Agroclimatic Mapping of Maize Crop Based on Soil Physical Properties

Agroclimatic Mapping of Maize Crop Based on Soil Physical Properties Durval Dourado Neto1 , Gerd Sparovek2, Klaus Reichardt3 Luiz Carlos Timm4 and Don...
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Agroclimatic Mapping of Maize Crop Based on Soil Physical Properties Durval Dourado Neto1 , Gerd Sparovek2, Klaus Reichardt3 Luiz Carlos Timm4 and Donald R. Nielsen5 1

Crop Science Department, ESALQ, University of São Paulo, Piracicaba, Brazil Soil Science Department, ESALQ, University of São Paulo, Piracicaba, Brazil 3 Department of Exact Sciences, ESALQ, University of São Paulo, Piracicaba, Brazil 4 CENA, University of São Paulo, Piracicaba, Brazil 5 Department of Land, Air and Water Resources, University of California, Davis, USA 2

Lecture given at the College on Soil Physics Trieste, 3 – 21 March 2003

LNS0418014

1

2 3 4 5

[email protected] [email protected] [email protected] [email protected] [email protected]

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INTRODUCTION With the purpose of estimating water deficit to forecast yield knowing productivity (potential yield), the water balance is useful tool to recommend maize exploration and to define the sowing date. The computation can be done for each region with the objective of mapping maize grain yield based on agroclimatic data and soil physical properties. AGRICULTURE: THE PROPOSED MODEL TO ESTIMATE YIELD Based on agroclimatic data, air temperature and solar radiation, a model was built to estimate the corn grain productivity (the energy conversion results in dry mass production). The proposed model is presented in the Figure 2. Conversion of CO2 in CH2O The carbon dioxide (CO2) fixation by plants is related to gross carbohydrate (CH2O) production and solar radiation, according with the following equation: CO2 + H2O + solar energy → CH2O + O2

(1)

The CO2 assimilation by C4 plants depends on the photosynthetic active radiation (PAR) and temperature (T) Figure 1. According to the experimental data (Heemst, 1986): a + b.q + c.q 2 + d .q 3 + e. ln(T ) Adc = (2) 2 1 + f .q + g .q 2 + h. ln (T ) + i.[ln(T )] where Adc is the carbon dioxide assimilation (µL.cm-2.h-1), q the photosynthetic active radiation (PAR, cal.cm-2.min-1), T the air temperature (ºC), and a, b, c, d, e, f, g, h and i are the empirical parameters obtained by multiple regression analysis (a = 1.732748682; b = 61.81088751; c = -254.72111; d = 333.7473141; e = 0.54180211; f = -0.19106242; g = 0.29248608; h = -0.5521966; i = 0.080139046). Knowing the specific mass of CO2 (44g.mol-1) and CH2O (30g.mol-1), the carbon dioxide assimilation (Adc, µL.cm-2.h-1) is converted to carbohydrate mass produced (MPCH2O, g.h-1.cm-2 of leaf) as function of leaf area index (IAF), climatic data, air temperature (T, °C) and PAR (q, cal.cm-2.min-1). For the whole crop cycle (C, days), knowing the degrees-day to flowering (GDf, °C.day), the reproductive phase duration (DFR, days), theoretical photoperiod (H, h.day-1) and mean leaf area index (IAFm), the total carbohydrate production (MCH2O, kg.ha-1.cycle-1) can be estimated using the following equation: M CH 2O =

36,585.P. Adc.IAFm .C.H T + 273

(3)

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D.D. Neto, G. Sparovek, K. Reichardt, L.C. Timm & D.R. Nielsen

where P is the local atmospheric pressure (atm). Solar radiation and theoretical photoperiod values are given in Tables 1 and 3, respectively. Grain productivity, maintenance and growth respiration and solar radiation To convert the final gross carbohydrate mass (MCH2O) in dry mass of different corn organs (grain, stem, root and seeds), to estimate grain yield (Pgr, kg.ha-1), is necessary some corrections. The first correction refers to carbohydrate mass consumption by respiration process (FAO, 1979): CRMC = 0.6 (T < 20ºC)

(4)

CRMC = 0.5 (T ≥ 20ºC)

(5)

where CRMC is the maintenance and growth respiration coefficient and T the air temperature (ºC). The second correction refers to intercepted solar radiation as function of maximum leaf area index (IAFmax):

CRs =

1 − e −0, 75 IAFmax 2

(6)

where CRS is the solar radiation extinction coefficient.

Figure 1. CO2 assimilation by C4 plants as function of PAR and air temperature (Heemst, 1986).

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The dry mass of maize grain is a fraction of the total dry mass harvested. According to experimental data (Dourado-Neto, 1999) for maize (grain), the harvest index is around 40% (IC = 0.4). Then, the final grain productivity (Pgr ) can be estimated as follows: Pgr = MCH2O.CRmc.CRs.IC

(7)

Table 1. Solar radiation at horizontal surface in the atmosphere (15th day of each month). Lat S

Jan

Feb

Mar

Apr

0o 2o 4o 6o 8o 10o 12o 14o 16o 18o 20o 22o 24o 26o 28o 30o 32o 34o 36o 38o 40o

35.59 36.05 36.80 37.56 38.06 38.06 39.27 39.77 40.03 40.53 40.99 41.49 41.49 41.74 41.99 41.99 42.25 42.25 42.25 42.25 41.99

36.80 37.05 37.56 37.81 38.06 38.52 38.52 38.77 39.02 39.02 39.02 39.02 39.02 38.77 38.52 38.52 38.06 37.81 37.56 37.05 36.80

37.05 37.05 37.05 37.05 36.80 36.55 36.30 36.05 35.84 35.59 35.09 34.58 34.08 33.58 33.12 32.62 32.20 31.11 30.65 29.89 28.89

35.84 35.59 35.09 34.58 34.08 33.58 33.12 32.36 31.61 31.11 30.15 29.39 28.64 27.68 26.92 25.96 24.95 24.20 23.24 22.23 21.23

May Jun Jul Aug Solar radiation (MJ m-2 dia-1) 33.83 33.12 32.62 31.61 30.90 30.15 29.18 28.43 27.68 26.67 25.71 24.70 23.70 22.73 21.73 20.77 19.76 18.76 17.54 16.29

32.87 32.11 31.36 30.65 29.64 28.64 27.68 26.67 25.71 24.70 23.70 22.73 21.73 20.77 19.26 18.30 17.04 15.83 14.82 13.82 15.07 12.35

33.37 32.62 31.61 31.11 30.15 29.39 28.64 27.68 26.67 25.96 24.95 23.95 22.99 21.73 20.77 19.76 18.76 17.52 16.29 15.32 14.07

34.83 34.33 33.83 33.37 32.62 32.11 31.61 30.90 30.15 29.39 28.64 27.68 26.92 26.17 52.21 24.20 23.24 22.23 21.23 20.26 19.26

Sep

Oct

Nov

Dec

36.30 36.30 36.05 36.05 35.84 35.59 35.09 35.09 34.58 34.08 33.58 33.12 32.62 31.86 31.11 30.65 29.90 29.14 28.18 27.17 26.42

36.55 36.80 37.05 37.56 37.56 37.56 37.56 37.81 37.81 37.81 37.56 37.56 37.56 37.30 37.05 36.55 36.30 36.05 35.59 35.09 34.58

35.84 36.55 37.05 37.56 38.06 38.31 38.77 39.27 39.52 40.03 40.28 40.53 40.53 40.78 40.99 40.99 40.99 40.99 40.99 40.78 40.53

34.83 35.59 36.55 37.26 37.81 38.52 39.02 39.52 40.03 40.78 41.24 41.49 41.99 42.50 42.75 43.00 43.25 43.46 43.46 43.71 43.71

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D.D. Neto, G. Sparovek, K. Reichardt, L.C. Timm & D.R. Nielsen

Figure 2. Schematic representation of the model

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AGROMETEOROLOGY AND SOIL PHYSICS Water balance The water balance can be done with the following variables (Thornthwaite & Mather, 1955; Dourado-Neto, 1999): crop coefficient (Kc) and root effective depth (Ze) for any weather data distribution. Potential and maximum evapotranspiration The potential evapotranspiration (ET0, mm.period-1)) by the Thornthwaite method (Dourado-Neto, 1999) can be estimated as follows:  H  T  ET0 = 0.53 N i  i 10 i   12  I  a = a0 + a1 I + a 2 I 2 + a3 I 3

a

(8) (9)

where Ti is the air temperature (ºC), I the termic index, a the empirical coefficient, Ni the number of days per period and Hi the theoretical photoperiod in the median day of the period i, and a the empirical coefficient (a0 = 0.49239, a1 = 0.01792, a2 = 0.0000771 and a3 = 0.000000675). The thermic index (I) and the theoretical photoperiod (H) can be calculated as follows: 12

∑T

I = 0.08745

1.514 j

(10)

j =1

H =

24

π

cos −1 [− tg (α )tg (φ )]

(11)

3

α = C 0 + ∑ [C i sin (2iπd / 365) − Di cos(2iπd / 365)]

(12)

i =1

where Tj is the air temperature (ºC) of the month j, α the solar declination (rad) in the median day of the period, d the Julian day (1 ≤ d ≤ 365), C0, Ci and Di are the empirical parameters (C0 = 0.006918, C1 = 0.070257, C2 = 0.000907, C3 = 0.00148, D1 = 0.399912, D2 = 0.006758 and D3 = 0.002697) and φ the latitude (rad). The maximum evapotranspiration (ETmi) corresponds the maximum crop yield:

ETmi = ET0i .K ci where Kci is the crop coefficient (Table 2 and Figure 3).

(13)

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Table 2. Maize crop coefficient (Kc) for Brazilian weather condition. Phenological stage1 up to Kc2 I 10% of vegetative phase 0.20 to 0.40 II 80% of vegetative phase Figure 3 III Flowering 0.95 to 1.20 IV physiological maturity point Figure 3 V harvest 0.3 to 0.5 1 Food and Agricultural Organization (1979) 2 Low values of Kc for relative humidity larger than 70% and wind speed lower than 5 m.s-1

t1

Kc 1

t3

t2

Kc3

t4

t5

Kc5

Kc i = Kc 1 (0 ≤ i < t1 )

Kci =

Kc3 − Kc1 . (i − t1 ) + Kc1 (t1 ≤ i < t 2 ) t 2 − t1

Kc i = Kc

3

Kc3

Kc

Kci = Kc1

Kc5 t1

t2

t3

t4

(t2 ≤ i < t3 ) Kc5 − Kc3 . (i − t3 ) + Kc3 (t 3 ≤ i < t 4 ) t4 − t3 Kc i = Kc 5 (t 4 ≤ i ≤ t 5 )

t5

time

Figure 3. Temporal variation (t1, t2, t3, t4, and t5: phenological stages duration) of crop coefficient (Kc1, Kc3 and Kc5) (FAO, 1979).

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Table 3. Theoretical photoperiod (H, hour.day-1) for different latitudes corresponding to 15th day of each month. Lat S 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40

Jan

Feb

Mar

Apr

12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 13.0 13.1 13.2 13.4 13.5 13.6 13.7 13.9 14.0 14.2 14.3 14.5 14.7

12.1 12.2 12.2 12.3 12.4 12.4 12.5 12.6 12.7 12.7 12.8 12.8 12.9 12.9 13.0 13.1 13.2 13.3 13.4 13.5 13.6

12.1 12.1 12.1 12.1 12.1 12.1 12.2 12.2 12.2 12.2 12.2 12.2 12.3 12.3 12.3 12.3 12.3 12.3 12.4 12.4 12.4

12.1 12.1 12.0 12.0 11.9 11.9 11.8 11.8 11.7 11.7 11.6 11.6 11.5 11.5 11.4 11.4 11.3 11.3 11.2 11.1 11.1

May Jun Jul Aug Theoretical photoperiod 12.1 12.1 12.1 12.1 12.0 12.0 12.0 12.0 11.9 11.8 11.9 12.0 11.9 11.7 11.8 11.9 11.7 11.6 11.7 11.9 11.7 11.5 11.6 11.8 11.6 11.4 11.5 11.7 11.5 11.3 11.4 11.6 11.4 11.2 11.2 11.6 11.3 11.1 11.1 11.5 11.2 10.9 11.0 11.4 11.1 10.8 10.9 11.3 10.9 10.7 10.8 11.2 10.8 10.5 10.7 11.2 10.7 10.4 10.6 11.1 10.6 10.2 10.4 11.0 10.5 10.0 10.3 10.9 10.3 9.8 10.1 10.9 10.2 9.7 10.1 10.7 10.1 9.5 9.8 10.6 9.9 9.3 9.6 10.5

Sep

Oct

Nov

Dec

12.1 12.1 12.1 12.1 12.1 12.0 12.0 12.0 12.0 12.0 12.0 12.0 11.9 11.9 11.9 11.9 11.9 11.9 11.9 11.8 11.8

12.1 12.1 12.2 12.2 12.3 12.3 12.4 12.4 12.4 12.5 12.5 12.6 12.6 12.7 12.8 12.8 12.9 12.9 13.0 13.1 13.1

12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 12.9 13.0 13.2 13.2 13.3 13.4 13.5 13.6 13.7 13.9 14.0 14.2 14.3

12.1 12.2 12.4 12.5 12.6 12.7 12.8 12.9 13.1 13.2 13.3 13.5 13.6 13.8 13.9 14.1 14.2 14.4 14.6 14.8 15.0

Water deficit and grain yield To estimate water deficit, the rainfall (Ci, mm) and the maximum crop evapotranspiration (ETmi, mm) must be computed: S i = C i − ETmi

If Si < 0: Li = Li −1 + S i  Armi If Si ≥ 0: Li = −CAD. ln  CADi

(15)   

(17)

(14)

Armi = CADi .e

 Li   CAD i 

Armi = Armi −1 + S i

   

(16) (18)

where Si and Li are auxiliary variables (mm), CADi the soil water holding capacity (mm), and Armi the soil water holding available (mm) in the period i. Thornthwaite & Mather (1955) suggested the following criterion to begin the water balance: L = 0 and Arm = CAD in the last period of wet season.

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D.D. Neto, G. Sparovek, K. Reichardt, L.C. Timm & D.R. Nielsen

The soil water holding capacity (CAD, mm), the soil water holding available (Arm, mm), the real evapotranspiration (ETr, mm) and water deficit (WDi, mm) are calculated as follows: CADi = 10.(θcc – θpmp).Zei

(19)

Armi = 10.(θi – θpmp).Zei

(20)

ETri = ETmi (Ci ≥ ETmi)

ETri = Ci + Vai (Ci < ETmi)

(21)

Vai = Armi – Armi-1 WDi = 0 (Ci ≥ ETmi)

WDi = ETmi - ETri (Ci < ETmi)

(24)

(22) (23) (25)

where Zei is the effective root depth (cm) in the period i, θcc and θpmp are soil water content corresponding to field capacity and wilting point (cm3.cm-3), θi the actual soil water content, Ci the rainfall, and Vai the soil water holding variation in the period i. The corn grain yield (Rgr) is calculated as function of grain productivity (Pgr) and depletion factor (fd): n

∏ (ETr ) i

fd =

Kci

i =1 n

∏ (ETmi )Kc

(26) i

i =1

Rgr = fd.Pgr

(27)

AGROCLIMATIC MAPPING OF MAIZE CROP BASED ON SOIL PHYSICAL PROPERTIES From agroclimatic data and soil physical properties, a map with region identification can be built for solar radiation (Figure 4), air temperature, rainfall, maize grain productivity and yield, potential and real evapotranspiration and water deficit. The map allows to identify the agroclimatic and the soil physical restrictions. This procedure can be used in different spatial (farm to State) and temporal (daily to monthly data) scales. The statistical analysis allows to compare estimated and observed values in different situations to validate the model and to verify which scale is more appropriate. A software was developed (Visual BASIC for Microsoft Windows environment) to forecast corn productivity.

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Figure 4. Annual solar radiation in Brazil.

REFERENCES DOORENBOS, J.; KASSAM, A.H. Efeito da água no rendimento das culturas. Campina Grande: UFPB, 1994. 306p. DOURADO-NETO, D. Modelos fitotécnicos referentes à cultura do milho. Piracicaba, 1999. 229p.: il. Tese (Livre-Docência) – Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo. FAO. Irrigation and drainage paper, Roma, n.33, 1979. 193p. GOUDIAAN, J.; LAAR, H.H. van. Modelling potential crop growth processes: the textbook with exercises. Dordrecht: Kluwer, 1994. 239p. HEEMST, H.D.J. van. Physiological principles. In: KEULEN, H. van.; WOLF, J. Modeling of agricultural production: Weather, soils and crops. Wageningen: Pudoc, 1986. p.13-26.

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HOOGENBOOM, G. Contribution of Agrometeorology to the simulation of crop production and its applications. Agricultural and Forest Meteorology, n.103, p.137-157, 2000. KEULEN, H. van.; PENNING DE VRIES, F.W.T.; DRESS, E.M. A summary model for crop growth. In: PENNING DE VRIES, F.W.T.; VAN LAAR, H. H. (Ed). Simulation of plant growth and crop production. Wageningen: Pudoc, 1982. p.8797. KEULEN, H. van.; WOLF, J. Modeling of agricultural production: Weather, soils and crops. Wageningen: Pudoc, 1986. 463p. LIMA, M.G. Calibração e validação do modelo ceres-maize em condições tropicais do Brasil. Piracicaba, 1995. 119p. Tese (Doutorado) – Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo. MUCHOV, R.C.; HAMMER, G.L.; CARBERRY, P.S. Optimizing crop and cultivar selection in response to climatic risk. In: MUCHOV, R.C.; BELLAMY, J.A. (Ed.) Climatic risk in crop production models and management for the semiarid Tropics and Subtropics. Wallingford: CAB International, 1991. p.235-262. PANDOLFO, C. Parâmetros básicos para uso na modelagem do rendimento de matéria seca em alfafa (Medicago sativa L.). Porto Alegre, 1995. 128p. Dissertação (Mestrado) – Faculdade de Agronomia, Universidade do Rio Grande do Sul. RAMALHO FILHO, A.; BEEK, K.J. Sistema de avaliação da aptidão agrícola das terras. Rio de Janeiro: EMBRAPA-CNPS, 1995. 65p. REYNOLDS, J.F. Some misconceptions of mathematical modeling. What’s New Plant Physiology, v.10, n.11, p.41-44, 1979. ROSENBERG, N.J.; BLAD, B.L.; VERMA, S.B. Microclimate: The biological environment. New York: John Wiley and Sons, 1983. 495p. SALISBURY, F.B. Plant Physiology. Belmont: Wadsworth, 1992. 682p. SPITTERS, C.J.T.; TOUSSAINT, H.A.J.; GOUDRIAAN, J. Separating the diffuse and direct component of global radiation and its implications for modelling canopy photosynthesis. I: Components of incoming solar radiation. Agricultural and Forest Meteorology, n.38, p.217-229, 1986a. SPITTERS, C.J.T.; TOUSSAINT, H.A.J.; GOUDRIAAN, J. Separating the diffuse and direct component of global radiation and its implications for modelling canopy photosynthesis. II: Calculation of canopy photosynthesis. Agricultural and Forest Meteorology, n.38, p.231-242, 1986b. THORNLEY, J.H.M. Mathematical models in plant physiology: a quantitative approach to problems in plant crop physiology. London: Academic Press, 1976. 318p. THORNTHWAITE, C.W.; MATHER, J.R. The water balance. Drexel Institute of Technology, v.8, n.1, p.1-14, 1955. VANCLOOSTER, M.; VIAENE, J.; DIELS, J.; CHRISTIAENS, K. Wave: a mathematical model for simulating water and agrochemical in the soil and vadose environment. Leuven: Katholieke Universiteit Leuven Press, 1994. 1v.

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