow-up study, Wallace (1920) suggested that precipitation during July is one of the main factors that affect crop yields in the southern part of the United States Corn Belt. This study was followed by several others that found a high correlation between the yield of corn and the precipitation in July/August in the central region of the United States (Robb, 1934).

More recent studies of the effects of precipitation on the corn crop yielded positive correlations (Nagy and Huzsvai, 1996). Extreme temperatures are among the factors that influence the development of crops. Minimum and freezing temperatures restrict the growth and development of crops. Extremely high temperatures also have this effect (Montoya et al., 2004). In semiarid regions of Canada the maximum correlation between wheat crop yield, temperature and precipitation is observed during the months of flowering (McCaig, 1997).

Among the different techniques used to study this problem is multiple regression analysis. This method was used to measure the influence of climate on the crop yield of soya beans in different states of the United States (Thompson, 1970). The results of this study showed that higher temperatures during planting and the lower ones during flowering produced the best crop yields. This finding also holds if there is normal precipitation during planting and above-average precipitation during the flowering stage (Thompson, 1970). Thompson also found that optimal crop yields were associated with average temperatures during planting and with below-average values during flowering. Above-average precipitation during flowering produced good yields, but rainfall during harvesting did not influence the yield (Thompson, 1969).

Extreme monthly temperatures, along with the accumulated precipitation, were also found to be related to the yield of cereals in England (Chmielewski and Potts, 1995). This result suggested that precipitation has the greatest influence on crop yield.

Research carried out in Argentina shows that during the last decades of the twentieth century increases in the yields of summer crops were caused primarily by increases in precipitation. These increases in the yields were affected to a lesser degree by changes in temperature (Magrin et al., 2005).

The influence of precipitation and temperature also determine the variability of the soya bean yield in Argentina (Penalba et al., 2007). In Tucumán province (Argentina), a study by Minetti and Lamelas (1995) showed that the regional precipitation in December and the thermal amplitude in February are two variables that explain more than 50% of the variability of the yield of soya bean crops in that province.

The present work was based on two of the most-consumed crops: corn and soya beans. Both of them have similar bioclimatic demands and are interchangeable in crop rotations (Pascale, 1969).

The development of international business enterprises is affected by variations in climate. Of these variations, the most important result from extreme conditions that occur in one country or simultaneously in more than one. Therefore, it is important to study the climate variations of crop producing areas in four of the main corn and soya bean producing countries. It is likewise important to characterize and to compare these variations. Accordingly, the precipitation and monthly maximum/minimum temperatures are analysed in order to define the relationship of crop yields with climate throughout the crop-growing cycles, and specifically to determine the coherence between particular climatic variables. Crop yield (production per hectare) is used because it reveals the factors that affect crops and because of its economic importance (McKeown et al., 2006).

In the present study, the ranges of precipitation and monthly extreme temperatures were analysed. The evolution of these climate parameters was described during each stage of the growth of crops. The evolution of crop yields was investigated, first relative to precipitation and then as a function of the climatic characteristics of each growing area. The yields were also fitted with models that use the precipitation and extreme temperatures.

2. Data and methods

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and methods
  5. 3. Results
  6. 4. Conclusions
  7. Acknowledgements
  8. References

Data on crop yields of corn and soya beans in Argentina with corresponding data from the other countries that are major producers of these crops were compared in this study. Using agricultural statistics supplied by the United Nations FAO (Food and Agriculture Organisation) as a reference for the period 1998–2008 for corn, the three leading producing nations were the United States, China and Brazil. Argentina occupied fifth place and Mexico fourth place. The five leading soya bean producers were the United States, Brazil, Argentina, China and India.

In the light of this information, four nations were chosen for this study: Argentina, Brazil, the United States and China. Each of these countries has extensive plains where both of these crops are grown. According to the aforementioned statistics 65% of the world production of corn and more than 90% of the world production of soya beans were grown in these countries. In addition, the first three countries are among the main exporters of these crops.

Each of these countries has characteristic areas in which these crops are produced. In Argentina, the main region is the Humid Pampa. This region extends towards the north and northwest. In Brazil, this area is located in the middle and in the southeastern region of the country. In the United States, the corresponding region is located in the eastern portions of the Great Plains and in the so-called Corn Belt in the American Midwest (the central section of the country). In China, there are two production areas: the southeastern region and part of the northwest of the country (USDA, 1994). To carry out this research, different reference localities (stations) situated in the aforementioned areas were used (Table 1). The reference localities were selected primarily for their geographic location. An additional selection criterion was the quality of the information recorded. The information chosen for use was to be based on monthly records that did not include missing data.

Table 1. Location of the reference stations
  LatitudeLongitude  LatitudeLongitude
  1. The abbreviations of the names of the stations used in the following tables are shown in parentheses.

ArgentinaFormosa (For)26.2 S58.23 OBrazilVilhena (Vil)12.73 S60.13 O
 Reconquista (Rec)29.18 S59.67 O Campo Grande (C.G.)20.47 S54.67 O
 Córdoba (Cor)31.32 S64.22 O Curitiba (Cur)25.52 S49.17 O
 Rosario (Ros)32.92 S60.78 O Irai (Ira)27.2 S53.28 O
 Junín (Jun)34.55 S60.95 O San Luis Gonzaga (SLG)28.4 S54.95 O
USABirmingham (Bir)33.57 N86.75 OChinaChaoyang (Cha)41.55 N120.45 E
 Columbia (Col)38.82 N92.22 O Chengde (Che)40.97 N117.83 E
 Peoria (Peo)40.67 N89.68 O Han Shou (HS)28.8 N108.77 E
 South Bend (SB)41.7 N86.32 O Bijie (Bij)27.3 N105.23 E
 Toledo (Tol)41.6 N83.8 O    

To compare the behaviour of climate in these regions in advance of crop yields, the extreme monthly temperatures (maximum and minimum) and the accumulated precipitation in each month over the common period 1979–2006 were analysed. The climatic database was obtained from the Climate Prediction Centre (CPC) and the National Centres for Environmental Prediction (NCEP) and the FAO (Food and Agriculture Organisation) crop data.

The study was carried out by examining the growth seasons and their component stages for both crops. The stages and the months included are shown in Table 2.

Table 2. Stages of corn and soya bean growing seasons for different regionsThumbnail image of
  • Arg, Argentina; Bra, Brazil; USA, United States; Chi N, northern area of China and Chi S, southern area of China.

  • Because the series of crops show year-to-year fluctuations and significant positive trends, the last-described series were filtered out in order to isolate the first series (Llano and Vargas, 2009). The goal of this filtering approach was to achieve a consistent fit in which the trend in the crop yields was independent of the variability in climate.

    To study the climate efficiency of these crops, an index defined as the ratio between the area averages of accumulated precipitation throughout each cycle and the annual crop yields was used.

    For estimating crop yields based on climatic variables multiple regression models were used according to Thompson (1969). The multiple regression technique allows reconstruction of the crop yield variable in terms of several climate parameters. This allows comparison of climate impacts on crops in the regions involved.

    3. Results

    1. Top of page
    2. Abstract
    3. 1. Introduction
    4. 2. Data and methods
    5. 3. Results
    6. 4. Conclusions
    7. Acknowledgements
    8. References

    3.1. Climatic characterization

    As a starting point for the study, a climate analysis of the reference stations used was carried out to observe the behaviour of the variables throughout the year (Table 3). The minimum temperatures are not shown, because it will be shown below that when both precipitation and maximum temperature are included in the analysis the minimum temperature did not contribute additional information about the crop yield.

    Table 3. Mean monthly maximum temperature and cumulative monthly precipitation during the study period (1979–2006)
     MonthMaximum temperature
    ChinaChe.− 22.69.818.725.228.729.928.824.317.37− 0.6

    In Argentina, the five selected localities behaved in similar ways in the months between May and October. These months were the driest in the study period, with values of accumulated precipitation close to 50 mm. In the remaining months, the accumulated precipitation exceeded 100 mm and reached values of as much as 150–200 mm. Precipitation in Argentina has a high regional variability. Regional precipitation exhibits a very well defined annual cycle in the north and northeast. It exhibits a uniform distribution with small maxima during spring and autumn in the east central area and northeast Argentina. Precipitation in the south is scarce (Hoffmann, 1975; Schwerdtfeger, 1976).

    The greatest annual precipitation during most months was registered in Formosa. The yearly accumulated average was 1419 mm. The results for Reconquista showed that most precipitation occurred during the transition months. The annual accumulated value was 1162.5 mm. The total annual precipitation for Córdoba was 917.9 mm; the low rainfall during the winter months was reflected in this value. In Rosario, the greatest accumulations were observed in the months of April to May and November to December. The annual accumulation value recorded was 943 mm. In Junín, the winter months exhibited the lowest values. The annual accumulated total was 993.9 mm.

    The five reference stations for Argentina were similar in their temperature regime. These stations had hot summers and cool winters. A distinctive annual wave according to the geographical location (north–south) was exhibited by the recorded values. During summer, those values ranged between 28 and 34 °C, whereas in winter the values fluctuated between 14 and 24 °C.

    The precipitation values for Brazil differed across stations. The greatest variability throughout the year in accumulated precipitation was exhibited by the two northernmost stations, Vilhena and Campo Grande. A difference between the summer (wet) and winter (dry) months was exhibited by these stations. Throughout the entire year, in Vilhena the total accumulated precipitation was 1719.6 mm and in Campo Grande 1080.1 mm.

    Curitiba did not show great within-year variation, resulting in a cumulative annual total of 1180.6 mm. Irai and San Luis Gonzaga, the two southernmost stations, exhibited less intra-annual variability. The annual accumulation for the two stations exceeded 1800 mm.

    The maximum temperature values also revealed differences between the stations located in the north and those located more to the south. Vilhena and Campo Grande exhibited little variability throughout the year, with amplitude of 4 °C. A strong annual difference was shown by the three stations in the south. Curitiba had the greatest amplitude, a value of 12 °C. The two remaining stations exhibited very similar behaviour, with an amplitude of 10 °C degrees.

    In the United States, the five stations showed similar precipitation regimes. Columbia exhibited an average annual accumulation of 1013.7 mm. In Peoria the annual accumulation was 862 mm. Toledo presented the same pattern observed for Columbia, with an accumulation of 848 mm. throughout the year. For South Bend, the value of accumulation was 950 mm. Birmingham, the southernmost United States station included in the study, exhibited a different precipitation regime and an annual accumulation of 1370 mm.

    Maximum temperatures for the stations analysed exhibited a strong annual pattern. Maximum winter temperatures were less than 5 °C. Maximum temperatures during the summer exceeded 30 °C.

    For the purposes of this study, China had been divided into two areas. The northern area containing the Chaoyang and Chengde stations and the southern area swith Han Shou and Bijie stations. The two northern stations had very similar regimes and almost identical accumulation values for monthly precipitation. The winters were very dry, whereas the summers were wet. Throughout the year, the accumulated precipitation was relatively low, near 450 mm. The southern stations exhibited the same annual pattern, but specific values differ. The winter months were dry, but the extremes of the north were not reached. The summer months were wetter. The annual precipitation accumulation in Han Shou was 1295 mm and in Bijie 860 mm.

    The maximum temperature again revealed differences between the north and the south. All the stations exhibited a distinct annual pattern (wave). At the northern stations, the winter months exhibited maximum temperatures less than 0 °C. Summer maximum temperature reached 30 °C. At the southern stations, variability was not as strong because winters were less harsh.

    Based on the behaviour observed at the different stations, it was decided to represent the variables in the regions by area averages of the reference season data. In Brazil and China, the analysis was carried out for two areas, a northern zone and a southern zone. The analyses for Argentina and the United States focussed on one area for each country (Table 4).

    Table 4. Monthly values of maximum temperature and accumulated precipitation regional for the average of reference stations
    MonthMaximum temperature
    Bra N29.029.029.429.528.027.628.
    Bra S30.229.528.725.822.220.420.522.622.925.627.629.5
    Chi N−− 0.3
    Chi S7.08.713.319.422.925.628.328.224.318.914.59.3
    1. Arg, Argentina; Bra N, northern area of Brazil; Bra S, southern area of Brazil; USA, United States; Chi N, northern area of China and Chi S, southern area of China.

    Bra N220.9192.9176.3110.764.723.314.129.972.9139.2153.9201.4
    Bra S147.3156.5122.2141.3131.6116.0109.8104.6144.5176.5124.3146.9
    Chi N1.
    Chi S24.528.943.786.2148.6186.5174.4144.996.184.241.818.4

    3.2. Regional climate environment

    In this part of the study the maximum temperature and the monthly precipitation accumulation were considered together in order to compare ranges and annual behaviour between regions (Figures 1 and 2). In Argentina, September marks the start of the corn cycle (Cs). This agricultural season is 9 months long and includes the three main stages of crop development: planting, flowering and harvest. The start of planting was generally characterized by maximum temperatures that were not extremely high and by low precipitation (50 mm on average). However, this lack of rainfall was quickly made up by the average accumulation values in the following months, especially during the flowering stage (December-January-February). In the ensuing harvest months, temperatures began to drop. Precipitation remained at the same value except during the last month of harvest (Ce), when precipitation values fell abruptly. Soya bean has a cycle that lasts for 7 months. Since the beginning of the cycle (Ss) in November, the precipitation values surpassed 100 mm in most months. The exception was harvest time in May (Se).

    thumbnail image

    Figure 1. Monthly mean maximum temperature versus accumulated monthly precipitation average for Argentina, Brazil North and Brazil South. It indicates the starting month and ending month of corn and soya bean growth. Cs - Corn starting campaign Ss - Soybean starting campaign Ce - Corn ending campaign Se - Soybean ending campaign equation image Arg equation image Bra N equation image Bra S

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    thumbnail image

    Figure 2. As Figure 1 except for United States, China North and China South. Cs - Corn starting campaign Ss - Soybean starting campaign Ce - Corn ending campaign Se - Soybean ending campaign equation image USA equation image Chi N equation image Chi S

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    In the northern Brazil area, the months involved in both growing seasons presented the same temperature range. Recorded amounts of precipitation were greatest at the start of both cycles in November (Cs-Ss) and in the following months. A difference occurred during the harvest stage, where the recorded accumulation decreased and reached values near 50 mm. The average for the southern stations exhibited higher temperatures at the start of both cycles and ended with lower values at harvest time. Precipitation values during all of the cycle surpassed 100 mm and reached their maximum during flowering.

    When the behaviour of the average values for reference stations in the United States was analysed it could be clearly seen that the range in the maximum temperature values was very different from that seen for Argentina and Brazil. During the planting and flowering months, the temperatures showed a greater range and very low variation in precipitation. During the course of the year, the temperature increased in a stepped way. The same sort of increase occurred with precipitation, which reached a maximum during the spring months, generally during May (Ss). The maximum temperature occurred in July to August (flowering months), accompanied by high precipitation values.

    For China, the average of both stations in the north was unusual. The transition months (spring for planting and autumn for harvest) had the same values both for maximum temperature and for precipitation. The maximum temperature exhibited wide variation. These values were below 0 °C in winter and reached 30 °C in summer (flowering). Precipitation varied from the dry season to the warm months (the accumulated precipitation reached 100 mm.).

    For the average of the southern stations, the shape of the curve changed. Maximum temperatures were higher than the temperatures recorded at the northern stations during the winter months. No difference between the two areas was observed for the warm months. In this case precipitation also varied from the dry winter to the wet warm season, but in this region the precipitation surpassed 150 mm (associated with flowering).

    3.3. Adaptability

    The previous analyses revealed that the studied crops were exposed to a wide range of precipitation and maximum temperatures during their development. The data exhibited different patterns of relationships between precipitation and maximum temperature.

    To complete the study, the behaviour of the precipitation during each cycle was analysed in terms of the regional averages for each zone. The average precipitation values and standard deviations are shown in Table 5. Similarly, Table 6 indicates the average values and standard deviation of detrended crop yields.

    Table 5. Mean values of accumulated precipitation during the growing season (in millimetres) and standard deviation value
    Precipitation (mm)CornSoya bean
    1. Period 1979–2006.

    Brasil N.11622611137255
    Brasil S.1093236973229
    China N.415110443113
    China S.814132921140
    Table 6. Mean and standard deviation of the corn and soya bean yields in the four countries studied
    Yield (kg ha)CornSoya bean
    1. Period 1979–2006.


    When both tables are analysed, it can be seen that the regions with most precipitation accumulation (both in Brazil) had the most variability. The regions with the greatest yield (United States and Argentina) also exhibited substantial variability, i.e. the yield reflected rainfall characteristics.

    To compare the adaptability or climatic efficiency of the crops in the areas, this property will be represented by deriving a coefficient relating precipitation to crop yields. The variability along the record of the coefficient is shown in Figures 3 and 4. In addition, the type of yield for each cycle is highlighted. The 27 growing areas were divided into three groups: high (h), medium (m) and low (l) yield, each of them with nine cycles. In each country the thresholds that separated in categories were different, they depended of the yield values.

    thumbnail image

    Figure 3. Evolution over time of the index (precipitation/yield) for the six regions under study. For the cultivation of corn. (h, high; m, medium; l, low yield in the growing season). equation image Argentina equation image Brazil north equation image Brazil south equation image USA equation image China north equation image China south

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    thumbnail image

    Figure 4. As in Figure 3 except for the cultivation of soya bean. equation image Argentina equation image Brazil north equation image Brazil south equation image USA equation image China north equation image China south

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    It could be seen that the two areas in Brazil had the lowest adaptability for corn, whereas the adaptability was highest in the United States and the northern region of China. On the other hand, the variability of this ‘index’ was determined by the variability of the precipitation more than by the range of the yields. This fact was evident because the ranges (high, low and medium) were distributed throughout the years in random way, without differentiating between maximum or minimum values of the adaptability coefficient.

    In the case of soya beans, the index showed that northern Brazil and China had the lowest adaptability to rain, although Brazil showed a change in the 1990s, when the year-to-year variability also increased. This latter behaviour was shared by southern Brazil. The maximum adaptability was recorded for the United States, Argentina and northern China.

    3.4. The crop yield—climatic variables relation

    As a part of the study of the relationship between crop yields and climate variables, multiple regression analysis was carried out. The parameters used in this methodology represented regional average climate variables in each one of the zones defined previously.

    Multiple regression models were developed based on specifications found in study by Thompson (1962). The author modelled corn yields for different states of the United States from 1930 to 1962. The model included climate variables: precipitation and temperatures. This kind of model has been adopted by several others such as Schlenker and Roberts (2006) and Tannura et al. (2008).

    In the first multiple regression model, the yield (Y) was modelled by three study variables: the accumulated precipitation (pp) during the growing season and the average values of the maximum temperature (Tx) and minimum temperature (Tn) throughout the entire cycle:

    • equation image(1)

    The difference between the original yield and the yield predicted was represented by δ.

    The existing dependence between precipitation and monthly maximum temperature was studied for each of the different stages of development of the crop and for the entire cycle of the regional averages. In the cases where the relationship between temperature and precipitation was significant, the effect was filtered out and the analysis was performed with the part of the maximum temperatures that was not explained by precipitation. Table 7 shows the correlation coefficients between the variables.

    Table 7. Correlation coefficient of regional averages of maximum temperature and precipitation on the growth stages
     CornSoya bean
     Growing stagePlantingFloweringHarvestGrowing stagePlantingFloweringHarvest
    1. Bold values show the existence of a linear relationship between the variables significant at 5%.

    Arg− 0.157− 0.2920.329− 0.131− 0.2970.608− 0.23− 0.089
    Bra N0.280.1860.0340.0180.2660.1860.037− 0.16
    Bra S− 0.2650.333− 0.307− 0.2820.410.333− 0.1080.368
    USA− 0.2720.410.577− 0.260.400.4320.547− 0.26
    Chi N− 0.142− 0.1190.475− 0.189− 0.192− 0.1190.369− 0.097
    Chi S0.1360.069− 0.140.621− 0.1040.2560.601− 0.139

    The second model was similar to the first, but it excluded the minimum temperature. In this two-variable model, the relationship between the variables used was more controlled, Tx′ represented the part of the maximum temperature that was not explained by the precipitation:

    • equation image(2)

    The last model used examined the behaviour of precipitation (pp) and the maximum temperature (Tx) during each of the three stages into which the growing season was divided (planting—flowering—harvest). At this point, the linear relationship existing between the variables involved at each stage was filtered again. The equation for this model was:

    • equation image(3)

    Because the third theoretical model gave the best fit to the crop yield data, these results were presented mainly in tables and figures. The results for the others theoretical models only were commented on. Table 8 shows the values of the regression coefficients for the third model (Equation (3)).

    Table 8. Values of the coefficients an (with n = 0, …, 6) of the third linear regression model that includes the maximum temperatures and precipitation in the stages of crops
    ArgCorn13371.50.461.940.2327.3− 330.6− 83.7
     Soya bean7050.9− 0.071.40− 0.55− 54.3− 82.6− 66.5
    Bra NCorn1100.0− 0.40− 0.07− 1.5161.136.2− 63.4
     Soya bean2633.2− 0.210.03− 0.36− 66.0− 6.743.3
    Bra SCorn3251.10.410.68− 0.79− 69.110.0− 0.6
     Soya bean3446.30.001.41− 0.36− 111.1− 75.5126.3
    Chi NCorn4322.90.010.690.57− 50.0− 45.832.6
     Soya bean1495.90.68− 0.080.440.51.7− 27.0
    Chi SCorn3984.6− 0.68− 0.111.50− 22.0− 67.217.6
     Soya bean521.90.140.11− 0.3119.34.0− 2.0
    USACorn16021.5− 1.23− 0.78− 1.71144.3− 483.3129.0
     Soya bean4377.7− 0.34− 0.28− 0.2442.2− 115.24.0

    The coefficient of determination (R2) was used to measure of the goodness of fit of the theoretical models. This coefficient measured the rate of change (percent) of crop yield that could be explained by climatic variables.

    3.4.1. Corn crops

    Table 9 shows the mean crop yield values of the regions, along with their standard deviations. These values were displayed according to the yield generated by the theoretical model that includes the maximum temperature and precipitation during each stage. The last column shows the value of the coefficient of determination.

    Table 9. Mean values of the original yield and standard deviation
    CornOriginal yieldStages
     AverageDeviationDeviationRestR2 (%)
    1. Summary of deviations, rest and coefficient of determination for the third theoretical model employed.

    Brazil N1660.5199.5148.6130.728.7
    Brazil S1660.5199.5138.899.748.4
    China N2887.5241.5137.3160.732.3
    China S2887.5241.5121.3168.325.2
    Soya bean  
    Brazil N1560.8195.791.5135.321.8
    Brazil S1560.8195.7146.6104.956.1
    China N1030.590.
    China S1030.590.031.069.411.8

    Beginning with the analysis for Argentina, the third linear regression model showed that the precipitation values and maximum temperatures, evaluated by stages, explained 58% of the crop yield variability. Figure 5 shows a better fit to the generated yield, mainly in the ‘peaks’ observed in the record at different stages. A better fit was obtained near the end of the period, but the observed variability was not well represented at the beginning.

    thumbnail image

    Figure 5. Detrended yield of corn in four producing regions (solid line) and yield generated according to the third linear regression model with the maximum temperature and precipitation data in the three stages of crop development (dotted line)

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    For the model applied to the regional average for northern Brazil, the coefficient of determination reached a value of 29%. Figure 5 shows that the variability displayed by the crop yield was well represented. However, the ‘peaks’ in the crop yield were not as well represented by the model.

    For the southern area of Brazil, when the regression model was used with the climatic variables represented by stages, the fit improved noticeably relative to that of the other models. The coefficient of determination showed that the values of accumulated precipitation (stage-wise fit) and the averages of their maximum temperatures represented 48% of the variability of the crop yield of corn.

    For the United States, when the third linear regression model was used the results showed that the use of accumulated precipitation and of average maximum temperatures explained 73% of the variability in yield. Figure 5 shows the resulting regression and the observed yield values. This model fitted the variability, as well as the ‘peaks’ of extreme yield.

    For the northern region of China, the values of the coefficient of determination did not differ between the theoretical models that used the data from the cycles and the regression model that uses the data of the variables in the stages. The value of coefficient of determination in all cases was around 35%.

    For southern China, using the third model according to stages increased the resulting variability, but the model was still unable to represent extremes. In this case, precipitation and temperature only explained 25% of the variability in the efficiency of corn production.

    3.4.2. Soya bean crops

    The theoretical regression model (specifically, the third model) applied to the soyabean crop in Argentina exhibited an increased coefficient of determination. The model successfully represented the variability of the crop, including extreme cycles such as 1988–1989, 1997–1998 and 2003–2004.

    For the northern part of Brazil, theoretical models did not represent the yield as well. The third model according to stages could not fit such cases of extreme yield as the 1990–1991, 2000–2001, 2002–2003 and 2004–2005 growing seasons. For the southern part of Brazil, the performance of this model was better than it was for the northern part. The variables included explained 56% of the variability in soya bean crop yield.

    The United States data exhibited behaviour similar to that observed for corn crops in Argentina. A stage-level theoretical regression model yielded a coefficient of determination of 56%.

    For northern China, the theoretical models did not adequately represent the variability in the yields of soya bean. The value of the coefficient of determination was 8% when stage-level model was used. Results for the southern part of China did not differ greatly from those for the northern part.

    4. Conclusions

    1. Top of page
    2. Abstract
    3. 1. Introduction
    4. 2. Data and methods
    5. 3. Results
    6. 4. Conclusions
    7. Acknowledgements
    8. References

    In the regions studied, the corn and soya bean growth seasons occupy between 7 and 9 months. In most of these areas, the briefest soya bean cycles occur during the same time as those of corn. Both the maximum temperatures and precipitation showed that the climate variables could attain wide ranges of values in the regions where both crops were produced.

    The climatic study conducted for the reference stations concluded that for the purpose of the analysis it was necessary to divide some countries into sub-regions. The division used consisted of one single region each for Argentina and for the United States, and two regions each (north and south) for Brazil and China.

    The regions to be analysed were selected according to their production capacity. These regions corresponded to the world's major corn and soya bean producing nations.

    The variable that characterized crops was yield. Yield exhibited a strong trend. This trend could be attributed in large measure to technological advances. The trend was removed from the data to reveal possible effects of climatic variables.

    Precipitation was the climatic variable that had the greatest influence on crop yields. This result followed from the fact that, in general, greater mean precipitation in the area was associated with greater yield. However, this effect depended strongly on the distribution of the precipitation in each growth stage of the crop. The adaptability of the crops in each area was studied. This study examined the efficiency of improved yield in terms of the annual temporal evolution of the coefficient between precipitation and yield. For corn crops, the United States and northern China exhibited the greatest benefit in conjunction with the lowest index values throughout the record. For soya bean, this index showed a great year-to-year variability. Argentina, United States and northern China exhibited the greatest adaptability.

    Different linear regression models were used to model the behaviour of crop yields. These models included the measured climate variables during the growing season and during the different stages. For most areas studied the models offered a satisfactory representation of the yields. The model that used the maximum temperature and accumulated precipitation according to each stage of the cycle yielded the best results. This finding was confirmed by the analysis of the coefficient of determination.

    For corn crops, the percentage of variance explained by the maximum temperature and accumulated precipitation in the stages varied from 25% in southern China to 73% in the United States. For soya bean crops, lower values of this coefficient were recorded for all the theoretical models used. It was once again observed that the stage model best represented the behaviour of crop yields. The highest coefficient values were obtained for southern Brazil and the United States (56%).


    1. Top of page
    2. Abstract
    3. 1. Introduction
    4. 2. Data and methods
    5. 3. Results
    6. 4. Conclusions
    7. Acknowledgements
    8. References

    This research was sponsored by projects UBA X228, CONICET PIP 112-200801-00762 and FONCyT PICT 2008-1820.


    1. Top of page
    2. Abstract
    3. 1. Introduction
    4. 2. Data and methods
    5. 3. Results
    6. 4. Conclusions
    7. Acknowledgements
    8. References
    • USDA 1994. Major World Crop Areas and Climate Profiles, Agricultural Handbook N° 664. United States Department of Agriculture: Washington, DC.
    • Chmielewski F, Potts J. 1995. The relationship between crop yields from an experiment in southern England and long term climate variations. Agriculture and Forest Meteorology 73: 4366.
    • Hoffmann J. 1975. Atlas Climático de América del Sur. Organización Meteorológica Mundial (OMM), UNESCO: Paris.
    • Llano MP, Vargas W. 2009. Relación entre variables climáticas y rendimiento de cultivos en las zonas de mayor producción. X Congreso Argentino de Meteorología., 5 al 9 de octubre de 2009, Buenos Aires, Argentina. Centro Argentino de Meteorólogos: Buenos Aires.
    • Magrin G, Trabaos M, Rodríguez G. 2005. Changes in climate and crop production during the 20th century in Argentina. Climatic Change 72(1–2): 229249.
    • McCaig T. 1997. Temperature and precipitation effects on durum wheat grown in southern Saskatchewan for fifty years. Canadian Journal of Plant Science 77: 215223.
    • McKeown AW, Warland J, McDonald MR. 2006. Long-term climate and weather patterns in relation to crop yield: a minireview. Canadian Journal of Botany 84: 10311036.
    • Minetti J, Lamelas C. 1995. Respuesta regional de la soja en Tucumán a la variabilidad climática. Revista Industrial y agrícola de Tucumán 72(1–2): 6368.
    • Montoya J, Domínguez G. 2004. Efectos de los factores meteorológicos sobre el rendimiento del maíz. Nota Técnica del Instituto Nacional de Tecnología Agropecuaria 81: 115.
    • Nagy J, Huzsvai L. 1996. The effect of precipitation on the yield of Maite (Zea mays L.). Cereal Research Communications 24(1): 93100.
    • Pascale AJ. 1969. Tipos agroclimáticos para el cultivo de la soja en la Argentina. Revista de la Facultad de Agronomía y Veterinaria de Buenos Aires 17(3): 3148.
    • Penalba O, Bettolli M, Vargas W. 2007. The impact of climate variability on soybean yields in Argentina. Multivariate regression. Meteorological Applications 14: 314.
    • Robb A. 1934. The critical period of corn in Northeastern Kansas. Monthly Weather Review 62: 286289.
    • Rose JT. 1936. Corn yield and climate in the Corn Belt. Geographical Review 26: 88102.
    • Schwerdtfeger W. 1976. Climates of central and South America. World Survey of Climatology 12: 532 pp.
    • Schlenker W, Roberts MJ. 2006. Nonlinear effects of weather on corn yields. Review of Agricultural Economics 28: 391398.
      Direct Link:
    • Smith W. 1914. The effect of weather upon the yield of corn. Monthly Weather Review 78: 7893.
    • Tannura MA, Irwin SH, Good DL. 2008. Weather, technology, and corn and soybean yields in the U.S. corn belt. Marketing and Outlook Research Report 2008-01, Department of Agricultural and Consumer Economics, University of Illinois: Urbana-Champaign, IL.
    • Thompson LM. 1962. An evaluation of weather factors in the production of corn. Report No. 12T, Center for Agricultural and Rural Development, Iowa State University: Ames, IA.
    • Thompson L. 1969. Weather and technology in the production of corn in the U. S. corn belt. Agronomy Journal 61: 453456.
    • Thompson L. 1970. Weather and technology in the production of soybeans in the Central United Status. Agronomy Journal 62: 232236.
    • Wallace H. 1920. Mathematical inquiry into the effect of weather on corn yield in the eight corn belt status. Monthly Weather Review 48: 439446.