Total Energy Input in Agro Climatic Zones
Energy in one form or another is a crucial input to agricultural production. Continually rising prices, increasing proportion of commercial energy in the total energy input to agriculture and the growing scarcity of commercial energy sources, such as fossil fuels, have necessitated the more efficient use of these sources for different crops. Wheat is one of the important crops of the Punjab state. The area under wheat has gone up to 3.33 Mha in 1996–97 from 1.4 Mha in 1960–61, i.e. an increase of 138%. During the same period, the productivity of wheat increased by 173% from 1244 to 3397 kg/ha. Keeping in mind the importance of the wheat crop, a need was felt for the more efficient and economic utilisation of all energy inputs for production. The scope for bringing new areas under wheat crop is very limited, so the yield should be raised by adapting mechanised and optimal practices. reported that the yield is linearly correlated with the total energy input, and fertiliser energy was found to affect the wheat and maize yields more than the irrigation energy. reported that most of the energy was used through irrigation and fertiliser for maize–wheat crop rotation. analysed the data regarding fertiliser energy use and crop yield for the wheat crop and concluded that the use of a fertiliser energy input in excess of 8500 MJ/ha reduced the yield of the wheat crop. reported that larger farmers used energy better to achieve the maximum yield of wheat. Therefore, the present investigation has tried to analyse the different mathematical relations between the crop yield and total energy input and establish the optimal levels of energy input for various agro-climatic zones of the Punjab state in India.
The state of Punjab has been divided into six agro-climatic zones depending upon soil type and condition. These zones are: zone 1 (sub-mountains undulating region with coarse, sandy to loamy-sand soils); zone 2 (undulating plain region with coarse, fine loamy and silty soils); zone 3 (central plain region with sandy-loam soils); zone 4 (western plain region with fine loamy and sandy-loam soils); and zone 5 (western region with excessively drained and sandy-loam soils); zone 6 (flood prone area). The soil type and conditions vary a lot from one zone to another hence their productivity. Depending upon the productivity of the soil in different zones, technological inputs also change and so do the productivities of the crops.
The data for the study were collected from different agro-climatic zones of the Punjab for the years 1986–1995. There were 1284 farmers under study: 135 from zone 1; 231 from zone 2; 164 from zone 3; 108 from zone 4; and 646 from zone 5. Various mathematical equations were fitted for each zone, viz. linear, Y=a+bX; exponential, Y=ae(bX); reciprocal curve, 1/Y=a+bX; quadratic, Y=a+bX+cX2; Robb’s parabolic, Y=ae(bX)+cX2; Nelder’s curve, Y=X/(a+bX+cX2); Wood’s curve, Y=aXbe(?cX); Gupta and Nigam’s curve, Y=a+bX+c/X; log quadratic, Y=aXbX2c; and log-linear, Y=aXb where Y is the yield in kg/ha and X is the total energy input in MJ/ha for the wheat crop. Optimal levels of energy input in different agro-climatic zones were deduced using constrained and unconstrained frontier production functions.
The use of power sources in different farm operations for cultivation of wheat under different agro-climatic zones is given in Table 1. Seed-bed preparation used maximum man power in zone 1 (47.5 h/ha), followed by zone 3 (42.8 h/ha), zone 2 (31.6 h/ha), zone 4 (7.2 h/ha) and the least occurred for zone 5 (4.9 h/ha). The use of animal power in zone 1 was greatest (44.0 h/ha) because this was a rainfed zone, where not many tractors are available and it was a minimum in zone 4 (0.3 h/ha). There was no use of animal power in zone 5 for seed bed preparation. Use of a tractor in seedbed preparation was greater in zone 4 (6.9 h/ha), followed by zone 2 (4.9 h/ha), zone 5 (4.3 h/h), zone 3 (3.8 h/ha) and zone 1 (2.1 h/ha). The use of a tractor depended on the type of soil, number of tillage operations performed and the previous crop. Sowing was done by tractor-drawn seed-drills. Being a rainfed region, which is called the Kandi area (zone 1), the available irrigation facilities were limited and those that exist are provided with water from Government wells, operated by 35 hp electric motors: that is why the maximum electricity was used in zone 1 (329 kWh/ha) even though the irrigation was limited. This is followed by zone 4 (239 kWh/ha), zone 3 (192 kWh/ha), zone 2 (78 kWh/ha) and zone 5 (21 kWh/ha). In zone 5, mostly canal-water was used to irrigate the fields — this is the cheapest source of energy. On the other hand, in zone 2, diesel engine operated pump sets were used. Weeding was done manually in zone 1 whereas weedicides were used in all other zones. Harvesting and threshing used the maximum man-power in zones 2 and 3 (192 h/ha each) and this was a minimum in zone 4 (72.4 h/ha). Combines have also been used in zones 3, 4 and 5 for harvesting and threshing. Sprayers were used in zones 3, 4 and 5 for 5.3, 8.5 and 3.0 h per ha, respectively. There was no manual weeding in zone 4. Transportation was done by animal-drawn carts in zones 1, 2 and 3, whereas it was done by tractor-drawn trailers in zones 4 and 5.
Seed-bed preparation, irrigation, harvesting and threshing consumed more than 80% of the total operational energy in all zones. The use of energy was a maximum in zone 3 (7911 MJ/ha) and a minimum in zone 5 (4834 MJ/ha). The use of energy in zone 5 was a minimum due to the canal irrigation which is the cheapest source of irrigation as far as energy is concerned. A greater use of a fertiliser was observed in zones 3, 4 and 5 than the recommended value. The use of machinery was greater in zones 4 and 3 compared with those in other zones. The yield of wheat was observed to be largest in zone 4 (4341 kg/ha) and least in zone 1 (1955 kg/ha). The low yield in zone 1 was due less exploitation of available resources.
Different mathematical relations as discussed in the previous section, were fitted between the wheat yield and the total energy input for all agro-climatic zones. Only those relations were considered which have R2 values exceeding 10%. A Nelder’s curve exhibited best relations between yield and total energy input for zone 1. The co-efficient of determination (R2) was 30.8%. The calculated t-values of the partial regression co-efficients b and c were 2.93 and 0.82, respectively, so indicating that the total energy input played an important role at a 5% level of significance in explaining the productivity level.
The plot of yield versus total energy input shows that the variation lies over the entire range of energy inputs. The variation in data was observed from ?50 to +90%. The points below the predicted line show that the farmers achieved a low yield despite providing a large energy input through irrigation via deep tubewells which really did not contribute significantly towards the yield. Some farmers achieved high yields despite low energy inputs which might be due to timely availability of rain or the exploitation of other inputs. It is also clear, from Fig. 2, that the yield increased as the total energy input increased and a further increase in energy input may increase the yield further.
The maximum co-efficient of determination (R2) between yield and total energy input was 16.3% for Robb’s parabolic and log-quadratic in zone 3, but Robb’s parabolic was plotted due to its better representation of the data than the log-quadratic curve. The calculated t-values of partial regression coefficients b and c were 3.98 and ?3.66, respectively, so indicating that the value of the total energy input (and its square) played an important role in explaining the productivity at 5% level.
Gupta and Nigam’s curve exhibited the best relations between yield and total energy input for wheat in zone 4. The co-efficient of determination (R2) between yield and total energy input was 19.4%. The calculated t-values of the partial regression co-efficients b and c were 2.82 and 2.09, respectively, so indicating that the total energy-input and its inverse played a role at 5% level of significance in explaining the productivity.
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