Scenario testing using APSIM

Four simulation workshops were conducted to evaluate responses to low rates of N, manure and legumes. APSIM was used to examine scenarios to contribute analyses and insights for fertility management. The exercise also provided participants with exposure to system analysis using simulation, and showed how APSIM can answer resource allocation questions relevant to resource- poor farmers. Trade-offs in allocating limited capital resources were examined. One scenario involved a typical farm household with a shallow, infertile sand, and moderate weed pressure. The farmer plants in stages to avoid risk of crop failure and because of labour constraints. Household labour can control weeds in only half of the area because of children’s education demands. Funds are sufficient to purchase fertilizer or to hire labour for weeding or to hire a draft animal to prepare additional area for earlier sowing. Given the unreliable rainfall and its influence on such decision-making, the question relevant to a resource-poor farmer is, “On average, which allocation of resource offers the best prospects?”

Investment scenarios simulated included none (baseline), purchase of fertilizer, splitting the investment between fertilizer and labour hire for weeding, and investment only in labour for weeding.

Different strategies for targeting the fertilizer were also simulated and subjected to economic analysis.

Eleven seasons were simulated.

Simulated baseline yields were low and variable and in line with participants’ expectations. Figure 1 shows the response for whole-farm production when fertilizer is either used on the earliest sown crop or split between the first two sown fields, both of which were weeded. The simulation shows a large benefit from fertilizer investment: the average yield increase would give a good return. In four of eleven seasons, there would be little if any return. There was a marginal advantage to splitting the small fertilizer input over a larger area. The ON-treatment data represent the baseline household maize production with no investment in fertility. In only two seasons was the household food requirement exceeded, in line with the type of subsistence living faced by households in these situations.

Whole Farm - C onc v s Distrib N

0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0 0 3 0 0 0 3 5 0 0 4 0 0 0

1 9 8 0 1 98 2 19 8 4 1 9 8 6 19 8 8 1 9 9 0 1 99 2

0 N 3 5 N _ Fie ld 1 1 7 N _ Fie ld 1 &2

FIG. 1. Simulated whole-farm production (kg grain ha^–1) for different inputs and distribution of N (35 kg N ha^–1 on earliest sown field or 17.5 kg N ha^–1 on first two sown fields) on an infertile sand, Bulawayo, Zimbabwe.

Table I. Simulated whole-farm production statistics (eleven years of data) for three investment scenarios

Mean yield St. dev. Minimum Investment scenario

(kg grain ha^–1)

None 1,190 505 413

Buy/apply N

on first two sowings 2,620 883 923

Hire labour for weeding/apply N

on first two sowings 2,410 500 1,580

Overall, the highest expected return (Table I) was from applying fertilizer on the two early-sown, weeded fields. Splitting the investment between fertilizer and labour hire to weed field 3 was almost as good, but had lower risk expectation.

The scenario analyses have been effective in showing how simulation can contribute to researcher learning about fertility management technologies in small-scale farming. For example, one collaborating project has now included extra weeding as an experimental treatment in its on-farm testing. Another project included low rates of N as part of its on-farm experimentation. The key ingredient missing at that time was direct input from farmers. A subsequent workshop in September 2000 focused on the sharing of on-farm experimental data and using simulation with farmers, thus obtaining farmer input in formulating scenarios and feedback on simulated, as well as experimental, results.

In October 2001, the knowledge gained from these new approaches to linking simulation to participatory research was extended through an international workshop, Linking Logics — Taking Simulation Models to the Farmers. This was a joint venture between a participatory research and gender analysis (PRGA) group and a soil-water and nutrient-management (SWNM) programme together with ICRISAT, CIMMYT and APSRU and, importantly, farmer groups in the Zimbabwean SAT. The combination of workshop sessions that brought together the participatory research and

simulation scientists, and the on-farm field sessions that brought those groups together with the farmers, proved challenging and exciting, and produced new insights into the problems of resolution of smallholders’ production constraints.

3.2. Research in India

Detailed results are reported elsewhere in this volume by Venkata Ramana [2]. The years were drier than average; July to December growing-season rainfall was 445 mm in 1999 and 500 mm in 2000, compared with the long-term average of 540 mm. Crop yields of the order of 1.5 t ha^–1 are respectable considering the degraded nature of the Mahabubnagar-District soils, and the below-average rainfall.

Intercropping sorghum with pigeon pea resulted in sorghum production similar to that by a sole sorghum crop, and production of additional good quality pigeon pea grain, plus (according to the chosen experimental protocol) a return to the soil of about 2 t ha^–1 of legume residue. The pigeon pea crop took little N (<1.5%) either from fertilizer or from FYM. Using the ¹⁵N-dilution method, the N derived from the atmosphere was 43% in sole pigeon pea and 65% in intercropped pigeon pea.

Applying FYM provided additional N to the crops, with 6 to 11% of its N being released and taken up in the first season. This was a consequence of the quality of the FYM (Table II), which was relatively high in total N and P with high concentrations of nitrate and ammonium. Urea N was more available:

castor retrieved 26 to 30% of applied N in the first season; pigeon pea took up 25 to 26%, and sorghum 29 to 38%. Associated growth increases were substantial, with FYM doubling grain yields, whereas urea and FYM plus urea each increased grain yields five-fold.

The second cropping season provided the opportunity to contrast the traditional system in which castor follows sorghum with an alternative of castor following a crop with a legume component. However, in this case, castor yield was not influenced by the preceding cropping system, being similar to that in 1999, though sorghum yield was substantially higher. Farmyard manure was effective in that it almost doubled yield, and urea, either with FYM or alone, more than doubled yields.

A group of local farmers selected and tested some alternative systems in their own fields. The traditional system of rotating sorghum and castor without inputs of fertilizer or FYM was invariably out-yielded by the intercropping alternative or by sole crops with inputs. The yields of these farmers and the yield increases obtained with alternative treatments were similar to those obtained on-station, indicating that the farmers had managed these crops carefully. They reported to the researchers that they were impressed by the improved systems, but it is too early to report if there has been any adoption at a larger scale or by other farmers in the district.

Table II. Quality and quantity of farmyard manure applied to plots in the on-station experiment at Palem in 1999, 2000 and 2001

Org C Total N Total P NO₃-N NH₄-N Year

(%) (mg kg^–1)

1999 17.7 1.96 1.02 2720 102 2000 20.5 2.20 1.04 82 237 2001 16.0 1.80 1.02 2290 210 Org C Total N Total P NO3-N NH4-N

(kg ha^–1)

1999 265 29.4 15.3 4.08 0.15 2000 307 33.0 15.6 0.12 0.36 2001 240 27.0 15.3 3.43 0.32

Table III. Does APSIM adequately simulate sorghum and pigeon-pea yields at Palem?

Observed yield

Simulated yield Crop Treatment

(t ha^–1) Sorghum Control (0N) 0.47 0.65

FYM 1.26 1.49

60N 2.69 1.97

FYM + 45N 2.14 1.16

Pigeon pea I/crop with sorghum 0.56 0.24

Table IV. What are the longer-term implications of soil-fertility inputs for crops at Palem? — means of APSIM-simulation outputs for eleven years

0N FYM 60N FYM + 45N System

(t ha^-1)

Continuous sorghum 1.33 1.91 3.13 3.08 Intercrop

Sorghum 2.18 2.28 2.58 2.56 Pigeon pea 0.40 0.40 0.39 0.39

Table V. What is the likely outcome of farmers applying less than the recommended fertilizer-N rate of 60 kg N ha^-1 to sorghum at Palem? — means of APSIM-simulation outputs for twelve years

Parameter 0N 10N 20N 30N 60N

Grain yield (t ha^-1) 1.03 2.04 2.63 2.88 3.13 Crop failure (years) 6/12 2/12 0/12 0/12 0/12

The third component of this work was the use of cropping-systems simulation to add value to the on-station and on-farm trials by exploring wider options than could be studied in trials. This component of the work lagged behind the field research, partly because data from the field were needed as inputs, and because there is still a tendency to leave modelling to the end of the project. With the APSIM simulator, sorghum can be simulated readily. Pigeon pea can also be simulated using the new pigeon pea module [3](Robertson et al., 2001). Fertilizer and FYM inputs can be simulated using the relevant soil-fertility modules. The missing capability is the absence of a module for castor growth and development.

APSIM did a reasonable job of simulating sorghum and pigeon pea yields at Palem (Table III), though the number of comparisons was still limited. The responses to N fertilizer and FYM inputs were encouraging.

Eleven-year simulation runs of continuous sorghum and sorghum/pigeon pea, with four soil-fertility inputs, are summarized in Table IV. On average, this predicts that the modest FYM inputs should significantly increase yields of continuous sorghum, and that N fertilizer should raise sorghum yield to about 3 t ha^–1. Intercropping sorghum and pigeon pea should enhance sorghum yield even without N inputs, and with FYM and N fertilizer, yields of 2.5 t ha^–1 sorghum and 0.40 t ha^–1 pigeon pea should be possible.

Simulation also showed that N fertilizer at fractions of the recommended rate could be attractive to resource-poor farmers (Table V); 10 kg N ha^–1 could double sorghum yield from 1.03 to 2.04 t ha^–1. Even more interesting, without N application, sorghum would fail to produce grain in 50% of years, yet crop failure would be reduced to zero with an annual application of only 20 kg N ha^–1 of urea.

3.3. Research in Zimbabwe

The first year of this research was one of high rainfall: 140% of normal. The overall mean maize yield of 1.1 t ha^–1 was respectable, and the fertilizer treatment yielded 23% more grain than the manure treatment. As a result of the high rainfall, the water-management treatment yielded the same as the control treatment. No further results were available to this author at the time of writing.

The on-farm component of this work did not provide conclusive results in the first season.

With the simulations, the same set of six treatments were tested using weather data from the 1991–

1992 season to the 1997–1998 season. The outputs are summarized in Table VI.

Some initial model runs were conducted using an assumed treatment of growing the crop with and without soil-fertility inputs. It is desirable to verify adequate model performance without inputs, then to determine whether the model responds to manure and fertilizer inputs in an acceptable manner. As often happens, the initial experience raised questions regarding the quality of the inputs, and this has meant some further characterization of inputs, which is now in progress. This work is continuing with inputs from J.P. Dimes and N. Nhamo.

In Zimbabwe, the 1990s were notable for frequency of drought years, particularly in 1991–1992. The simulations can be interpreted to indicate that tied ridging did not improve yields, and this is likely to be true in such a light soil with low water-holding capacity. Manure alone was not predicted to improve grain yield. Fertilizer inputs remained as the only input to substantially improve maize grain yield. The simulations indicated also that fertilizer inputs reduce the frequency of crop failures due to dry years, and this confirms observations in field trials that so far have not been given the publicity that they deserve.

4. DISCUSSION

This work has not yet identified alternatives to traditional sorghum-castor rotations on the degraded lands in the Mahabubnagar District of Andhra Pradesh. India. The indications are that it might be agronomically useful to insert pigeon pea into sorghum-castor rotations, and to try inputs of FYM and urea. These recommendations could have been made by an experienced agronomist without huge inputs of research, so what was the value of using ¹⁵N labelled materials? And why would the use of a simulation tool add further value to the exercise?

Table VI. Simulated maize grain yield as affected by inputs of farmyard manure, fertilizer and water management between 1991–1992 and 1997–1998, Makaholi, Zimbabwe

91–92 92–93 93–94 94–95 95–96 96–97 97–98 Mean Treatment

(t ha^–1)

FYM-TR^a 0 4.97 0 0.02 0.04 0.03 0.04 0.73 FYM-OF^b 0 5.01 0 0.02 0.04 0.03 0.04 0.73 Fert-TR^b 0 7.23 4.95 4.18 6.33 0.09 4.77 3.95

Fert-OF 0 7.45 4.86 4.31 6.14 0.08 4.60 3.96 Comb-TR 0 7.33 0.04 4.46 0.07 4.55 4.29 2.96 Comb-OF 0 7.45 0.04 4.42 0.06 4.69 4.25 2.99

a Tied ridge.

b Open furrow.

The case for using isotopic methods is that N management is a critical factor in any farming system used in the study area, but simply adding fertilizer at a recommended rate is not a solution for a resource-poor farmer, particularly when choices for making N inputs also include animal manures, crop residues, and BNF. The value of the different sources varies considerably in this semi-arid situation with large seasonal variation in rainfall. The use of ¹⁵N permits researchers to quantify the value of the different N inputs, showing for example that sole pigeon pea obtained 57% of its N from the soil, whereas pigeon pea intercropped with sorghum took only 35% of its N from the soil.

According to a recent evaluation of published work on the effect of drought on BNF, pigeon pea’s symbiosis with rhizobia is more sensitive to drought than is the pigeon pea plant itself [4] (Serraj et al.

1999), and these simulations support that observation. Dealing with FYM has been difficult because of the magnitude of variation in its quality factors which is now becoming evident as information accumulates on animal manures in countries of the south. Similar problems have occurred with crop residues, though usually it has been assumed that crop residues are of little consequence because of burning or of total harvest and removal. While it is true that burning is still widespread in some regions (e.g. the Punjab of India), and total grazing occurs in many African and Asian countries, there are signs that the use of crop residues in the management of crop nutrition may achieve more importance in the future, as is now being seen with mucuna residues in Malawi (J. Rusike, personal communication). It is clear that in countries of the south, sources of N for crops will need to include BNF, crop residues and animal manures in addition to (or instead of) fertilizer, and that assessment of quality factors of different inputs will be needed. There is a clear indication that input combinations will be increasingly used, for example small inputs of fertilizer in combination with FYM. The simulation results indicate that a re-evaluation of fertilizer recommendations is needed; no farmer in the study districts would apply the recommended rate of N, and there are likely to be large yield benefits from fractions of the recommended rate.

The simulation analysis provided an opportunity to test alternative rates of application of fertilizers, and also to examine the likely outcome of combinations of sources of N. The outcome was a clear indication that small fertilizer inputs could greatly increase crop yields, and that such small inputs could be more attractive to smallholder farmers than the much higher recommended rates. Subsequent on-farm experimentation (G.M. Heinrich et al., personal communication) supports this idea, and has led to further on-farm testing in other districts of the drier parts of Zimbabwe. The research here has also indicated that modest N inputs not only increase yield, but also reduce the risk of crop failure.

Given that the monsoon has not failed in India during the period from 1990, it can be suggested that the frequent “droughts” experienced in Mahabubnagar District are more due to N deficiency than shortage of water. In Zimbabwe, this idea is not yet likely to find acceptance because of the highly skewed relationship between fertilizer cost and availability, and the value of grain produced.

Given the importance of N deficiency, how do we researchers make recommendations for management when there are several potential sources of input material, and also very fluid economic conditions? Good experimentation is still needed, but simulation tools are now available that permit us to examine a range of options more efficiently than could be done by traditional experimentation. In these studies, the modelling outputs suggest that we should question some established beliefs about crop production in the study areas.

Production of the SAT crops sorghum and millet has been declining in terms of total product and area sown. Sixteen years of the Sorghum and Millet Improvement Program has resulted in the release of numerous improved varieties but has not resulted in higher yields. These varieties perform well in good soils on research stations, but are no better than traditional cultivars on degraded lands. Improved management options are generally not adopted because of a combination of lack of knowledge, shortage of labour, lack of funds for investment, difficulty in marketing, perception of risk, alternative investment priorities, etc. ICRISAT and its partners have tackled this question in several ways, including the use of improved dissemination methods [better farmer-participatory research (FPR), farmer field schools], focus on more relevant options (modified tied ridges, legumes for farming systems, seed priming, small packets of seed and fertilizer, improved use of FYM, easier striga management), and use of new tools for helping farmers (linking simulation to FPR, engaging change

4. CONCLUSIONS

— In India, use of ¹⁵N has helped understand the role of BNF in pigeon pea, the modest recycling of N from FYM, and the efficiency of N uptake from fertilizer.

— In India, dealing with crop production where the major growth constraint is nutritional rather than water (as appears to be the case at Palem), it is important to have a good understanding of the efficiency of crop use of soil-fertility inputs, and equally important for researchers to engage with farmers so that the farmers become involved in conducting their own research.

— Systems simulation, for example APSIM, is now sufficiently advanced to be useful in looking at a wider range of options over longer periods than can be achieved by traditional research alone.

— In future, systems simulation should be used to help engagement with farmers, as has already been done in Australia and Zimbabwe.

— In view of the clients of this research not having funds to invest in soil fertility, fertilizer rates tested should be lower than those locally recommended.

— To break away from the present poor adoption of soil-fertility technologies, future experimentation should utilize new tools, such as isotopes, residue-quality evaluation, modern on-farm methods and systems simulation to make faster impact on farmer household livelihoods in tropical semi-arid regions.

ACKNOWLEDGMENTS

We acknowledge the encouragement of the late T. Bappi Reddy, formerly Director of RARS, Palem, in the initiation of this collaboration between ANGRAU and ICRISAT. We are grateful to the APSRU, Australia, particularly Peter Carberry and Stuart Brown, for support in the application of the APSIM modelling tool.

REFERENCES

[1] McCOWN, et al., APSIM: a novel software system for model development, model testing and simulation in agricultural systems research, Agric. Systems 50 (1996) 255–271.

[2] RAMANA, M.V., et al., Management of nitrogen and evaluation of water-use efficiency in traditional and improved cropping systems of southern Telangana region in Andhra Pradesh, India (this volume).

[3] ROBERTSON, M.J., et al., Predicting growth and development of pigeonpea. A simulation model, Field Crops Res. 70 (2001) 89–100.

[4] SERRAJ, R., et al., Symbiotic N₂ fixation response to drought, J. Exp. Bot. 50 (1999) 143–155.

MANAGEMENT OF NITROGEN AND EVALUATION OF WATER-USE