e c o l o g i c a l m o d e l l i n g 2 1 0 ( 2 0 0 8 ) 312–326
avai lab le at www.sc iencedi rec t .com
journa l homepage: www.e lsev ier .com/ locate /eco lmodel
Impact of generated solar radiation on simulatedcrop growth and yield
Axel Garcia y Garcia ∗, Larry C. Guerra, Gerrit HoogenboomThe University of Georgia, Department of Biological and Agricultural Engineering, 1109 Experiment Street, Griffin, GA 30223, USA
a r t i c l e i n f o
Article history:
Received 21 March 2007
Received in revised form
24 July 2007
Accepted 7 August 2007
Published on line 27 September 2007
Keywords:
Stochastic solar radiation
Cropping System Model (CSM)
Decision Support System for
Agrotechnology Transfer (DSSAT)
Water use
Irrigation
Rainfed
a b s t r a c t
The availability of observed daily solar radiation (OSR) is restricted to recent years. Its estima-
tion through different methods is necessary to develop long-term data sets for agricultural
and environmental applications. The objective of this study was to analyze the impact of
using generated daily solar radiation (GSR) on simulated growth and yield of cotton, maize,
and peanut. Nine locations representing Georgia’s major crop belt were selected. Daily
weather data from the Georgia Automated Environmental Monitoring Network (AEMN),
including solar radiation, maximum and minimum temperature, and precipitation, were
duplicated. The OSR was removed from one set and then generated using a stochas-
tic procedure. The Cropping System Models (CSM)-CROPGRO-Cotton, CERES-Maize, and
CROPGRO-Peanut of the Decision Support System for Agrotechnology Transfer (DSSAT) v4
were used to simulate crop growth and yield at each location with both OSR and GSR and for
rainfed and irrigated conditions. The statistical analysis included summary statistics, Pear-
son’s coefficient of correlation, mean squared deviation (MSD) and its components, namely:
squared bias (SB), squared difference between standard deviations (SDSD), lack of correla-
tion weighted by the standard deviations (LCS), and regressions. Within locations, for the
three crops under rainfed and irrigated conditions, GSR did not significantly affect simu-
lated total evapotranspiration and aboveground biomass and yields. For the three crops,
deviations of simulated water use and yields from GSR with respect to simulated water use
and yields from OSR were lower for the rainfed than for the irrigated conditions. Yields from
the CSM-CROPGRO-Cotton and -Peanut models had lower deviations than yields from the
CSM-CERES-Maize model. LCS was the major component of the MSD suggesting that the
extent of the difference between standard deviations of GSR and OSRG could affect the out-
puts of the crop models. Nevertheless, for most locations none of the MSD components of
the GSR showed significant correlation with simulated yields and the overall performance
of the models was not affected. It can be concluded based on the results of this study that
GSR can be used as an input for crop model simulation models when OSR is not available.
data for crop model applications (IBSNAT, 1990; Hunt et al.,
1. Introduction
The critical agrometeorological variables associated withagricultural production considered the minimum weather
2001) are precipitation, air temperature, and solar radiation(Hoogenboom, 2000). Precipitation does not control any ofthe plant processes directly. However, indirectly it affects
he plant growth and development through either droughtr excessive water stress. Air temperature regulates theevelopmental rates; solar radiation provides the energy forvapotranspiration and the photosynthesis processes, includ-ng carbohydrates partitioning and biomass growth (Boote andoomis, 1991).
Observed weather data for modeling applications are oftenimited to a few locations due to short recording period,ong periods of missing data, the availability of only monthlyverages or totals, or because only a few variables, mainlyaximum and minimum air temperature and rainfall, are
ecorded. For instance, most of the automatic weather sta-ions of the U.S. National Weather Service (NWS) are locatedn regions where agriculture normally is not an importantconomic sector. Nevertheless, these stations are the mostommon source of weather data for modeling applicationsn the country (Hoogenboom, 2000) despite the possibility ofignificant errors due to poor siting associated with exposureroblems (Hubbard, 1994; Hunt et al., 1998; Rivington et al.,006). Clearly, these constraints will result in erroneous modelutputs, as models are sensitive to the inaccuracy of weatherata due to observation errors (Heinemann et al., 2002; Fodornd Kovacs, 2005). Additionally, it is well documented thatrop models are sensitive to errors from weather data esti-ates from either empirical or stochastic approaches (Meinke
t al., 1995; Hunt et al., 1998; Hansen, 1999; Hartkamp et al.,003; Donatelli et al., 2003; Ball et al., 2004; Grant et al., 2004;arcia y Garcia and Hoogenboom, 2005; Rivington et al., 2005).
Dynamic crop simulation models, such as the Croppingystem Model (CSM), respond to changes and variations in airemperature, solar radiation, precipitation and carbon diox-de (Hoogenboom et al., 1995; Jones et al., 2003). Thus, wehould expect that the errors associated with estimating oner more weather variables using different types of approachesill impact simulated yield that could lead to erroneous con-
lusions on the use of crop model outputs. The impact ofsing generated weather data on the outcome of crop simula-ion models has been well documented. However, most of thetudies are focused on the impact of air temperature and pre-ipitation, with little (Meinke et al., 1995; Soltani et al., 2000;ubrovsky et al., 2000) or no emphasis (Hartkamp et al., 2003)n solar radiation.
Weather data, mainly solar radiation, for model applica-ions are rarely available. If solar radiation is not recorded,t can be estimated based on sunshine hours (Angstrom,924). If sunshine hours are also not available, it can be esti-ated based on air temperature (Bristow and Campbell, 1984;
oodin et al., 1999), or from a combination of precipitation andir temperature through stochastic (Richardson, 1981), neu-al network (Elizondo et al., 1994), or deterministic (Weiss andays, 2004) approaches. The stochastic method, usually calledeather generator, is a numerical model capable of producingne or more weather variables with similar statistical charac-eristics that naturally occur in a given location. The weatherenerator for solar radiation (WGENR), is an adaptation ofhe solar radiation simulation model originally developed by
odges et al. (1985), which was based on Richardson’s (1981)GEN algorithm. While Richardson’s approach uses a set of
ocation-specific constants to generate daily rainfall, air tem-erature, and solar radiation, Hodges uses recorded rainfall
0 ( 2 0 0 8 ) 312–326 313
and air temperature to generate solar radiation. The WGENRhas been used for studying the ability of the CERES-Maizemodel on simulating the annual fluctuation in maize produc-tion for the U.S. corn belt (Hodges et al., 1987), for climatechange studies (Cooter, 1990; Smith and Tirpack, 1990), for pre-dicting daily solar radiation from interpolated climate records(Grant et al., 2004), and for improving its empirical parameters(Cooter and Dhakhwa, 1995; Garcia y Garcia and Hoogenboom,2005).
The objective of this study was to analyze the impact ofusing generated daily solar radiation on simulated growth andyield of cotton, maize, and peanut. Specific objectives were todetermine the extent of the components of the total devia-tion (MSD) from generated and observed solar radiation onsimulated water use and yield of cotton, maize, and peanut.
2. Materials and methods
2.1. Observed weather data
Complete records of daily solar radiation, maximum and min-imum air temperature, and rainfall recorded with automaticweather stations from nine locations were used for this study.The weather stations are part of the Georgia Automated Envi-ronmental Monitoring Network (AEMN) and are located in theregion where most of the traditional row crops are grown inGeorgia (Fig. 1). The records for each location varied from 7 to12 years for a total for 91 years of data (Table 1).
2.2. Generated daily solar radiation
The daily observed weather data from the nine locations wereduplicated and the solar radiation was removed from one set.Then, the removed solar radiation was generated using theWGENR (Hodges et al., 1985), as modified by Garcia y Garciaand Hoogenboom (2005). The simplified WGENR algorithm is
SR = SRL × SRSD + SRBAR (1)
where SR is the generated daily solar radiation. SRBAR cor-responds to annual curves of long-term average daily valueswith separate curves for dry and wet days. The wet days aredefined as those days with rainfall greater than zero. SRBARis defined as
SRBAR = RM + RA{cos[0.0172(j − 172) + a]} (2)
where RM and RA are the respective average annual dailyand amplitude SR value for either wet or dry days, j = 1, 2,3, . . ., 365, a = 0.0 for the northern hemisphere and a = 183for the southern hemisphere. SRL, a random component,is a function of two 3 × 3 matrices originally developed byRichardson (1981) to describe the cross-correlations betweendaily minimum and maximum temperatures and SR for thecontinental United States. Daily deviations estimated from
these empirical cross-correlations were then multiplied bythe standard deviation (SRSD) conditioned to wet or dry days.The main difference between WGEN (Richardson, 1981) andWGENR (Hodges et al., 1985) algorithms is that Richardson’s
314 e c o l o g i c a l m o d e l l i n g 2 1 0 ( 2 0 0 8 ) 312–326
ther
Fig. 1 – Locations of the wea
approach uses a set of location-specific constants to estimatedaily rainfall, SR, and maximum and minimum temperature
whereas Hodges et al. (1985) proposal uses recorded max-imum and minimum temperature and rainfall to generateSR. Thus, its use is not restricted to continental USA andcould certainly be extended to wider climate scenarios as
Table 1 – Location of the automatic weather stations used in th
recorded maximum and minimum air temperature andrainfall are normally available for long-term weather data.
WGENR was selected because of its potential for use not onlywith crop model applications (Hodges et al., 1985) but alsofor climate change studies (Cooter, 1990; Smith and Tirpack,1990).
he Decision Support System for Agrotechnology TransferDSSAT) is a suite of computer programs that facilitate thepplication of crop simulation models. It includes capabil-ties for solving problems at field, farm, and higher levelsJones et al., 2003) such as application programs for sea-onal and sequence analyses that assess the economicisks and environmental impacts associated with irriga-ion, fertilizer and nutrient management, climate change,oil carbon sequestration, climate variability and precisionanagement (Hoogenboom et al., 2004). DSSAT is based
n the Cropping System Model (CSM), which simulatesrowth, development, and yield for a particular crop thats grown in a uniform area under prescribed or simulatedrop management. CSM is a dynamic model that simu-ates the changes in plant and soil water, carbon, anditrogen of a cropping system over time. The CSM incor-orates all crops as modules using a single soil modelnd a single weather module. Solar radiation is the pri-ary driving force for the simulation of crop biomass. Two
pproaches are used: (a) a canopy carbon assimilation pho-osynthetic process in CSM-CROPGRO models by either dailyanopy photosynthesis or hourly hedgerow light intercep-ion and leaf-level photosynthesis (Boote and Pickering, 1994)ptions and (b) a conversion of daily intercepted photosyn-hetically active radiation into plant dry matter with these of a crop-specific radiation use efficiency parameter
or the CSM-CERES models; light interception is computeds a function of leaf area index, plant population andow spacing (Ritchie et al., 1998; Jones et al., 2003). Sinceoth the CSM-CROPGRO and CSM-CERES have solar radia-ion as a primary driven force for biomass production andanopy transpiration, bias from generated solar radiationost likely affect the crop’s assimilation and transpiration
ates.
.4. Crop water use, growth, and yield simulations
he CSM-CROPGRO-Cotton, -CERES-Maize, and -CROPGRO-eanut included in the DSSAT v4.0 (Jones et al., 2003;oogenboom et al., 2004), were used to simulate water use orvapotranspiration (ETC), growth and yield of cotton, maize,nd peanut. The cultivars were the hybrid DP555BG/RR forotton, the hybrid PI38G for maize, and the variety Geor-ia Green for peanut. Both irrigated and rainfed conditionsere considered, as these are common management prac-
ices in the southeastern USA. The planting dates for rainfednd irrigated conditions were, respectively, April 20 anday 10 for cotton, March 15 and March 25 for maize, andpril 20 and May 1 for peanut. The soil profile informationas obtained form the National Resources Conservation Ser-
ices of the USDA (NRCS/USDA, 2003) and Perkins (1987);he nearest available information was used when no dataere available for a specific site. The simulation options were
et for using the Priestley and Taylor (1972) evapotranspi-
ation method and for the irrigated scenario, irrigation waspplied when necessary. The models were run for each loca-ion using the two weather data sets that included OSR andSR.
0 ( 2 0 0 8 ) 312–326 315
2.5. Statistical analysis
For each location, the summary statistics and the Pearson’scoefficient of correlation (r) were calculated for the pair ofdata sets GSR and OSR, total crop evapotranspiration fromGSR (ETCG) and OSR (ETCO), and simulated total abovegroundbiomass and yields based on GSR (YG) and OSR (YO). The devi-ations of the GSR from OSR were determined from the dailydata of each cropping season starting on March 1st and endingon October 31st. As the only cause of variation between eachpair of data set was the source of solar radiation, OSR, ETCO,and YO were used to analyze the deviations of GSR, ETCG, andYG through the mean squared deviation (MSD) and its com-ponents (Eq. (3)) (Kobayashi and Salam, 2000). This approachallows for the calculation of each of the three components ofthe deviation, namely: squared bias (SB, Eq. (4)), squared dif-ference between standard deviations (SDSD, Eq. (5)) and lack ofcorrelation weighted by the standard deviations (LCS, Eq. (6)):
MSD = SB + SDSD + LCS (3)
SB = (x − y)2 (4)
SDSD =
⎛⎝
√√√√1n
n∑i=1
(xi − x)2 −
√√√√1n
n∑i=1
(yi − y)2
⎞⎠
2
(5)
LCS = 2
⎛⎝
√√√√1n
n∑i=1
(xi − x)2
⎞⎠
⎛⎝
√√√√1n
n∑i=1
(yi − y)2
⎞⎠ (1 − r) (6)
SDs =
√√√√1n
n∑i=1
(xi − x)2 (7)
SDm =
√√√√1n
n∑i=1
(yi − y)2 (8)
where x and y are the means of the generated (xi) and observed(yi) daily solar radiation, total evapotranspiration, or yielddata (i = 1, 2, 3, . . ., n) and SDs (Eq. (7)) and SDm (Eq. (8)) are thestandard deviations for GSR and OSR, ETCG and ETCO, or YG
and YO, respectively. The lower the values of MSD, the closerthe GSR, ETCG, or YG are to OSR, ETCO, or YO, respectively.The SB represents the bias of the GSR, ETCG, or YG from OSR,ETCO, or YO, respectively. A larger SDSD indicates that GSRfailed to simulate the extent of the OSR, ETCO, or YO variationwithin locations, respectively. A bigger value of LCS indicatesthat GSR failed to simulate the pattern of the OSR, ETCO, orYO variation across the period considered, respectively. Themean squared variation (MSV) corresponds to SDSD + LCS;a bigger MSV value indicates that GSR failed to simulatethe variability of the OSR, ETCO, or YO around the average,respectively. The MSD-based analysis also includes r as a
constituent (Kobayashi and Salam, 2000).
Assuming that errors associated with GSR are small, andhence, will impact the least the outputs of crop models, weshould expect YG is highly correlated to YO. Therefore, we
i n g
316 e c o l o g i c a l m o d e l l
could expect an association between YG and YO and the MSDof GSR (MSDGSR), and consequently, a dependence of YG to YO
and MSDGSR, as expressed in the following equation:
YG = b(YO + MSDGSR) + ε (9)
From Eqs. (3)–(6) and then simplifying, Eq. (9) can also be writ-ten as a multiple regression equation (Eq. (10)):
where k is a constant or intercept, including the error (ε) and b1,. . ., b4 are the regression coefficients for variables YO, SDSDGSR,LCSGSR, and SBGSR. Thus, multiple regressions were performedto determine the extent of the Y with respect to Y . The rela-
G O
tive importance of each component of the MSDGSR on YG wasdetermined using the ˇ coefficients of the regression by trans-forming the dependent and independent variables to z-scoresbefore running the regression. The ˇ coefficients are all mea-
Fig. 2 – Monthly averages for daily observed and generated solartemperature, and total rainfall.
2 1 0 ( 2 0 0 8 ) 312–326
sured in standard deviations and represent a unique predictiveimportance of the independent variables. Because the ˇs mayunderestimate joint contributions of a variable that does notmake a contribution alone, the correlation of the independentvariable with the dependent variable is also reported. The sta-tistical analyses were performed using SAS-Analyst and theSAS Proc Reg procedure (SAS Institute, 1999).
3. Results
3.1. Local weather conditions
During the cropping season that ranged from March (plant-ing for maize) to October (harvest for cotton) and for allnine locations, daily solar radiation, air temperature, and pre-
cipitation showed a similar tendency within locations. Themaximum solar radiation occurred in May, the driest monthof the cropping season, decreased and stabilized by June andJuly, which was the wettest period of the cropping season,
radiation, observed maximum and minimum air
g 2 1 0 ( 2 0 0 8 ) 312–326 317
amittchtT
3
TwGgotfdtsst
Fig. 3 – Mean squared deviation and its components for
e c o l o g i c a l m o d e l l i n
nd then decreased for the remainder of the season. Theonthly average solar radiation varied from 12.9 MJ m−2 day−1
n October to 23.2 MJ m−2 day−1 in May. The maximum airemperature was recorded from June to August, with July ashe warmest month. The monthly average temperature of theropping season ranged from 8.5 to 27.8 ◦C, with the lower andigher temperatures recorded in Sumter and Toombs coun-ies, respectively. Precipitation varied from 44 mm month−1 inoombs to 165 mm month−1 in Sumter (Fig. 2).
.2. Generated daily solar radiation
he performance of the daily solar radiation generator thatas used in this study was evaluated extensively by Garcia yarcia and Hoogenboom (2005) for 17 locations in South Geor-ia, USA. Their study showed slight underestimations andverestimations for the lower and higher daily solar radia-ion values, respectively. Also, their study showed that therequency of distribution of the GSR, for both dry and wetays, was similar to the frequency of distribution of the OSR for
he same conditions. For this study, monthly averages of dailyolar radiation during the cropping season also showed somelight underestimations and overestimations of the GSR forhe lower and higher daily solar radiation values. However, for
generated average daily solar radiation during the croppingseasons. SDSD, squared difference between standarddeviations; LCS, lack of correlation weighted by thestandard deviations; SB, squared bias.
Table 2 – Summary statistics for simulated cotton seed yield [(seeds + lint), kg ha−1] using observed (OSR) and generated(GSR) daily solar radiation for rainfed and irrigated scenarios
S.D.: standard deviation; Min: minimum; Max: maximum. ¶Average yields within location are significantly different (P < 0.05).
i n g
318 e c o l o g i c a l m o d e l l
all locations GSR follows the same tendency of the observeddata (Fig. 2).
Based on the MSD approach, Toombs county is among thelocations with smaller deviation on its GSR with respect toOSR; a combination of small difference between the stan-
dard deviations of GSR and OSR and high correlation (0.81)are the main reason for that. Conversely, Tift county had thegreater MSD among locations, basically due to higher differ-ence between the standard deviations of GSR and OSR. In most
Fig. 4 – Mean squared deviation and its components for total evagenerated daily solar radiation. SDSD, squared difference betweethe standard deviations; SB, squared bias.
2 1 0 ( 2 0 0 8 ) 312–326
locations, the variability of the YG around the average, repre-sented by the MSV (SDSD + LCS), was higher than the bias (SB);LCS and SDSD were, respectively, the major and minor com-ponents of the total deviation of GSR (Fig. 3). Nevertheless, thecorrelation between GSR and OSR during the cropping seasons
were significantly different from 0 (P < 0.05) within locations,with a Pearson’s coefficient of correlation (r) varying between0.76 for Decatur county and 0.81 for Toombs and Burke coun-ties (Fig. 3).
potranspiration (m) of cotton, maize, and peanut usingn standard deviations; LCS, lack of correlation weighted by
g 2 1
3c
FtcrT
Fgt
e c o l o g i c a l m o d e l l i n
.3. Impact of generated solar radiation on simulatedrop water use
or the three crops, within locations, significant correla-
ion (P < 0.05) was found between ETCG and ETCO for rainfedonditions whereas for irrigated conditions not significant cor-elation was found in four counties for cotton (Mitchell, Terrell,ift, and Toombs) and peanut (Baker, Bulloch, Terrell, and Tift)
ig. 5 – Mean squared deviation and its components for simulateenerated daily solar radiation. SDSD, squared difference betweehe standard deviations; SB, squared bias.
0 ( 2 0 0 8 ) 312–326 319
and two counties (Bulloch and Tifton) for maize. However, forthe three crops and for both rainfed and irrigated conditionswithin locations, means of ETCG did not differ significantlyfrom ETCO (results are not presented).
Based on the MSD approach, ETCG deviations from ETCO ofthe three crops were lower for the rainfed than for the irrigatedconditions. This is because the ability of plants to photosyn-thesize under limiting soil water conditions is reduced and
d yield (kg ha−1) of cotton seed, maize, and peanut usingn standard deviations; LCS, lack of correlation weighted by
i n g
320 e c o l o g i c a l m o d e l l
potential differences on ETCG with respect to ETCO could alsobe masked. Variations on solar radiation lead to variations inthe atmospheric demand and ETC. Several factors, includingsoil water availability, affect crop’s response to variations onatmospheric demand. As observed by Bert et al. (2007), undernon-limiting water conditions, higher radiation values helpto maintain higher photosynthesis rates, resulting in greateraboveground biomass production and yields. For both rain-fed and irrigated conditions, ETCG deviations for cotton andpeanut were more variable than deviations of ETCG for maize.For most of the locations, and for rainfed and irrigated condi-tions, LCS and SDSD were the major components of the totaldeviation of simulated cotton, maize, and peanut evapotran-spiration (Fig. 4). These results suggest that deviations of GSRwith respect to OSR could be important source of potentialerrors on simulated crops’ water use.
3.4. Impact of generated solar radiation on simulatedcrop growth and yield
Within locations, for the three crops and for both rainfed andirrigated conditions, simulated total aboveground biomass atmaturity using GSR did not differ significantly from simulatedtotal aboveground biomass at maturity using OSR. Addition-
S.D.: standard deviation; Min: minimum; Max: maximum. ¶Average yields
2 1 0 ( 2 0 0 8 ) 312–326
ally, correlations with simulated total aboveground biomass atmaturity from GSR and OSR were significantly different from 0(P < 0.05) (results are not presented). These results are consis-tent with those found by Meinke et al. (1995) using differentcombinations of weather data from three different weathergenerators as inputs for maize, sunflower, and wheat cropmodels.
Except for Burke, Terrell, and Toombs counties for cottonunder rainfed conditions, no significant differences (P < 0.05)were found between averages of YO and YG within loca-tions for the three crops. For the three crops under irrigatedconditions, no significant differences were found betweenaverages of YO and YG within locations. Additionally, for bothscenarios minimum and maximum yields as well as the inter-annual variation of YG were closely related to those from YO
(Tables 2–4). For most of the locations, the correlations withYO and YG for cotton, maize, and peanut were significantlydifferent from 0 (P < 0.05). The values for r were as low as 0.97(cotton) and as high as 0.99 (cotton, maize, and peanut) forrainfed conditions and as low as 0.60 (maize) and as high as
0.99 (cotton) for irrigated conditions (Tables 5–7).
Based on the MSD approach, YG deviations from YO of thethree crops were lower for the rainfed than for the irrigatedconditions. Nevertheless, the YG of cotton and peanut had low
) using observed (OSR) and generated (GSR) daily solar
within location are significantly different (P < 0.05).
e c o l o g i c a l m o d e l l i n g 2 1 0 ( 2 0 0 8 ) 312–326 321
Table 4 – Summary statistics for simulated peanut yield (kg ha−1) using observed (OSR) and generated (GSR) daily solarradiation for rainfed and irrigated scenarios
ields
MmCmafessabnpSdttfaFais
y
Pickering, 1994), allowing the plants to use radiation moreefficiently.
The higher MSD for YG of maize with respect to irrigatedcotton and peanut yields could be due to a combination of fac-
S.D.: standard deviation; Min: minimum; Max: maximum. ¶Average y
SD for both rainfed and irrigated conditions while the YG ofaize had lower MSD for rainfed than for irrigated conditions.
ontrary to our findings, Xie et al. (2003), using the ALMANACodel (Kiniry et al., 1992), found that variations in solar radi-
tion lead to higher relative percent change on maize yieldsor rainfed than for irrigated conditions. More recently, Bertt al. (2007) using the CERES-Maize model, found high sen-itivity of the model to changes in radiation with respect tooil variables; however, possible errors in the input variablesround the central values seemed to have been dampenedy the model. As for GSR (Fig. 3), LCS was the major compo-ents of the total deviation of simulated cotton, maize, andeanut yields. The other components of the deviation, i.e.,DSD and SB, were negligible (Fig. 5), suggesting that the stan-ard deviations, constituents of LCS (Eq. (6)), of the GSR duringhe cropping season could be an important source of poten-ial errors for the simulated yields. However, yield deviationsrom using GSR seemed to have had low impact on the over-ll simulated yield for cotton, maize, and peanut. Similarly,odor and Kovacs (2005), using the 4 M crop model (Fodor etl., 2003), a maize model based on CERES-Maize, found a small
mpact of the error associated with observed solar radiation onimulated maize yield.
The small impact of GSR on simulated cotton and peanutields, both C3 plants, might be due to their lower light satu-
within location are significantly different (P < 0.05).
ration than maize, a C4 plant (Fig. 6), which limits the increaseon their use of solar radiation. Since the CSM-CROPGRO mod-els use the hourly hedgerow light interception and leaf-levelphotosynthesis approach, photosynthesis is computed forboth sunlit and shaded leaves (Boote et al., 1998; Boote and
Fig. 6 – Differences in C3 and C4 photosynthesis.
322 e c o l o g i c a l m o d e l l i n g 2 1 0 ( 2 0 0 8 ) 312–326
Table 5 – Relationship between simulated cotton seed yield using generated and observed solar radiation and thecomponents of the mean squared deviation of generated daily solar radiation
P < 0.ation
Pr > |t| = parameters are significantly different from 0 for the t-test atof correlation with YG and YO or YG and MSDGSR components. *Correl
tors, such as its high light saturation (Fig. 6) and its improvedwater use efficiency compared to C3 plants. On the other hand,the small MSD of YG for maize under rainfed conditions could
be masked due to the concept of RUE, a crop-specific coeffi-cient used for biomass accumulation of the CSM-CERES model.RUE is robust under optimum conditions of growth and devel-opment (Lizaso et al., 2005), but highly affected by water stress
05; ˇs are the standardized coefficients; r is the Pearson’s coefficientsignificantly different from 0 at P < 0.05; blank cells: no significance.
(Ritchie et al., 1998; Earl and Davis, 2003), limiting evapotran-spiration and consequently, total dry matter accumulation(Boote et al., 1998).
The ˇ coefficients of the multiple regression analysisallowed for determining the relative importance of YO andthe components of the MSDGSR, explanatory variables, on YG.For rainfed conditions and for the three crops, YO had the
e c o l o g i c a l m o d e l l i n g 2 1 0 ( 2 0 0 8 ) 312–326 323
Table 6 – Relationship between simulated maize yield using generated and observed solar radiation and the componentsof the mean squared deviation of generated daily solar radiation
P < 0.ation
lStwfrh
Pr > |t| = parameters are significantly different from 0 for the t-test atof correlation with YG and YO or YG and MSDGSR components. *Correl
argest ˇ coefficient within locations, followed by LCS, SB andDSD. These results are consistent with the strong correla-ions found between YG and YO for cotton, maize, and peanut
ithin locations. For the components of the MSDGSR, only LCS
or maize in Decatur county and for peanut in Mitchell and Ter-ell counties and SB for maize in Bulloch and Sumter counties,ad correlations with YG that were significantly different from
05; ˇs are the standardized coefficients; r is the Pearson’s coefficientsignificantly different from 0 at P < 0.05; blank cells: no significance.
0 (P < 0.05). For irrigated conditions and for the three crops,except for peanut in Baker, Sumter, and Toombs counties, YO
had the largest ˇ coefficients within locations, followed by SB,
LCS, and SDSD. Except for peanut in Sumter county, correla-tions between YG and YO of cotton, maize, and peanut, as wellas between YG and LCS (for cotton in Terrell county) and SB(for maize in Bulloch and Mitchell counties and for peanut in
324 e c o l o g i c a l m o d e l l i n g 2 1 0 ( 2 0 0 8 ) 312–326
Table 7 – Relationship between simulated peanut yield using generated and observed solar radiation and thecomponents of the mean squared deviation of generated daily solar radiation
P < 0.ation
Pr > |t| = parameters are significantly different from 0 for the t-test atof correlation with YG and YO or YG and MSDGSR components. *Correl
Baker and Sumter counties) were significantly different from0 (P < 0.05) (Tables 5–7).
The low effect of LCS on Y , in spite of having the largest
G
ˇ coefficient for 56% of the locations for rainfed cotton andmaize, can be explained through its constituents; the standarddeviations and correlation (Eq. (6)). The standard deviations forGSR and OSR had a similar tendency and high variation within
05; ˇs are the standardized coefficients; r is the Pearson’s coefficientsignificantly different from 0 at P < 0.05; blank cells: no significance.
locations, but their daily averages during the cropping seasonwere strongly correlated (Fig. 3). Although SB had the largestˇ coefficient within 67% of the locations for peanut, its impact
on YG was negligible; the statistical comparisons showed thatcorrelations with YG and SB were not significantly differentfrom 0 (P < 0.05) (Table 7), meaning that the small differencesbetween the averages of GSR and OSR (Fig. 2), constituents
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B
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f SB (Eq. (2)), did not affect the average of simulated yieldsTables 2–4).
. Conclusions
he main objective of this study was to determine the impactf using generated daily solar radiation (GSR) on simulatedrowth and yield of cotton, maize, and peanut. Specific objec-ives were to determine the extent of the components ofhe total deviation (squared bias, squared difference betweentandard deviations, and lack of correlation weighted by thetandard deviations) from generated and observed solar radi-tion on simulated water use and yield of cotton, maize, andeanut. Within locations, the use of generated daily solar radi-tion did not significantly affected simulated cotton, maize,nd peanut total water use, aboveground biomass, and yieldor both rainfed and irrigated conditions.
For the three crops, the MSD of simulated total water usend yield using GSR was lower for the rainfed than for therrigated conditions. Yield simulated with the CSM-CROPGRO-otton and -Peanut models had lower deviations than yieldimulated with the CSM-CERES-Maize model. Thus, potentialystematic errors on daily solar radiation seemed to affectess the CSM-CROPGRO than the CSM-CERES family of mod-ls. In most of the locations, LCS was the major componentf the total deviation affecting the outputs of the models; thether components, SDSD and SB, were negligible. As the lackf correlation (LCS) is weighted by the standard deviation ofenerated and observed data, the extent of their differenceould be an important source of potential error for modelpplications. Nevertheless, none of the MSD components ofhe GSR, including MSV, showed significant correlation withimulated yields for most locations. Thus, the overall perfor-ance of the models was not affected. It can be concluded
ased on the results of this study that GSR can be used asn input for crop model simulation models when OSR is notvailable.
cknowledgements
his work was conducted under the auspices of the South-ast Climate Consortium (SECC; secc.coaps.fsu.edu) andupported by a partnership with the United States Depart-ent of Agriculture-Risk Management Agency (USDA-RMA),
y grants from the US National Oceanic and Atmosphericdministration-Office of Global Programs (NOAA-OGP) andSDA Cooperative State Research, Education and Extensionervices (USDA-CSREES) and by State and Federal funds allo-ated to Georgia Agricultural Experiment Stations Georgiagricultural Experiment Stations Hatch project GEO01654.
e f e r e n c e s
ngstrom, A., 1924. Solar and terrestrial radiation. Q. J. R.Meteorol. Soc. 50, 121–125.
all, R.A., Purcell, L.C., Carey, S.K., 2004. Evaluation of solarradiation prediction models in North America. Agron. J. 96,391–397.
0 ( 2 0 0 8 ) 312–326 325
Bert, F.E., Laciana, C.E., Podesta, G.P., Satorre, E.H., Menendez,A.N., 2007. Sensitivity of CERES-Maize simulated yields touncertainty in soil properties and daily solar radiation. Agric.Syst. 94 (141), 150.
Boote, K.J., Loomis, R.S., 1991. The prediction of canopyphotosynthesis. In: Boote, K.J., Loomis, R.S. (Eds.), ModellingPhotosynthesis—From Biochemistry to Canopy. CSSA SpecialPublication Number 19. Crop Science Society of America,Inc./American Society of Agronomy, Inc, Madison, Wisconsin,pp. 109–140.
Bristow, K.L., Campbell, G.S., 1984. On the relationship betweenincoming solar radiation and daily maximum and minimumtemperature. Agric. For. Meteorol. 31, 159–166.
Cooter, E.J., Dhakhwa, G.B., 1995. A solar radiation model for usein biological applications in the south and southeastern USA.Agric. For. Meteorol. 78, 31–51.
Cooter, E.J., 1990. The impact of climate change on continuouscorn production in the southern USA. Climatic Change 16,53–82.
Donatelli, M., Bellocchi, G., Fontana, F., 2003. RadEst 3.0: softwareto estimate daily radiation data from commonly availablemeteorological variables. Eur. J. Agron. 18, 363–367.
Dubrovsky, M., Zalud, Z., Stastna, M., 2000. Sensitivity ofCERES-Maize yields to statistical structure of daily weatherseries. Climatic Change 46, 447–472.
Earl, H.J., Davis, R.F., 2003. Effect of drought stress on leaf andwhole canopy radiation use efficiency and yield of maize.Agron. J. 95, 688–696.
Elizondo, D., Hoogenboom, G., McClendon, R.W., 1994.Development of a neural network model to predict daily solarradiation. Agric. For. Meteorol. 71, 115–132.
Fodor, N., Mathene-Gaspar, G., Pokovai, K., Kovacs, G.J., 2003.4M-software package for modelling cropping systems. Eur. J.Agron. 18, 389–393.
Fodor, N., Kovacs, G.J., 2005. Sensitivity of crop models to theinaccuracy of meteorological observations. Phys. Chem. Earth30, 53–57.
Garcia y Garcia, A., Hoogenboom, G., 2005. Evaluation of animproved daily solar radiation generator for the southeasternUSA. Climate Res. 29, 91–102.
Goodin, D.G., Hutchinson, J.M.S., Vanderlip, R.L., Knapp, M.C.,1999. Estimating solar irradiance fro crop modeling usingdaily air temperature data. Agron. J. 91, 845–851.
Grant, R.H., Hollinger, S.E., Hubbard, K.G., Hoogenboom, G.,Vanderlip, R.L., 2004. Ability to predict solar radiation valuesfrom interpolated climate records for use in crop simulationmodels. Agric. For. Meteorol. 127, 65–75.
Hansen, J.W., 1999. Stochastic daily solar irradiance for biologicalmodeling applications. Agric. For. Meteorol. 94 (53), 63.
Hartkamp, A.D., White, J.W., Hoogenboom, G., 2003.Comparisonof three weather generators for crop modeling: a case studyfor subtropical environments. Agric. Syst. 76, 539–560.
Heinemann, A.B., Hoogenboom, G., Chojnicki, B., 2002. Theimpact of potential errors in rainfall observation on thesimulation of crop growth, development and yield. Ecol.Model. 157, 1–21.
Hodges, T., Botner, D., Sakamoto, C., Hays Haug, J., 1987. Usingthe CERES-Maize model to estimate production for the U.S.corn belt. Agric. For. Meteorol. 40, 293–303.
i n g
326 e c o l o g i c a l m o d e l l
Hoogenboom, G., Tsuji, G.Y., Pickering, N.B., Curry, R.B., Jones,J.W., Singh, U., Godwin, D.C., 1995. Decision support system tostudy climate change impacts on crop production. Agron. J.59, 51–75.
Hoogenboom, G., 2000. Contribution of agrometeorology to thesimulation of crop production and its applications. Agric. For.Meteorol. 103 (137), 157.
Hoogenboom, G., Jones, J.W., Wilkens, P.W., Porter, C.H., Batchelor,W.D., Hunt, L.A., Boote, K.J., Singh, U., Uryasev, O., Bowen,W.T., Gijsman, A.J., Du Toit, A.S., White, J.W., Tsuji, G.Y., 2004.Decision Support System for Agrotechnology Transfer Version4.0 [CD-ROM]. University of Hawaii, Honolulu, HI.
Hubbard, K.G., 1994. Spatial variability of daily weather variablesin the High Plains of the USA. Agric. For. Meteorol. 68 (29), 41.
Hunt, L.A., White, J.W., Hoogenboom, G., 2001. Agronomic data:advances in documentation and protocols for exchange anduse. Agric. Syst. 70, 477–492.
Hunt, L.A., Kuchar, L., Swanton, C.J., 1998. Estimation of solarradiation for use in crop modelling. Agric. For. Meteorol. 91(293), 300.
International Benchmark Sites Network for AgrotechnologyTransfer Project, 1990. Technical Report 2. Field & LaboratoryMethods for the Collection of the IBSNAT Minimum Data Setfor the Decision Support System for Agrotechnology Transfer(DSSAT V2.1). Department of Agronomy and Soil Sci., Collegeof Trop. Agric. and Human Resources, University of Hawaii,Honolulu, HI.
Kiniry, J.R., Williams, J.R., Gassman, P.W., Debaeke, P., 1992. Ageneral, process-oriented model for two competing plantspecies. Trans. ASAE 35, 801–810.
Kobayashi, K., Salam, M.S., 2000. Comparing simulated and
measured values using mean squared deviation and itscomponents. Agron. J. 92, 345–352.
Lizaso, J.I., Batchelor, W.D., Boote, K.J., Westgate, M.E., 2005.Development of a leaf-level canopy assimilation model forCERES-Maize. Agron. J. 97, 722–733.
2 1 0 ( 2 0 0 8 ) 312–326
Meinke, H., Carberry, P.S., McCaskill, M.R., Hills, M.A.,McLeod, I., 1995. Evaluation of radiation and temperaturedata generators in the Australian tropics and sub-tropicsusing crop simulation models. Agric. For. Meteorol. 72,295–316.
NRCS/USDA, 2003. Soil Survey Staff 2003. National Soil SurveyCharacterization Data, Soil Survey Laboratory, National SoilSurvey Center USDA-NRCS, Lincoln, NE.
Perkins, H.F., 1987. Characterization data for selected GeorgiaSoils. Special Publication 43. The Georgia AgriculturalExperiment Stations, College of Agriculture, Athens, GA.
Priestley, C.H.B., Taylor, R.J., 1972. On the assessment of surfaceheat and evaporation using large scale parameters. Mon.Weather Rev. 100, 81–92.
Richardson, C.W., 1981. Stochastic simulation of dailyprecipitation, temperature, and solar radiation. WaterResources Res. 17, 182–190.
Rivington, M., Bellocchi, G., Matthews, K.B., Buchan, K., 2005.Evaluation of three model estimations of solar radiation at 24UK stations. Agric. For. Meteorol. 132, 228–243.
Rivington, M., Matthews, K.B., Bellocchi, G., Buchan, K., 2006.Evaluating uncertainty introduced to process-basedsimulation model estimates by alternative sources ofmeteorological data. Agric. Syst. 88, 451–471.
SAS Institute, 1999. SAS Online Doc. Version 8. SAS Institute,Cary, NC.
Smith, J.B., Tirpack, D.A. (Eds.), 1990. The Potential Effect of GlobalClimate Change on the United States. Hemisphere, New York.
Soltani, A., Latifi, N., Nasiri, M., 2000. Evaluation of WGEN forgenerating long term weather data for crop simulations.
Agric. For. Meteorol. 102, 1–12.
Weiss, A., Hays, C.J., 2004. Simulation of daily solar irradiance.Agric. For. Meteorol. 123, 187–199.
Xie, Y., Kiniry, J.R., Williams, J.R., 2003. The ALMANAC’s modelsensitivity to input variables. Agric. Syst. 78, 1–16.
