Weather-based irrigation scheduling for various farms (commercial and small-scale)

J. M. de Jager and J. A. Kennedy. Department of Agrometeorology, University of the Orange Free State, Bloemfontein, South Africa

SUMMARY

Effective methods for the rapid dissemination of irrigation information to various farmers were sought. Three levels of technology transfer were examined - high, which is based upon the Putu-Irrigation computer model and weather data; intermediate, which replenishes cumulative daily reference crop evaporation; and minimum (conventional), which applies fixed amounts of water at fixed time intervals. Irrigation application efficiency (AE) was defined as crop transpiration per unit applied water. Wheat-grain yields were measured on seven farms and applied irrigation on six farms. Irrigation scheduling was simulated using automatic weather station data, the Putu simulation model and nine different scheduling strategies. Simulated AE obtained with the two high technology methods were compared to those obtained with three intermediate and four conventional scheduling strategies. For high, intermediate and minimum technology AE averaged 82%, 64% and 46% respectively. Intermediate technology transfer could possibly offer an effective, convenient, inexpensive alternative in multi-user situations. Procedures for disseminating the relevant information to many users were developed.

Decision support for scheduling irrigation on both commercial and small-scale farms entails the transfer of large volumes of data intended for either numerous fields on a single farm or for numerous small plots. Cutting costs is important to low-input farmers, but not for commercial farmers, who require high quality, scientific information. Low-input farmers prefer simple instructions for specific fields. The dissemination of the many data required to both types of user from a central source does, however, present logistical problems.

Weather-based irrigation scheduling methods suitable for both types of operation were investigated. The specific objectives of the paper were to assess how the most relevant and useful information may be produced and disseminated to large numbers of users.

MATERIALS AND METHODS

Site, climate, crop and soil

The investigation was carried out for location Douglas in the arid irrigation region of the western Free State, Republic of South Africa. The annual normal rainfall and temperature in this area are 315 mm and 24 °C respectively. Corresponding normals for the summer and the winter are 236 mm and 29 °C, and 79 mm and 14°C respectively. The irrigation of spring wheat, Triticum aestivum Cultivar SST4, planted on 27 July 1994 on a 1.8 m deep sandy loam was studied. Seasonal saturation vapour deficit was 2.2 kPa.

The levels of decision support technology

Water application efficiency was compared for three levels of decision support technology, viz. high, intermediate and minimum. The latter corresponds to present conventional practice.

For high technology, an automatic weather station and the Putu-Irrigation simulation model (De Jager, 1992) which has been shown by De Jager et al. (1987) to accurately schedule irrigation, was used. Putu-Irrigation is an irrigation model named after the Zulu word for maize porridge. It was used to simulate daily values of crop transpiration (Ev) as the product of reference crop evaporation (Eo) and empirical crop specific evaporation coefficients as well as daily soil profile water deficits. The model has been validated by De Jager et al. (1987) and Mottram and de Jager (1994).

In intermediate technology, cumulative reference crop evaporation is replenished before crop water stress occurs. The most accurate equation for determining reference crop evaporation from a short grass surface (Eo), viz. the Penman-Monteith equation (see Jensen et al. 1990) was used.

Irrigation practices and simulations

In practice two types of irrigation occur in the western Free State, viz. demand-driven centre pivot (or sprinkler) or supply-driven flood irrigation. While the former case applies predominantly to commercial farming, the latter is appropriate to small-scale low-input farming. The capacity of centre pivot systems limits water application rates to 12 mm/d. For supply-driven flood or bucket irrigation, individual applications generally exceed 50 mm. The most common fixed interval irrigations practised, are either bi-daily, weekly, or fortnightly with application rates of 12, 80 or 100 mm per application, respectively.

All simulations assumed the soil profile to be at field capacity (10 kPa tension) at the time of planting. This required an initial irrigation of 50 mm to fill the 1.8 m soil rooting zone and is in agreement with common practice; as are the flood schedules, which were tested, appropriate for state river or canal schemes. Deficit irrigation, which replaces a simulated soil water deficit of 60 mm with 50 mm of irrigation (cf. method 2 in Table 1), was also examined.

The nine different methods of scheduling irrigation investigated, including the irrigation scheduling decision criteria for each, are described in Table 1.

Observations

All water applications were as measured by farmer cooperators in three or four rain gauges installed under each centre pivot. The number of plots per farm varied between one and five.

Wheat-grain yields were as delivered at the farm cooperative. The average yield for all farms delivered to the farm cooperative from surrounding farms was 5 200 kg/ha (cf. strategy 5, Table 1).

Calculation of irrigation water application efficiencies

Water application efficiencies attained during irrigation were both simulated and derived from field measurements. A modified definition of irrigation application efficiency (AE) was adopted.

Since, for a given seasonal saturation vapour pressure deficit, the seasonal crop transpiration (Ev) is directly related to biomass production (Kieselbach, 1916), Ev may be taken to represent the true outcome of an irrigation operation for which the input may be deemed to be the water applied (in millimetres) by irrigation (I) plus rainfall (R). Hence, since efficiency is the ratio of outcome to input, application efficiency (AE) may be defined:

TABLE 1 - Measured wheat-grain yields at seven sites and the farm cooperative together with relevant simulated and field derived application (AE) and water-use (WUE) efficiencies for the 1994 wheat growing season in the western Free State

Scheduling method		Irrigation		AE		WUE Field Eq. 3 (kg/m3)	Meas. grain yield (kg/ha)
		Simulated (mm)	Measured (mm)	Simulated Eq. 1 (%)	Field Eq. 2 (%)
HIGH
	1. Putu modelled water use and adviser expertise		665	81	82	1.10	6100
			640	84	91	1.18	6500
			590	90	95	1.14	6300
			680	79	83	1.14	6300
			722	74	69	1.01	5600
	Average		659	82	84	1.16	6160
	2. DEF > 60 mm, I = 50 mm	700		77
INTERMEDIATE
	3. I = S Eo when S - 50 mm	800		67
	4. I = S Eo, I < 12 mm	865	865	62	53	.92	5100
	5. I = DEF, I < 12 mm		870	62	54	.94	5200
	Average	833	868	64		.93	5150
MINIMUM
	6. Bi-daily 12 mm/d	878		61
	7. Fortnightly I = 100 mm	950		57
	8. Weekly I = 80 mm	1570		35
	9. Centre pivot conventional practice 12 mm/d	1706		32	27	.92	5100
	Average	1276		46	27	.92	5100

I - Irrigation amount (mm).

DEF - Profile soil water deficit below field capacity assumed to be the volumetric soil water content corresponding to 10 kPa (mm).

Eo - Daily reference crop evaporation for short grass as computed by the Penman-Monteith equation (mm).

AE = Ev/(I + R), (1)

This AE was expressed as a percentage and computed each day using the cumulative values of modelled Ev, R and I and Equation 1. The seasonal variation in AE for strategies 1, 4 and 8, representing the three levels of technology transfer are depicted in Figure 1. Also, seasonal final values of AE are listed in Table 1.

Furthermore, to make possible the comparison of computed values of AE with some form of field observation, seasonal AE values were derived from measured wheat-grain yield using Equation 2, cf. Table 1. Here the total transpiration required to produce a measured crop yield was calculated using a transpiration equivalent of grain yield of 0.92 kg/ha/mm. The latter was derived from the Kieselbach (1916) equation assuming a harvest index for wheat of 0.6; a normalized biomass/transpiration ratio for C₃ plants of 40 kg kPa/mm (Monteith, 1990); and the measured seasonal saturation vapour pressure deficit of D = 2.2 kPa. Thus,

AE = 2.2 Yg/[(0.6 * 40)(I + R)] = 0.092 Yg/(I + R) (2)

where, Yg is wheat-grain yield (in kilograms per hectare).

FIGURE 1 - Daily values of cumulative application efficiency simulated for a wheat crop in the western Free State for HIGH, INTERMEDIATE and MINIMUM levels of technology transfer

Water-use efficiency (WUE) is here defined as harvested yield per unit water transpired, cf. Equation 3. Values derived from field observations were expressed in kilograms per cubic metre and are given in Table 1.

WUE = 0.1 Yg/(1 + R) (3)

RESULTS AND DISCUSSION

No water stress was simulated in any of the treatments. Simulated transpiration totalled to Ev = 552 mm in all treatments. In the 1994 wheat growing season, 20 mm of rainfall was recorded. The benefits of adviser expertise in particularly the late season for the high technology application is most evident in Figure 1. Table 1 shows that the simulated AEs for high, intermediate and minimum technology may be expected to approximate 82%, 64% and 46% respectively. For minimum technology, the simulated efficiencies ranged between 32 and 61%; the latter could be attained with a sprinkler system. Fortnightly flood irrigation reduces soil evaporation and hence its favourable efficiency of 57% compared to 35% for weekly flood. The simulated average AEs for all three technologies agreed reasonably well with the field derived efficiencies of 84%, 54% and 27% respectively. Strategy 6 may not be considered as a practicable solution for low-input farms, but offers exciting possibilities for commercial farms.

Simulated AE for intermediate technology closely followed that of high technology except in the late season, cf. Figure 1, but exceeded minimum technology by approximately 8% (Table 1). Average seasonal values of AE obtained with high technology (82%) were found to be approximately 36% higher than the value of 46% estimated for minimum technology. Intermediate technology has also been found to be popular among farm cooperatives in sub-humid areas (Mottram et al., 1994).

The measured grain yields shown in Table 1 suggest that high technology, by reducing the amount of alkaline water applied, and hence the soil alkalinity, improves the efficiency with which nutrient fertilizers are utilized. Furthermore, the high risk of over irrigation associated with minimum technology methods, apart from wasting water will in addition leach nutrients from the root zone. High alkalinity and over irrigation could thus partly explain the lower yields of 5100 kg/ha (see Table 1) obtained with the minimum technology methods. All in all, this emphasizes the need for implementation of more advanced irrigation scheduling techniques, such as a combined use of appropriate models and advice from experts.

Financial returns due to reduced irrigation ($US 0.13 per millimetre) and improved yields ($US 0.182 per kilogram) for high technology were better than returns from intermediate and minimum technology by approximately $US 175 and $US 193 respectively per hectare.

Technology transfer methods

Based on the results and discussion above, the following methods for disseminating irrigation scheduling advice may be suggested:

High technology. Soil water deficits are modelled using an appropriate model run on locally measured weather data, plus data from a weekly forecast based upon long-term mean weather records. Analysis of these deficits and crop growth stage and the application of personal expertise, enables the local adviser to communicate to farmers via fax, telephone modem or e-mail, how much and when to irrigate.

Intermediate technology. Each week, daily Eo computed from the Penman-Monteith equation and relevant weather data is displayed on noticeboards, or conveyed telephonically to farmers who simply replenish cumulative reference crop evaporation (method 3 or 4, Table 1). Enterprising operators and cooperatives apply appropriate crop evaporation coefficients.

Minimum technology. Fixed interval irrigations of given amounts are applied at regular intervals (methods 7 to 9, Table 1).

CONCLUSIONS

From the simulations, measurements and computations carried out, it may be concluded that:

· Use of high technology transfer could improve seasonal irrigation AE by approximately 36% above that attainable using conventional methods (i.e., minimum technology).

· An intermediate level of technology which replenishes accumulated reference crop evaporation, should improve AE by approximately 8% above conventional farming practice.

· Both the high and intermediate procedures are considerably better than flood irrigation of 80 mm at weekly intervals for which an AE = 35% might be expected. Fortnightly flood irrigations, by reducing simulated soil evaporative losses, could improve AE to approximately 57% for this practice.

· Applications of 12 mm/d every second day throughout the season appears to be a more efficient conventional practice than flood irrigation for the western Free State.

Convenient methods for disseminating high and intermediate technology information have been developed.

REFERENCES

De Jager, J.M. 1992. The PUTU Decision Support System. Monograph of the Department of Agrometeorology, University of the Orange Free State, Bloemfontein. 95 p.

De Jager, J.M., Van Zyl, W.H., Kelbe, B.E. and Singels, A. 1987. Research on a weather service for scheduling the irrigation of winter wheat in the OFS. Final report by the Department of Agrometeorology of the University of the Orange Free State to the Water Research Commission. WRC Report No. 117/1/87. pp 277.

Jensen, M.E., Burman, R.D. and Allen, R.G. 1990. Evapotranspiration and irrigation water requirements. ASCE Manuals and Reports on Engineering Practice No. 70. ISBN 0-87262-763-2. ASCE, New York. 323 p.

Kieselbach, T.A. 1916. Transpiration as a factor in crop production. University of Nebraska. Agric. Exp. Stn. Bulletin 6.

Monteith, J.L. 1990. Steps in crop climatology. In: Challenges in Dryland Agriculture: A Global Perspective. P. Unger (ed.). pp. 273-282. Proc. of the Int. Conf. on Dryland Farming (14-18 Sept., 1988). Amarillo/Bushland Texas.

Mottram, R. and De Jager, J.M. 1994. Research on Maximising Irrigation Project Efficiency in Different Soil-Climate Situations. Report to the WRC by the Department of Agrometeorology, University of the Orange Free State. 198 p.