AGR2FFT - Future Farming Technologies: APSIM Models Workshop Report

This report analyzes various APSIM models related to future farming technologies, focusing on the interpretation of graphs depicting soil water content, cumulative runoff in different soil types (clay and sand fallow), surface organic matter changes, and the impact of nitrogen fertilizers (Urea, nitrate, ammonium) on soil composition and crop yields. It examines the relationship between rainfall patterns, soil nitrate levels, and denitrification, as well as the distribution of nitrate in soil after fertilizer application over time. The report further investigates the effect of different nitrogen fertilizer applications on sorghum yields and analyzes chickpea plant yields and their probability of exceeding certain yield amounts. The analysis provides insights into soil dynamics, nutrient management, and crop performance under varying environmental conditions and agricultural practices.

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Graph 1
The blue line marked ‘esw’ represents the extractable soil water with the variables being
rain, esw, and the constant time. The ‘esw’ graph presents with irregularity directly proportional
to the rainfall patterns. Thus, it rises with increased soil water content due to increased rainfall,
and similarly, it drops with the fall in the rain. The irregular ‘esw’ is directly proportional to the
rainfall since, with the increase in precipitation, there is increased soil water content and with
considerable soil and plant properties in the area, the ‘esw’ will increase however a fall in
rainfall would introduce soil water loss mechanisms such as evaporation and without
replenishment from frequent rain, the esw will drop gradually until another period of rainfall
commences then it steadily rises again (Newbwerry et al., 2017).
Graph 2
The blue and red lines are representations of the cumulative runoffs. The blue line
represents overland flow on a clay fallow and the red one on sand fallow with variables as
cumulative runoffs, rain, and constant time. The graph shows that clay fallow has a higher
cumulative runoff compared to the sand fallow. This is because these two soil types both have
different properties contributing to their water retention and saturation abilities (Abu-Hamdeh et
al., 2018). Sand, on the other hand, has coarse and large grains which allow for water infiltration
hence constant unsaturation, and little cumulative runoffs since most of the rainwater often seep
much deeper into the soil making runoffs minimal unless it is on a low land with water table
close to the surface.
Graph 3

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The blue line represents the surface organic matter in a soil sample over a year with
variables of rain and surface organic matter cover and constant time. The blue line appears to be
irregularly declining with a relation to the rainfall pattern since during minimal rainfalls the
graph seems to be on a plateau phase indicating zero decline however presence of rainfall seems
to initiate the drop further with each moment the decline being even much steeper than the
previous decline throughout the year. This is relative since at the beginning there are tremendous
plant and animal residues (Saunders and Rogers, 2017).
Graph 4
The line represents cumulative runoffs in clay soils of different characteristics with
variables of cumulative runoffs, rain, and constant time. The blue line represents cumulative
runoffs in clay fallow while the red line represents cumulative runoffs on clay soil with residue.
The two characteristic soil samples have different cumulative runoff rates with clay fallow
having a higher capacity than the clay residue. This is because clay fallow has been exposed to
the erosion agents such as wind and rain which have removed the top loose soil layer that first
absorbs the water.
Graph 5
The three lines represent nutrient used in, and as fertilizers, the blue line represents Urea,
the red line nitrate whereas the green line the ammonium component with these also as the
variables and constant time. The graph shows the Urea curve surging by exponentially rising and
similarly declining at the same rate. This is because Urea when introduced into the soil, it is
converted to ammonia/ammonium ions a reaction catalyzed by specific soil bacteria. With time,
ammonium will accumulate as Urea gets depleted. Ammonium ions are also oxidized through

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nitrification to create nitrite which is further oxidized to nitrate by soil bacteria (Galloway et al.,
2004). This reaction thus utilizes the ammonium in the soil until their depletion. The nitrate
curve rises exponentially due to the rich ammonium soil in the first short period then it rises
steadily over time since if not utilized by plants, it accumulates in the soil making it nitrate-rich.
Graph 6
The blue line represents the rainfall patterns, the red line the extractable soil water, the
green line the total soil nitrate while, the orange line the denitrification level, and all these are
also the variables with the time constant. The chart reflects a relative plateau nitrate levels after
an exponential rise in the first days, rainfall patterns corresponding to the ‘esw’ content and the
denitrification pattern closely similar to the rainfall pattern. An anoxic environment such as soils
more so wet and poorly ventilated soils are conducive for denitrification. The rainfall contribute
to the anoxic environment since during these periods, free and dissolved oxygen in the soil are
often limited hence the nitrate would act as terminal electron acceptor getting reduced to a N2 in
the process. (Revsbech and Sorensen, 2013).
Graph 7
The blue line represents the nitrate distribution few days after application of the fertilizer,
whereas the red line represents the distribution of nitrate in the soil after about five months. The
axes are labeled as depth in the Y-axis and nitrate amount in the X-axis. The chart shows that
immediately after fertilizer application, the nitrate closer to the soil surface is highest and it
decreases steadily as you increase the depth to a point beyond which there are no nitrates, at
depth 450. This is because only small amounts of nitrate have been formed and time has not
passed to allow for distribution hence the high concentration near the topsoil where the fertilizer

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was applied. The red line however shows that after some time, nitrate have mostly spread all
over the soil with the majority of it being at the plant root levels and this amount decreases as
you move towards the surface and also from the root position as you move further deeper into
the ground (Knowles, 2011).
Graph 8
The three lines represent sorghum yields in soils with different nitrogen composition rates
due to dissimilar fertilizer applications. The yields present as the variable and time is constant.
The blue line represents sorghum yields with no fertilizer, and the red one represents yields
corresponding to 30kg nitrogen fertilizer application and the green one are the yields after 60kg
nitrogen fertilizer application. The difference is because nitrogen is essential in plant growth;
hence, optimum application ensure the best possible yields (Xu et al., 2011).
Graph 9
The chart represent chickpea plants yields planted in the same soil repeatedly over
several years with time as constant and varying yield amounts. The blue bars represent chickpea
yields of 10 plants whereas the red bars represent yields from 15 chickpea plants. Although there
might be a slight change in the yields over time, chickpea plants do not often rely on nitrates for
efficient growth as they are legumes and fix their nitrogen from the atmosphere into the soil
(Elkoca et al., 2007).
Graph 10
The blue and red lines in the chart represent chickpea plants percentage probabilities of
exceeding certain yield amounts as 10 and 15 chickpea plants respectively. The axes are as the
probability of exceedance above in the Y-axis and yield amount in the X-axis. It is evident that

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the probability of the 15 chickpea plants producing more yields is higher than the 10 chickpea
plants. For instance, at 50%, there is a probability of the 10 chickpea plants to yield about 2050
tonnes compared to the 15 chickpea plants probability to yield about 2300 tones.

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References
Abu-Hamdeh, N.H., Ismail, S.M., Al-Solaimani, S.G. and Hatamleh, R.I., 2018. Runoff and
erosion as affected by tillage system and polyacrylamide in two sandy loam soils
differing in silt and clay contents in semi-arid regions. Soil & Environment, 37(1).
Elkoca, E., Kantar, F. and Sahin, F., 2007. Influence of nitrogen fixing and phosphorus
solubilizing bacteria on the nodulation, plant growth, and yield of chickpea. Journal of
Plant Nutrition, 31(1), pp.157-171.
Galloway, J.N., Dentener, F.J., Capone, D.G., Boyer, E.W., Howarth, R.W., Seitzinger, S.P.,
Asner, G.P., Cleveland, C.C., Green, P.A., Holland, E.A. and Karl, D.M., 2004. Nitrogen
cycles: past, present, and future. Biogeochemistry, 70(2), pp.153-226.
Knowles, O.A., Robinson, B.H., Contangelo, A. and Clucas, L., 2011. Biochar for the mitigation
of nitrate leaching from soil amended with biosolids. Science of the Total
Environment, 409(17), pp.3206-3210.
Newberry, S.L., Prechsl, U.E., Pace, M. and Kahmen, A., 2017. Tightly bound soil water
introduces isotopic memory effects on mobile and extractable soil water pools. Isotopes
in environmental and health studies, 53(4), pp.368-381.
Revsbech, N.P. and Sørensen, J. eds., 2013. Denitrification in soil and sediment (Vol. 56).
Springer Science & Business Media.
Saunders, T. and Rogers, C.B., 2017. Improving soil health with a multispecies cover cropping
system: preliminary and intermediate data and analysis.

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Xu, G., Fan, X. and Miller, A.J., 2012. Plant nitrogen assimilation and use efficiency. Annual
review of plant biology, 63, pp.153-182.