Content uploaded by Harsh Pathak
Author content
All content in this area was uploaded by Harsh Pathak on Aug 03, 2024
Content may be subject to copyright.
The authors are solely responsible for the content of this paper, which is not a refereed publication. Citation of this work should state
that it is from the Proceedings of the 16th International Conference on Precision Agriculture. EXAMPLE: Last Name, A. B. & Coauthor,
C. D. (2024). Title of paper. In Proceedings of the 16th International Conference on Precision Agriculture (unpaginated, online).
Monticello, IL: International Society of Precision Agriculture.
Advancing Adaptive Agricultural Strategies:
Unraveling Impacts of Climate Change and Soils on
Corn Productivity Using APSIM
Harsh Pathak a, Corban J. Warren b, Dennis R. Buckmaster a, Diane R. Wang b
a. Department of Agricultural and Biological Engineering, Purdue University,
West Lafayette, IN, USA, 47907
b. Department of Agronomy, Purdue University, West Lafayette, IN, USA,
47907
A paper from the Proceedings of the
16th International Conference on Precision Agriculture
21-24 July 2024
Manhattan, Kansas, United States
Abstract.
With unprecedented challenges to achieve sustainable crop productivity under climate change
and varying soil conditions, adaptive management strategies are required for optimizing
cropping systems. Using sensors, cropping systems can be continuously monitored and the
data collected by them can be analyzed for making informed adaptive management decisions to
enhance productivity and environmental sustainability. But sensors reflect present conditions or
provide some history, yet decisions should also consider what is yet to occur. This study
leverages the use of the state-of-the-art biophysical model, Agricultural Production System
sIMulator (APSIM), which takes the genetics (G), environmental (E), and management (M) data,
to predict the growth and yield of corn (Zea Mays L.), a major crop for United States. Using
digital twin models, we can project outcomes of different management decisions under varying
environmental conditions and soil types and in context of climate change. The key objectives of
this research were to elucidate the impacts of varying soil conditions and climate scenarios on
corn growth and yield and further identify the best optimum practices (planting date, amount of
nitrogen fertilizer, and amount of irrigation) to improve yield and profitability. In doing so, we
characterize system resilience by running simulations over 38 years of past weather data for
four locations having four different soil types and under two different climate scenarios.
Keywords.
APSIM, Adaptive Management, Biophysical Modeling, Climate Change, Digital Twin, Irrigation
Management, Nitrogen Fertilizer Management, Simulation
Proceedings of the 16th International Conference on Precision Agriculture
21-24 July, 2024, Manhattan, Kansas, United States
2
Introduction
Climate change poses a formidable challenge to global food security as variations in
meteorological parameters profoundly impact crop production. These variations in
meteorological parameters constitute increases in nocturnal and diurnal warming and irregular
rainfall patterns and causes abiotic and biotic stresses (Abendroth, 2021). This issue is
particularly critical for corn (Zea Mays L.) production in the United States, given its substantial
economic importance and its role as a major source of calories and nutrients both for humans
and animals. To meet the food demands of growing global population, which is expected to be
9.8 billion by 2050, cereal production, including corn, must increase by approximately 70-100%
(Bayu, 2020; Sharma, 2022). This increase in corn production can be achieved by developing
genotypes and adaptive farm management strategies that are resilient to new climatic
conditions. Adaptive farm management strategies are important because once the seeds with
given genetic (G) traits have been sown, the characteristics and response of the seeds are fixed
and cannot be changed and new genetic traits cannot be added and their realized performance
can be only modulated by changing the management practices in the given environmental (E)
conditions This interplay between G, E, and M has been widely studied to design ideotypes for
the future (Jamshidi, 2023). Yet, there is a paucity of research focused on recommending
adaptive M practices tailored to local conditions and understanding how these M strategies
interact with others to impact corn yield.
Therefore, there is a pressing need to reframe the research question related to agricultural
production, aiming to enable stakeholders to make informed and adaptive farm management
decisions in context of climate change (Thornton, 2014). Some of the management practices
that could be changed/adapted in context of climate change are planting date, date(s) and rates
of nitrogen (N) fertilizer, and irrigation rules. For example, planting corn early in the season can
mitigate the impact of excessive heat in the growing season and can potentially preserve yield.
However, planting too early in the season can decrease yield due to frosts (Pathak, 2023).
Applying too little N fertilizer reduces yield, while excessive amounts result in diminishing
returns as corn N uptake becomes constant, leading to negative ecological and environmental
consequences. Additionally, water stress during the critical growth stages of corn production will
reduce yield while irrigating more increase incidence of disease and water logging (Pathak,
2023).
Biophysical (process-based) or crop growth models can be used to understand the
consequences of the variation in management practices in context of climate change (Baum,
2020). These models are built upon the physiological understanding of plant growth and
processes and are represented in non-linear differential equations. Some of the commonly used
crop growth modeling platforms include Agriculture Production System sIMulator (APSIM)
(McCown, 1996), Decision Support for Agrotechnology Transfer (DSSAT) (Jones, 2003), and
World Food Studies (WOFOST) (Van Diepen, 1989). Typically, these models are often used for
qualitative understanding of crop response in terms of G, E, and M rather than for their
quantitative prediction accuracy. (Pathak, 2023) used APSIM to simulate the growth of corn
under different N fertilizer treatments and evaluate the effect of rainfall on corn yield and other
environmental factors. Similarly, (Baum, 2020) used APSIM to evaluate how the planting dates
of corn might change in Iowa in context of climate change. They simulated the corn production
under six climate change scenarios and reported that the optimum planting date will shift by ±5
days with an increase in yield by 10%. Nandan (2021) simulated the corn production under
different climate scenarios and found that that reduction in 30% of precipitation could reduce the
mean yield by 10% and will require adaptive irrigation strategies to mitigate the loss. However,
none of these studies have specifically addressed how the interactions between different
management practices might affect corn yield under varying climate change scenarios.
Therefore, the primary objective of this research is to examine the influence of distinct
management decisions, namely planting dates, N fertilizer application rates, and irrigation
protocols, on corn yields within four varied soil types and under two climatic conditions. We will
comment on how individual treatments and their interactions impact corn yield.
Proceedings of the 16th International Conference on Precision Agriculture
21-24 July, 2024, Manhattan, Kansas, United States
3
Materials and Methods
Model Description
APSIM is a mechanistic, process-based, open-source simulator that helps to simulate farming
systems including crops, soil, and environmental models (Holzworth, 2014). Its popularity has
surged due to its modular architecture and user-friendly interface (Brown, 2014). In this
research, APSIM next generation (version 2022.6.7044.0) was used along with following
modules: maize model, SOILN model, and SOILWAT to simulate the corn production under
different weather conditions on the daily time steps (Soufizadeh, 2018; Probert, 1998).
Experimental Setup
In this study, diverse sets of management practices were simulated under different climatic
conditions to understand its impact on corn yield. Three different planting dates, namely April 1
(early planting), April 30 (falls under optimum planting window), and May 30 (late planting) were
simulated on APSIM. Furthermore, three different amounts of urea-N 142 kg/ha (75% of the
common practice), 190 kg/ha (common practice), and 237 kg/ha (125% common practice) were
included in the study along with two irrigation rules (zero irrigation and irrigation using 75
percent of plant available water content (PAWC) as trigger point and 100 percent of PAWC as
stopping point). The N fertilizer was applied six weeks after planting, typically corresponding to
the V4-V6 growth stage. Pioneer P1197 cultivar with a cumulative relative maturity of 111 days
was used in this study and was sown at a population of 8 plants per m2 with 1 bud per plant at a
row spacing of 750 mm (about 2.46 ft) and a depth of 50 mm (about 1.97 in). To simulate the
potential climate impacts, two global warming scenarios were followed (Filippelli, et al., 2020):
• Mid-century projections: low carbon dioxide emissions (550 ppm), where the base line
daily temperature was increased by 2.5 K (2.5 °C) and base line daily rainfall was
increased by 6%.
• End-century projections: high carbon dioxide emissions (670 ppm), where the base line
daily temperature was increased by 5.5 K (5.5 °C) and base line rainfall was increased
by 10%.
Site Description and Agrometeorological Data
The APSIM next generation (version 2022.6.7044.0) was used for running the simulation for
four locations, namely Agronomy Center for Research and Education (ACRE) (40˚29’20.9” N,
87˚0’11.7” W), Northeast Purdue Agriculture Center (NEPAC) (41° 6' 51.85'' N, 85° 26' 56.03''
W), Southeast Purdue Agriculture Center (SEPAC) (39° 2' 28.64'' N, 85° 31' 24.24'' W), and
Pinney Purdue Agriculture Center (PPAC) (41° 27' 3.61'' N, 86° 56' 28.51'' W). The APSIM next
generation facilitates direct download and integration of weather and soil data into the
simulation. The weather data required for the experiment simulation was linked with the NASA
POWER gridded database (https://power.larc.nasa.gov/data-access-viewer/) and was directly
downloaded by the APSIM interface for ACRE farms into APSIM-readable format(.met
extension) from 1984 to 2021. The weather data included six weather variables: maximum and
minimum temperatures (degrees Celsius), total precipitation (millimeters per day), average
incident shortwave radiation (Megajoule per square meter per day), wind speed (meters per
second), and specific humidity (grams of water per kilogram of dry air). Additionally, APSIM is
linked with ISRIC soil database (https://www.isric.org/), which provides the soil information by
location. The data includes soil features from 0 cm to 180 cm depth, encompassing physical
properties like soil bulk density, wilting point, field capacity, saturation point, and soil saturated
conductivity; chemical properties such as soil pH; and organic properties including organic
carbon content and are presented in Appendix table 1 to 4. In this study, both the weather and
soil data were directly downloaded and integrated into the simulations, but the weather file
remained the same across four locations to evaluate the effect of changing soil properties on
yield. For changing the weather files as per climate change, the simple climate controller plugin
of APSIM was used to change the temperature, rainfall, and carbon dioxide.
Proceedings of the 16th International Conference on Precision Agriculture
21-24 July, 2024, Manhattan, Kansas, United States
4
Statistical evaluations
Simulation results from APSIM were exported into excel (.xlsx) format and subsequently utilized
in RStudio for statistical evaluation, namely analysis of variance (ANOVA) to determine the effect
of different treatment on corn yield.
Results and Discussions
Effect and interaction of management practices on corn yield under different climate
scenarios and soil types
Figure 1 and Table 5 (in appendix) show that planting date, N fertilizer amount, irrigation rules,
soil types, and weather scenarios have significant impact on corn yield.
Figure 1: Simulated effect of nitrogen fertilizer, irrigation rules, location (soil type), and weather scenarios on corn yield
Planting within the optimum window results in higher yield, while misalignment reduces yield by
exposing plants to heat stress or frosts. The results align well with the literature (Van Roekel,
Proceedings of the 16th International Conference on Precision Agriculture
21-24 July, 2024, Manhattan, Kansas, United States
5
2011), where they reported that corn yield decreases around15 to 30% with the delay in four
weeks of planting. Corn yield is highly dependent on the amount of N fertilizer applied, as seen
in Figure 1, except for mid-century and end-century late planting (May 30). These findings align
with existing literature (Zelenák, 2022), which reported that corn yield increased approximately
5000 kg/ha, with the increase in N fertilizer rates from 0 kg/ha to 150 kg/ha. This increase in
yield is because N fertilizer promotes plant growth, increases biomass, and helps plants to
reach their genetic yield potential (Soufizadeh, 2018).
In addition to N fertilizer amount, water availability also significantly affects corn yield (p-value <
0.0001). Figure 1 illustrates that the application of irrigation improves the corn yield significantly
across all locations and under different climate change scenarios and is also shown in (Pathak
2023). Irrigating reduces corn sensitivity to precipitation, by supplementing soil moisture
required at critical growth stages of corn development, particularly during the grain filling stage.
From the figure 1, it is clearly evident that why Indiana farmers are now slowly adopting
irrigation practices for corn production (Dong, 2023). (Ruis, et al., 2021) found that full irrigation
can improve the corn yield by 11% as compared to limited irrigation, where water applied was 5
to 10 cm less than full irrigation. Apart from the management practices, which can be controlled
by humans, climate scenarios (weather) and soil properties play crucial roles in determining
corn yield. The figure shows that yield varies significantly with change in soil properties and
climate scenarios, even with consistent management practices.
From table 5, it is evident that planting date has significant interactions with N fertilizer amount.
Early planting enhances N uptake due to cooler soil temperatures and reduced volatilization
losses (Liu, 2019). Conversely, higher temperature leads to increased water evaporation from
soil, impacting soil moisture levels and consequently N uptake from the soil. Therefore, it can be
seen from figure 1, that for all the locations with the climate change the optimal planting date will
be early in April to get higher yield. For mid-century and late-century scenarios, planting on May
30 does not significantly increase yield due to the higher temperatures. The warmer days
accelerate the vegetation stage, and without side-dress N supplementation until July, N uptake
is limited. This interaction between the N and soil moisture is further illustrated by the significant
interaction between N fertilizer amount and irrigation rules and are shown with p-value less than
0.0001. Irrigating under extreme heat conditions will supplement the soil moisture and thereby
improves N uptake. Soil moisture retention capacity is dependent on soil physical and chemical
properties and is also influenced by weather parameters (temperature and rainfall) and in turn
also affects N uptake. Therefore, it can be concluded planting date and N fertilizer amounts
have significant interactions with soil properties and temperatures.
Conclusion
In this study, we explored how crop growth models, such as APSIM can be used to help make
informed farm management decisions at farm-level by simulating the long-term experiment at
four locations across Indiana under two climate change scenarios. The simulation study results
demonstrates that planting date, irrigation, N fertilizer amount, soil properties, and weather
scenarios had significant impact on corn yield (p-value < 0.0001). Furthermore, with the climate
change scenarios, the planting date of corn needs to shift ahead, the optimal period for these
locations across Indiana will be in early April, irrigation will be required to supplement the soil
moisture to help mitigate the higher temperatures, and an N fertilizer increment would not be
helpful when delaying the planting beyond optimal window. Based on these results, it can be
concluded that the interplay between plants’ physiological needs and environmental factors is
complex and requires strategic and adaptive planning. Tailoring the farm management
guidelines to site-specific conditions by using real-time weather and soil data to improve
resilience to climate variability.
Acknowledgments
This work is sponsored by the NSF award number EEC-1941529 and by National Institute of Food
Proceedings of the 16th International Conference on Precision Agriculture
21-24 July, 2024, Manhattan, Kansas, United States
6
and Agriculture, U.S. Department of Agriculture, under the agreement number of 2022-38640-
37486 through the North Central Region SARE program under project number GNC23-371.
References
Abendroth, L. J., Miguez, F. E., Castellano, M. J., Carter, P. R., Messina, C. D., Dixon, P. M., & Hatfield, J. L. (2021).
Lengthening of maize maturity time is not a widespread climate change adaptation strategy in the US Midwest.
Global Change Biology, 27(11), 2426-2440. doi: https://doi.org/10.1111/gcb.15565
Baum, M. E., Licht, M. A., Huber, I., & Archontoulis, S. V. (2020). Impacts of climate change on the optimum planting
date of different maize cultivars in the central US Corn Belt. European Journal of Agronomy, 119, 126101.
doi :https://doi.org/10.1016/j.eja.2020.126101
Bayu, T. (2020). Review on contribution of integrated soil fertility management for climate change mitigation and
agricultural sustainability. Cogent Environmental Science, 6(1), 1823631.
doi: https://doi.org/10.1080/23311843.2020.1823631
Brown, H. E., Huth, N. I., Holzworth, D. P., Teixeira, E. I., Zyskowski, R. F., Hargreaves, J. N., & Moot, D. J. (2014). Plant
modelling framework: software for building and running crop models on the APSIM platform. Environmental Modelling
& Software, 62, 385-398. doi: https://doi.org/10.1016/j.envsoft.2014.09.005
Dong, Y., Christenson, C., Kelley, L., & Miller, S. (2024). Trends and future of agricultural irrigation in Michigan and
Indiana. Irrigation and Drainage, 73(1), 346-358.. doi: https://doi.org/10.1002/ird.2862
Filippelli, G. M., Freeman, J. L., Gibson, J., Jay, S. J., Moreno-Madriñán, M. J., Ogashawara, I., & … & Wells, E. M.
(2020). Climate change impacts on human health at an actionable scale: A state-level assessment of Indiana, USA.
Climatic Change, 1985-2004. doi: https://doi.org/10.1007/s10584-020-02710-9
Holzworth, D. P., Huth, N. I., deVoil, P. G., Zurcher, E. J., Herrmann, N. I., McLean, G., ... & Keating, B. A. (2014).
APSIM–evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software,
62, 327-350. doi: https://doi.org/10.1016/j.envsoft.2014.07.009
Jamshidi, S., Murgia, T., Morales-Ona, A. G., Cerioli, T., Famoso, A. N., Cammarano, D., & Wang, D. R. (2023).
Modeling interactions of planting date and phenology in Louisiana rice under current and future climate conditions.
Crop Science. doi: https://doi.org/10.1002/csc2.21036
Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., ... & Ritchie, J. T. (2003). The
DSSAT cropping system model. European journal of agronomy, 18(3-4), 235-265. doi: https://doi.org/10.1016/S1161-
0301(02)00107-7
Liu, S., Wang, X., Yin, X., Savoy, H. J., McClure, A., & Essington, M. E. (2019). Ammonia volatilization loss and corn
nitrogen nutrition and productivity with efficiency enhanced UAN and urea under no-tillage. Scientific Reports, 9(1).
doi: https://doi.org/10.1038/s41598-019-42912-5
McCown, R. L., Hammer, G. L., Hargreaves, J. N. G., Holzworth, D. P., & Freebairn, D. M. (1996). APSIM: a novel
software system for model development, model testing and simulation in agricultural systems research. Agricultural
systems, 50(3), 255-271. doi: https://doi.org/10.1016/0308-521X(94)00055-V
Nandan, R., Woo, D. K., Kumar, P., & Adinarayana, J. (2021). Impact of irrigation scheduling methods on corn yield
under climate change. Agricultural Water Management, 255, 106990.
doi: https://doi.org/10.1016/j.agwat.2021.106990
Pathak, H., Buckmaster, D. R., Messina, C., & Wang, D (2023). Crop growth model: Optimal Application of Nitrogen
Fertilizer in Corn for Economic Returns and Environmental Sustainability. ASABE Annual International Meeting.
Omaha, Nebraska: American Society of Agricultural and Biological Engineers.
doi :https://doi.org/10.13031/aim.202300421
Pathak, H., Buckmaster, D. R., Messina, C. D., & Wang, D. (2023). Optimizing Irrigation Management for Sustainable
Corn Production: A Simulation Study Using APSIM. ASA, CSSA, SSSA International Annual Meeting. St. Louis: ASA-
CSSA-SSSA. Retrieved from https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/152055
Probert, M. E., Dimes, J. P., Keating, B. A., Dalal, R. C., & Strong, W. M. (1998). APSIM's water and nitrogen modules
and simulation of the dynamics of water and nitrogen in fallow systems. Agricultural systems, 56(1), 1-28.
doi: https://doi.org/10.1016/S0308-521X(97)00028-0
Ruis, S. J., Burr, C., Blanco-Canqui, H., Olson, B., Reiman, M., Rudnick, D., & ... & Hanford, K. (2021). Corn residue
baling and grazing impacts on corn yield under irrigated conservation tillage systems. Agronomy Journal, 2387-2397.
doi :https://doi.org/10.1002/agj2.20642
Sharma, R. K., Kumar, S., Vatta, K., Bheemanahalli, R., Dhillon, J., & Reddy, K. N. (2022). Impact of recent climate
change on corn, rice, and wheat in southeastern USA. Scientific Reports, 12(1), 16928.
doi: https://doi.org/10.1038/s41598-022-21454-3
Soufizadeh, S., Munaro, E., McLean, G., Massignam, A., Van Oosterom, E. J., Chapman, S. C., ... & Hammer, G. L.
(2018). Modelling the nitrogen dynamics of maize crops–Enhancing the APSIM maize model. European Journal of
Agronomy, 100, 118-131. doi: https://doi.org/10.1016/j.eja.2017.12.007
Thornton, P. K., Ericksen, P. J., Herrero, M., & Challinor, A. J. (2014). Climate variability and vulnerability to climate
change: A review. Global change biology, 20(11), 3313-3328. doi: https://doi.org/10.1111/gcb.12581
Proceedings of the 16th International Conference on Precision Agriculture
21-24 July, 2024, Manhattan, Kansas, United States
7
Van D iepe n, C . V., Wo lf , J . V., Van Keu len, H., & Ra pp old t, C. (198 9). WOFO ST: a sim ul ati on mod el o f cr op pr od uct io n.
Soil use and management, 5(1), 16-24. doi: https://doi.org/10.1111/j.1475-2743.1989.tb00755.x
Van R oek el , R. J., & Co ul ter, J. A. (2 011 ). Ag ro nom ic res pons es o f co rn t o pla nt ing date and pla nt de ns ity. Agronomy
Journal, 103(5), 1414-1422.doi: https://doi.org/10.2134/agronj2011.0071
Zelenák, A., Szabó, A., Nagy, J., & Nyéki, A. (2022). Using the Ceres-Maize model to simulate crop yield in a long-term
field experiment in Hungary. Agronomy, 12(4), 785. doi: https://doi.org/10.3390/agronomy12040785
Appendix
Table 1: Soil physical, chemical, and organic properties for farm at ACRE
ACRE (40˚29’20.9” N, 87˚0’11.7” W)
Depth
BD
AD
LL 15
DUL
SAT
KS
LL
KL
XF
PAWC
pH
Carbon
(cm)
(0-1)
0-15
1.40
0.12
0.229
0.345
0.442
29.57
0.229
0.08
1
0.116
6.59
4.500
15-30
1.40
0.21
0.229
0.345
0.442
21.70
0.229
0.08
1
0.116
6.59
4.500
30-60
1.49
0.23
0.230
0.346
0.408
15.78
0.230
0.08
1
0.116
7.12
2.250
60-90
1.55
0.18
0.182
0.312
0.385
13.26
0.182
0.08
1
0.130
7.12
1.420
90-120
1.64
0.13
0.125
0.270
0.351
14.46
0.125
0.08
1
0.145
7.23
0.750
120-150
1.80
0.11
0.111
0.254
0.291
20.48
0.111
0.06
0.9
0.143
7.86
0.750
150-180
1.80
0.11
0.111
0.254
0.291
26.13
0.111
0.03
0.5
0.143
7.86
0.750
BD stands for bulk density (g/cc), AD stands for Air dry (mm/mm), LL15 stands for wilting point at 15 bars (mm/mm), SAT stands for
saturated water content (mm/mm), DUL stands for drained upper limit (mm/mm), KS stands for saturated soil conductivity (mm/mm),
LL stands for lower limit (mm/mm); and PAWC are crop specific parameter and in this case, it’s for maize (mm/mm), LL stands for
maize lower limit (mm/mm), KL stands for maize water conductivity between soil layers (/day), XF stands for maize extinction
coefficient, pH depicts the soil pH, Carbon (total %) is the soil organic matter percentage
Table 2 : Soil physical, chemical, and organic properties for farm at NEPAC
NEPAC (41° 6' 51.85'' N, 85° 26' 56.03'' W)
Depth
BD
AD
LL 15
DUL
SAT
KS
LL
KL
XF
PAWC
pH
Carbon
(cm)
(0-1)
0-15
1.41
0.08
0.235
0.351
0.430
21.70
0.235
0.06
1.000
0.116
6.30
2.733
15-30
1.54
0.08
0.230
0.324
0.390
15.33
0.230
0.06
0.907
0.094
6.30
1.622
30-60
1.59
0.08
0.230
0.313
0.378
10.82
0.232
0.06
0.769
0.081
6.38
1.160
60-90
1.61
0.08
0.228
0.313
0.370
11.14
0.253
0.04
0.717
0.060
6.78
0.977
90-120
1.61
0.07
0.219
0.312
0.371
12.58
0.282
0.02
0.718
0.030
7.13
0.902
120-150
1.61
0.07
0.217
0.312
0.373
13.49
0.299
0.01
0.726
0.013
7.42
0.894
150-180
1.61
0.07
0.214
0.312
0.376
14.72
0.312
0.00
0.736
0.000
7.64
0.885
BD stands for bulk density (g/cc), AD stands for Air dry (mm/mm), LL15 stands for wilting point at 15 bars (mm/mm), SAT stands for
saturated water content (mm/mm), DUL stands for drained upper limit (mm/mm), KS stands for saturated soil conductivity (mm/mm),
LL stands for lower limit (mm/mm); and PAWC are crop specific parameter and in this case, it’s for maize (mm/mm), LL stands for
maize lower limit (mm/mm), KL stands for maize water conductivity between soil layers (/day), XF stands for maize extinction
coefficient, pH depicts the soil pH, Carbon (total %) is the soil organic matter percentage
Proceedings of the 16th International Conference on Precision Agriculture
21-24 July, 2024, Manhattan, Kansas, United States
8
Table 3 Soil physical, chemical, and organic properties for farm at SEPAC
SEPAC (39° 2' 28.64'' N, 85° 31' 24.24'' W)
Depth
BD
AD
LL 15
DUL
SAT
KS
LL
KL
XF
PAWC
pH
Carbon
(cm)
(0-1)
0-15
1.42
0.08
0.233
0.365
0.420
38.76
0.233
0.06
1.000
0.132
5.90
2.281
15-30
1.54
0.08
0.230
0.335
0.385
27.37
0.230
0.06
0.876
0.105
5.80
1.041
30-60
1.59
0.08
0.233
0.324
0.375
17.72
0.235
0.06
0.748
0.089
5.70
0.590
60-90
1.64
0.08
0.237
0.316
0.355
14.05
0.264
0.04
0.602
0.052
5.75
0.374
90-120
1.68
0.08
0.228
0.304
0.340
15.87
0.291
0.01
0.516
0.013
5.94
0.295
120-150
1.68
0.07
0.224
0.300
0.340
17.01
0.299
0.00
0.509
0.001
6.02
0.287
150-180
1.69
0.07
0.219
0.294
0.340
18.56
0.294
0.00
0.000
0.000
6.12
0.277
BD stands for bulk density (g/cc), AD stands for Air dry (mm/mm), LL15 stands for wilting point at 15 bars (mm/mm), SAT stands for
saturated water content (mm/mm), DUL stands for drained upper limit (mm/mm), KS stands for saturated soil conductivity (mm/mm),
LL stands for lower limit (mm/mm); and PAWC are crop specific parameter and in this case, it’s for maize (mm/mm), LL stands for
maize lower limit (mm/mm), KL stands for maize water conductivity between soil layers (/day), XF stands for maize extinction
coefficient, pH depicts the soil pH, Carbon (total %) is the soil organic matter percentage
Table 4 Soil physical, chemical, and organic properties for farm at PPAC
PPAC (41° 27' 3.61'' N, 86° 56' 28.51'' W)
Depth
BD
AD
LL 15
DUL
SAT
KS
LL
KL
XF
PAWC
pH
Carbon
(cm)
(0-1)
0-15
1.36
0.06
0.170
0.286
0.430
77.75
0.170
0.06
1.000
1.360
6.00
3.123
15-30
1.45
0.05
0.160
0.267
0.400
73.37
0.160
0.06
1.000
1.450
5.95
1.979
30-60
1.47
0.05
0.158
0.257
0.395
69.23
0.158
0.06
1.000
1.472
5.93
1.418
60-90
1.51
0.04
0.142
0.232
0.380
73.37
0.160
0.05
1.000
0.072
6.05
0.874
90-120
1.53
0.03
0.118
0.200
0.380
90.4
0.156
0.03
1.000
0.044
6.24
0.607
120-150
1.53
0.03
0.114
0.196
0.380
96.92
0.163
0.02
1.000
0.033
6.32
0.604
150-180
1.53
0.03
0.109
0.190
0.380
105.7
0.171
0.01
1.000
0.019
6.42
0.600
BD stands for bulk density (g/cc), AD stands for Air dry (mm/mm), LL15 stands for wilting point at 15 bars (mm/mm), SAT stands for
saturated water content (mm/mm), DUL stands for drained upper limit (mm/mm), KS stands for saturated soil conductivity (mm/mm),
LL stands for lower limit (mm/mm); and PAWC are crop specific parameter and in this case, it’s for maize (mm/mm), LL stands for
maize lower limit (mm/mm), KL stands for maize water conductivity between soil layers (/day), XF stands for maize extinction
coefficient, pH depicts the soil pH, Carbon (total %) is the soil organic matter percentage
Table 5 Effect and interaction of different treatments on corn yield
Treatments
p-value
Planting date
<0.0001
Nitrogen
<0.0001
Irrigation rules
<0.0001
Location
<0.0001
Weather scenario
<0.0001
Planting date * Nitrogen
<0.0001
Planting date * Irrigation rules
>0.15
Proceedings of the 16th International Conference on Precision Agriculture
21-24 July, 2024, Manhattan, Kansas, United States
9
Nitrogen * Irrigation rules
<0.0001
Location * Weather scenario
<0.0001
Planting date * Location
<0.0001
Nitrogen * Location
<0.0001
Irrigation rules * Location
0.001
Planting date * Weather scenario
<0.0001
Nitrogen * Weather scenario
<0.0001
Irrigation rules * Weather scenario
>0.15
Planting date * Nitrogen * Irrigation rules
>0.15
Planting date * Nitrogen * Location
<0.0001
Planting date * Irrigation rules * Location
<0.0001
Nitrogen * Irrigation rules * Location
<0.0001
Planting date * Nitrogen * Weather scenario
<0.0001
Planting date * Irrigation rules * Weather scenario
0.04
Nitrogen * Irrigation rules * Weather scenario
>0.15
Planting date * Location * Weather scenario
0.002
Nitrogen * Location * Weather scenario
0.013
Irrigation rules * Location * Weather scenario
>0.15
Planting date * Nitrogen * Irrigation rules * Location
>0.15
Planting date * Nitrogen * Irrigation rules * Weather scenario
>0.15
Planting date * Nitrogen * Location * Weather scenario
>0.15
Planting date * Irrigation rules * Location * Weather scenario
>0.15
Nitrogen * Irrigation rules * Location * Weather scenario
>0.15
Planting date * Nitrogen * Irrigation rules * Location * Weather scenario
>0.15
It is to be noted that p-value < 0.0001 signifies that the variable had significant effect on response variable. A p-value between 0.0001
and 0.15 suggests that the variables might have significant effect on response variable under certain conditions and number of
replications. While the p-value > 0.15 signifies that there is no significant effect of variables on the response variables.
