**5. Marginal response in bovines**

The weight gain in growing bovines in pasture in the dry season is curvilinear as a function of supplement supply, based on corn and soybean meal, in which the supplement conversion (kg of supplement/kg of accretion in weight gain) becomes worse with increase in the supplementation (Lana et al., 2005; Keane et al., 2006; Lana, 2007b) (Figure 6).

The milk production by supplemented cows in pasture or in feedlot is also curvilinear as a function of increase in the concentrate supply, based on corn and soybean meal (Figure 7A), in which the marginal increase in milk production per kg of concentrate decreases with increase in the amount of concentrate (Bargo et al., 2003; Pimentel et al., 2006a; Sairanen et al., 2006; Lana et al., 2007a,b), as shown in Figure 7B, and in some studies the milk response to concentrate was satisfactory only up to 2-4 kg of concentrate/animal/day (Fulkerson et al., 2006).

Rationality in the Use of Non Renewable Natural Resources in Agriculture 277

0.1 2.1 2.0 2.9

0.2 0.9 1.3 3.0

0.6 2.0 2.9

2.3 3.3 4.0

1.3 1 Considering US\$0.966/kg of N, US\$1.208/kg of P2O5 and US\$0.36/kg of bean, it is necessary 2.68 and 3.36 kg of bean to pay 1 kg of N or P2O5. Efficiency worse than 2.68 or 3.36:1 for N or P2O5 is not economically desirable. These calculations can be used to choose the level of fertilization. 2 Level of

Table 4. Efficiency of use of fertilizers (kg of bean/kg of fertilizer) calculated with base in the

Fig. 6. Body weight gain (BWG) of growing bovines in pasture during the dry season, as a function of daily intake of supplement with 24% CP, in which the values among parenthesis represent the differential in kilograms of supplement given daily divided by the differential

in weight gain, in relation to the previous treatment (Lana, 2005; Lana et al., 2005)

Efficiency of use of fertilizers (kg of grains/kg of fertilizer) 1 50 2 100 150 200 250 300

> 0.0 0.4 0.4 0.6

> 0.0 0.2 0.3 0.7

> 0.1 0.4 0.6

> 1.1 1.2 2.1

0.0 0.3 0.2 0.4

0.0 0.1 0.2 0.5

0.1 0.2 0.4

0.8 0.9 1.7

0.0 0.8 1.6 2.4 3.2 Supplement (kg/animal/day)

0.0 0.2 0.2 0.3

0.0 0.1 0.1 0.3

0.0 0.2 0.3

0.6 0.6

0.0 0.8 0.8 1.1

0.1 0.4 0.5 1.3

0.2 0.7 1.1

1.5 1.9 2.9

> 0.0 0.2 0.4 0.6 0.8 1.0

ADG (kg/animal/day) .

Fertilizer (kg/ha)

N

N

P2O5

P2O5

equations of Table 3

0.0 0.2 0.4 0.6 0.8 1.0

(1.5:1)

ADG (kg/animal/day) .

Second factor

N (kg/ha) 0 30 60

N (kg/ha) 0 50 120

1.1 10.9 11.5 14.2

> 0.6 2.8 4.5 7.9

5.4 12.2 15.3

> 4.0 6.9 6.0

fertilizer (kg/ha) - N in the first two cases or P2O5 in the last two cases.

(40:1) (7.0:1)

0.0 0.8 1.6 2.4 3.2 Supplement (kg/animal/day)

The curvilinear response can also be verified with specific nutrients, such as the observed positive curvilinear response in milk production and negative curvilinear response in the efficiency of use of nitrogen by increasing the dietary crude protein content from 11 to 19% in cows with mean production of 38 kg of milk/day (Baik et al., 2006). In the third experiment of Figure 7, in addition to decreasing response in milk production, there was decreasing response in body weight variation with increase in the concentrate level (0.20, 0.12, and 0.095 kg of body weight gain per additional kilogram of concentrate intake; Teixeira et al., 2006).


1 Kg of fertilizer/ha; 2 Ton of grain/ha; 3 1 = Bolsanello et al. (1975) and Oliveira et al. (1982), p.155; 2 = Malavolta (1989), p.273.

Table 3. Changes in constants of linear regression of reciprocal of bean production (x1,000 kg/ha) as a function of reciprocal of amount of fertilizer (kg/ha/year), by a second factor

According to the Biotechnology and Biological Sciences Research Council (1998), formerly known as AFRC (Agricultural and Food Research Council), all currently feed systems calculate the dietary requirements of energy and protein to meet the animals needs for maintenance and production. However, in practice, the situation is different, because there is no need for the farmer to meet the cow's nutritional requirements if it is against the economical interest. So, it is evident that studies in animal response to increasing levels of concentrate or specific nutrients are needed, as suggested by Lana (2003; p.87).

Although the animal's responses to nutrients are curvilinear, the daily weight gains estimated by the level 1 of NRC (1996) of beef cattle are linear as a function of intakes of metabolizable energy and protein (Figure 8A). In the same way, the milk production estimated by the model CNCPS 5.0 as a function of intakes of metabolizable energy and protein, and model NRC (2001) of dairy cattle as a function of intakes of net energy for lactation and metabolizable protein, were linear by using increasing levels of concentrate


The curvilinear response can also be verified with specific nutrients, such as the observed positive curvilinear response in milk production and negative curvilinear response in the efficiency of use of nitrogen by increasing the dietary crude protein content from 11 to 19% in cows with mean production of 38 kg of milk/day (Baik et al., 2006). In the third experiment of Figure 7, in addition to decreasing response in milk production, there was decreasing response in body weight variation with increase in the concentrate level (0.20, 0.12, and 0.095 kg of body weight gain per additional kilogram of concentrate intake;

Coefficient

2.794 8.516 6.718 8.205

183.36 46.235 15.623 28.056

6.7411 8.8241 10.186

180.38 90.586 132.74

(b) r2 ks

0.82 1.00 0.99 1.00

0.23 0.78 0.99 0.98

0.98 1.00 1.00

0.95 1.00 1.00

5 11 15

136 87 199

1 kmax 2 Sorce of

0.5 1.3 1.4 1.6

0.1 0.4 0.7 1.0

0.7 1.2 1.5

0.8 1.0 1.5 data 3

1

2

1

2

(a)

2.044 0.782 0.710 0.630

10.764 2.5229 1.3364 0.9539

1.3812 0.8181 0.6842

1.3257 1.0402 0.6684

concentrate or specific nutrients are needed, as suggested by Lana (2003; p.87).

1 Kg of fertilizer/ha; 2 Ton of grain/ha; 3 1 = Bolsanello et al. (1975) and Oliveira et al. (1982), p.155; 2 =

Although the animal's responses to nutrients are curvilinear, the daily weight gains estimated by the level 1 of NRC (1996) of beef cattle are linear as a function of intakes of metabolizable energy and protein (Figure 8A). In the same way, the milk production estimated by the model CNCPS 5.0 as a function of intakes of metabolizable energy and protein, and model NRC (2001) of dairy cattle as a function of intakes of net energy for lactation and metabolizable protein, were linear by using increasing levels of concentrate

Table 3. Changes in constants of linear regression of reciprocal of bean production (x1,000 kg/ha) as a function of reciprocal of amount of fertilizer (kg/ha/year), by a second factor According to the Biotechnology and Biological Sciences Research Council (1998), formerly known as AFRC (Agricultural and Food Research Council), all currently feed systems calculate the dietary requirements of energy and protein to meet the animals needs for maintenance and production. However, in practice, the situation is different, because there is no need for the farmer to meet the cow's nutritional requirements if it is against the economical interest. So, it is evident that studies in animal response to increasing levels of

Teixeira et al., 2006).

(kg/ha/year) Second factor Intercept

N (kg/ha) 0 30 60

N (kg/ha) 0 50 120

Fertilizer

N

N

P2O5

P2O5

Malavolta (1989), p.273.


1.3 1 Considering US\$0.966/kg of N, US\$1.208/kg of P2O5 and US\$0.36/kg of bean, it is necessary 2.68 and 3.36 kg of bean to pay 1 kg of N or P2O5. Efficiency worse than 2.68 or 3.36:1 for N or P2O5 is not economically desirable. These calculations can be used to choose the level of fertilization. 2 Level of fertilizer (kg/ha) - N in the first two cases or P2O5 in the last two cases.

Table 4. Efficiency of use of fertilizers (kg of bean/kg of fertilizer) calculated with base in the equations of Table 3

Fig. 6. Body weight gain (BWG) of growing bovines in pasture during the dry season, as a function of daily intake of supplement with 24% CP, in which the values among parenthesis represent the differential in kilograms of supplement given daily divided by the differential in weight gain, in relation to the previous treatment (Lana, 2005; Lana et al., 2005)

Rationality in the Use of Non Renewable Natural Resources in Agriculture 279

Up to 150 6 117 37 56 14 151-300 12 238 70 73 28 301-600 14 451 106 94 43 601-1200 14 821 111 159 66 1201-2400 3 1667 316 633 132 2401-4800 1 4000 300 1800 300

Up to 150 0.38 3.17 8.33 2.08 0.25 151-300 0.40 3.40 8.57 3.27 0.38 301-600 0.41 4.27 10.53 4.77 0.45 601-1200 0.59 7.41 12.51 5.15 0.41 1201-2400 0.42 5.27 12.66 2.63 0.21 2401-4800 1.00 13.33 13.33 2.22 0.17 Table 5. Number of producers, daily mean milk production by producer, area for the herd, number of animals and milking cows in the herd, and productivity indexes per area and per

The milk production in farmer level (first case) ranged from 60 to 4000 kg/producer/day. The increase in milk production was highly correlated with the number of milking cows (r = 0.94), followed by moderate correlation with the size of pasture (r = 0.67) and, surprisingly, the productivity per cow and per unit of area did not correlate with the milk production per producer (r = 0.11 and 0.06, respectively; Table 6). In the nation level (second case), the result repeated, in which there was high correlation of milk production/state/year with the total of milking cows in relation to productivity of milk/km2/year and milk/cow/year (r = 0.95,

Parameter Correlation (r) Parameter Correlation (r)

herd 0.94 Milk (kg/ha) 0.13

Total milking cows 0.93 Milk (kg/cow/day) 0.11 Area for the herd (ha) 0.67 Milking cows/ha 0.06

Table 6. Linear correlation of daily milk production by producer with: total of animals in the herd, total of milking cows, area for the herd, farmer size and some productivity indexes (daily milk production per hectare and per cow, milking cows per hectare and daily milk

Therefore, the milk production by Brazilian farmers is more dependent on the farmer size and pasture extension, than on productivity indexes, and similar effect occur with crop production (Lana, 2009). Agricultural showed the same results verified with dairy cattle, in

Total area in the farm 0.20 Milk (kg/total of

Area for the herd (ha)

Milk (Kg/milking cow/day)

Herd (number of animals)

Milk (Kg/total herd/day)

animals/day) -0.11

Milking cows (number)

Milking cows/total herd

Production per producer (Kg of milk/day)

Milk (Kg/ha/day)

Production (Kg of milk/ producer/day)

Kg of milk/ producer/day

Number of producers

Milking cows/ha

cow, as a function of production levels

0.55 and 0.51, respectively; Table 7).

production per total animals in the herd)

Total animals in the

(Figure 8B), as suggested by Lana (2005; p.290-291) and Lana (2007b; p.39 a 43). Therefore, in order to these systems be compatible with the tropical conditions, in which it is more evident the curvilinear responses to nutrients, it is necessary modifications in future versions, by adopting models of saturation kinetics.

Fig. 7. Production of milk (A) and efficiency of use of concentrate (B) as a function of intake of increasing level of concentrate in three experiments (Pimentel et al., 2006b, 2006c; Teixeira et al., 2006)

#### **6. Production versus productivity**

Two studies were conducted to evaluate the factors that affect milk production in Brazil, on farmer level or by state of federation (Guimarães et al., 2008; Lana et al., 2009). In the first case, data were collected from fifty producers that sell milk for a dairy plant in the south region of Rio de Janeiro state, including data of daily milk production by producer, with the respective data of production per cow and per hectare, farmer size and size area designated to the herd, total of milking cows and herd size, and breed (Table 5). In the second case, data were collected from EMBRAPA and IBGE in the years of 2004-2006, in which the emphasis was in milk production per state instead of production per producer.

Fig. 8. Mean daily weight gain of steers in pastures, observed and estimated by level 1 of NRC (1996) as a function of intake of metabolizable energy and protein in the supplement (A); and observed milk production (mean of data of Figure 7A) and estimated by CNCPS 5.0 and NRC (2001) as a function of intakes of metabolizable energy or net energy of lactation, respectively, and metabolizable protein (B)

(Figure 8B), as suggested by Lana (2005; p.290-291) and Lana (2007b; p.39 a 43). Therefore, in order to these systems be compatible with the tropical conditions, in which it is more evident the curvilinear responses to nutrients, it is necessary modifications in future

Efficiency, kg of milk/ .

Fig. 7. Production of milk (A) and efficiency of use of concentrate (B) as a function of intake of increasing level of concentrate in three experiments (Pimentel et al., 2006b, 2006c; Teixeira

Two studies were conducted to evaluate the factors that affect milk production in Brazil, on farmer level or by state of federation (Guimarães et al., 2008; Lana et al., 2009). In the first case, data were collected from fifty producers that sell milk for a dairy plant in the south region of Rio de Janeiro state, including data of daily milk production by producer, with the respective data of production per cow and per hectare, farmer size and size area designated to the herd, total of milking cows and herd size, and breed (Table 5). In the second case, data were collected from EMBRAPA and IBGE in the years of 2004-2006, in which the emphasis

A

Fig. 8. Mean daily weight gain of steers in pastures, observed and estimated by level 1 of NRC (1996) as a function of intake of metabolizable energy and protein in the supplement (A); and observed milk production (mean of data of Figure 7A) and estimated by CNCPS 5.0 and NRC (2001) as a function of intakes of metabolizable energy or net energy of lactation,

Milk (kg/cow/day) .

0123456 Concentrate (kg/cow/day)

Obs milk Est milk

B

kg of concentrate

0 0.2 0.4 0.6 0.8 1 1.2

> 01234567 Concentrate, kg/animal/day

Exp1 Exp2 Exp3 B

Exp1 Exp2 Exp3 A

versions, by adopting models of saturation kinetics.

01234567 Concentrate, kg/animal/day

was in milk production per state instead of production per producer.

Observed ME allowed MP allowed

0 0.8 1.6 2.4 3.2 Supplement, kg/animal/day

**6. Production versus productivity** 

0 0.3 0.6 0.9 1.2 1.5

respectively, and metabolizable protein (B)

ADG, kg/animal/day .

Milk, kg/animal/day .

et al., 2006)


Table 5. Number of producers, daily mean milk production by producer, area for the herd, number of animals and milking cows in the herd, and productivity indexes per area and per cow, as a function of production levels

The milk production in farmer level (first case) ranged from 60 to 4000 kg/producer/day. The increase in milk production was highly correlated with the number of milking cows (r = 0.94), followed by moderate correlation with the size of pasture (r = 0.67) and, surprisingly, the productivity per cow and per unit of area did not correlate with the milk production per producer (r = 0.11 and 0.06, respectively; Table 6). In the nation level (second case), the result repeated, in which there was high correlation of milk production/state/year with the total of milking cows in relation to productivity of milk/km2/year and milk/cow/year (r = 0.95, 0.55 and 0.51, respectively; Table 7).


Table 6. Linear correlation of daily milk production by producer with: total of animals in the herd, total of milking cows, area for the herd, farmer size and some productivity indexes (daily milk production per hectare and per cow, milking cows per hectare and daily milk production per total animals in the herd)

Therefore, the milk production by Brazilian farmers is more dependent on the farmer size and pasture extension, than on productivity indexes, and similar effect occur with crop production (Lana, 2009). Agricultural showed the same results verified with dairy cattle, in

Rationality in the Use of Non Renewable Natural Resources in Agriculture 281

The agriculture progress is based in improvements of animals and plants productivity per unit of area, which is only applicable when land is the limiting factor, but other factors are

Models of saturation kinetics are important tools to improve the efficiency and decrease costs of utilization of non renewable natural resources in agriculture, allowing the conservation of these resources for the future generations, and decreasing the negative

Fig. 9. Effect of cultivated area and productivity on production of some main cultures (coffee, sugarcane, corn and bean), in municipalities of Zona da Mata and Central of Minas

Http://scotaaron.com/resources2.html

inc., ISBN 0-8247-8994-6, New York, NY, United States

Aaron, S. (2005). *Some Statistics on Limited Natural Resources*, 31.07.2006, available from

Angus, J.F. (1995). Modeling N Fertilization Requirements for Crops and Pasture, In:

*Nitrogen Fertilization in the Environment*, P.E. Bacon, (Ed.), 109-127, Marcel Dekker

emerging as limiting, such as water, fertilizer and petrol.

**7. Conclusions** 

impacts in the environment.

Gerais state, Brazil.

**8. References** 

which cultivated area generally presents more than 90% correlation with crop production (Table 8 and Figure 9). Then, the concepts about agricultural production need to be revised, facing the actual problems related with the inadequate use and depletion of the non renewable natural resources, and environmental pollution.


Table 7. Linear correlation (r) of annual milk production by Brazilian states with total of milked cows, liters of milk/km2/year, liters of milk/cow/year and surface of the state (in km2).


Source: Lana & Guimarães (2010); n = number of municipalities; 1a = first harvest; 1b = mean of second and third harvest; 2 = racemes instead of ton (1,000 kg); 3 = number (x1000) of oranges instead of ton (1,000 kg); 4a = rice planted in wet land; 4b = rice planted in dry land, without or with irrigation; 5a,b,c = harvest 1, 2 and 3, respectively; Source of data: www.cidadesnet.com.br (year of 2003).

Table 8. Linear correlation (r) among some variables related to agricultural production, in municipalities of Zona da Mata and Central of Minas Gerais state, Brazil.
