Full Length Research Paper
ABSTRACT
Crop models known to be based on the theory of crop physiology for describing the dynamic process of crop growth are recently explored for their uncertainties in model application under resource limited conditions. The aim of this study was to test Environmental Policy Integrated Climate (EPIC), on upland land rice production by taking into account seasonal variability in Guinean and Guinean-Sudanian zones in Benin and Nigeria (West Africa). A range of data available under farmer or experimental conditions in rainfed agriculture were measured or used from literature. The results show the accuracy of the model to simulate LAI, total above ground biomass and grain yield of upland rice for 2 NERICA rice cultivars. After calibration, the model showed average mean relative error between 0.06 and 0.15 with the model efficiency up to 0.98% in the case of LAI. The assessment of the model performances about sensitivity to N or P fertilizer application is also discussed under Ultisols. Large root mean square (RMSE) in calibration and the validation (>100) process suggested that robustness of the model became restrictive under severe environmental conditions such as in drought or flooding condition. Performance of the model at large scale should be executed of with land marginality classification.
Key words: Environmental Policy Integrated Climate (EPIC), modeling, upland rice, West Africa.
INTRODUCTION
MATERIALS AND METHODS
RESULTS AND DISCUSSION
Model validation
The calibration of the EPIC model for upland rice was focused on nitrogen and phosphorus as main constraints to crop growth. The validation was carried out on three sites (Niaouli, Bohicon and Pingou) over two seasons. At Niaouli the experiment tested different levels of N and P input, at Bohicon NPK application was tested and Pingou was an on-farm field experiment (reference in Table 3).
The validation of the model showed that a relatively high gap between averages simulated and observed yield (Table 7). The mean error was 1.2 Mg ha-1 whereas the mean relative error was 3.0 Mg ha-1, which showed a very large overestimation of the simulated yields at plot level. The variation of the individual plots was also quite high resulting in root mean square error of more than 100%. The observed grain mean grain yield was lower than the average in the calibration suggesting various stress effects. Indeed some causes of rice failure attributed to floods and drought were reported b for NERICA evaluation on 5 locations with similar pedoclimatic conditions to these experiments in Benin republic (JAICAF, 2007). Therefore, before the use of the model in the assessment of impacts of and adaptations to climate variability and climate change in spatial studies, there is still a need for improvement in the amount and quality of available data collection.
Figure 5 showed a scatter plot of the observed and estimated value of sites used for model validation. The average yield in plots used for validation was relatively low, this is due to crop failure in 2006 in Niaouli where the average yield was below 1 Mgha-1 leading to the model overestimation. In fact, the experimental design was originally set up to evaluate the tolerance to drought with nutrients application for NERICA cultivar. Niaouli is located in the sub humid zone with bimodal rainfall pattern. The mid-season rainfall pattern associated with the sandy topsoil texture induced severe drought stress. The soil type “terre de barre” was described by Azontonde (1991) as soil with good physical hydraulic characteristics but with low water storage and their structure can be rapidly destroyed when there is no proper technique for maintaining organic matter.
The sensitivity of NERICA cultivar to water stress is well documented. Akinbile et al. (2007) showed that with NERICAs yield decreased under optimal satisfactory conditions almost linearly with evapotranspiration thus indicating that water application remained dominant factor at all the stages of production. In EPIC model, the potential harvest index is adjusted daily according to water stress suffered by the crop (Williams, 1995). During the calibration, the sensitivity of model was increased by setting the water stress impact (WSYF parameter), which allowed harvest index to drop to 0.01 in case of severe drought. The effect of water stress could be in fact limited to HI reduction. Fuji et al. (2004) reported that some Nerica lines showed high dry matter production under drought condition among other rice cultivars, and this have been correlated with stomata conductance (r=0.63**).
However, intensive rains of short duration followed by long dry spells that occurred during the flowering period which lead to increased sterility and decrease in grain weight (O´Toole and Moya, 1981). De Barros et al. (2005) observed slight overestimation of grain yield by the EPIC simulations was attributed to high rates of floral abortion caused by dry spells during the flowering periods since this factor is not considered in the model.
CONCLUSION
CONFLICT OF INTEREST
REFERENCES
Akinbile CO, Sangodoyin AY, Nwilene FE (2007). Growth and Yield Responses of Upland Rice (NERICA) under Different Water Regimes in Ibadan, Nigeria. J. Appl. Irrigat. Sci. 42:199-206. | ||||
Akintayo I, Cisse B, Zadji L (2008). Guide pratique de la culture des NERICA de plateau Centre du riz pour l'Afrique (ADRAO). Juillet 2008; P. 26. | ||||
Affholder F (2001). Modélisation de culture et diagnostic agronomique régional Mise en point d’une méthode au cas du mais chez les petits producteurs du Brésil central Thèse de doctorat s l’institut national agronomique Paris-Grigon | ||||
Asch F, Dingkuhn M, Sow A, Audebert A (2005). Drought-induced changes in rooting patterns and assimilate partitioning between root and shoot in upland rice. Field Crops Res. 93:223–236. Crossref |
||||
Atchade NS (2005). Caractérisation des stations pour la riziculture pluviale au sud-Bénin. Cas de IITA (Godomey), Niaouli et Bohicon. Master thesis. University of Abomey Clavi. P. 88. | ||||
Azontonde HA (1991). Propriétés physiques et hydrauliques des sols au Bénin. Soil Water Balance in the SudanoSahelian Zone. Proceedings of the Niamey Workshop, February 1991. IAHS Publ. P. 199. | ||||
Banziger M, Cooper M (2001). Breeding for low-input conditions and consequences for participatory plant breeding: examples from tropical maize and wheat. Euphytica 122:503–519. Crossref |
||||
de Barros I, Williams JR, Gaiser T (2005). Modeling soil nutrient limitations to crop production in semiarid NE of Brazil with a modified EPIC version II: Field test of the model. Ecol. Model. 181:567–580. | ||||
Becker M, Johnson DE (2001). Cropping intensity effects on upland rice yield and sustainability in West Africa. Nutr. Cycl. Agrosyst. 59:107-117. | ||||
Bouman BAM,Van Laar HH (2006). Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions. Agric. Syst. 87:249-273. Crossref |
||||
Chow TL, Rees HW, Monteith JO, Toner P, Lavoie J (2007). Effects of coarse fragment content on soil physical properties, soil erosion and potato production. Can. J. Soil Sci. 87:565–577. Crossref |
||||
Daroub SH, Gerakis A, Ritchie JT, Friesen DK, Ryan J (2004). Development of a soil-plant Phosphorus simulation model for calcareous and weathered tropical soils. Agric. Syst. 76:1157–1181. Crossref |
||||
Ekeleme F, Kamara AY, Oikeh SO, Omoigui LO, Amaza P, Chikoye D (2009). Response of upland rice cultivars to weed competition in the savannas of West Africa. Crop Prot. 28:90–96. Crossref |
||||
Enwezor WO, Udo EJ, Usoroh NJ, Ayotade KA, Adepeju JA, Chude VO, Udegbe CL (1989). Fertilizer use and management practices for crops in Nigeria (Series No. 2). Fertilizer Procurement and Distribution Division. Federal Ministry of Agriculture, Water Resources and Rural Development, Lagos, Nigeria. | ||||
FAO (2002) Local climate estimator, LocClim 1.0 CD-ROM. | ||||
Félix R (2006). Optimisation de l'irrigation et de la fertilisation azotée et la recherche d'une stratégie limitant la pollution des nappes souterraines par les nitrates. PhD thesis. Faculty of Agronomy, Gembloux | ||||
Fofana M (2008). Effets de la sécheresse sur les paramètres agronomiques et la qualité des grains du riz (Oryza sp.) Présentée en vue de l'Obtention du grade de Docteur de l'Université de Lomé. P. 161. | ||||
Fuji M, Andoh C, Ishihara S (2004) Drought resistance of Nerica (New Rice for Africa) compared with Oryza sativa L. and millet evaluates by stomatal conductance and soil water content. New direction on a diverse planet: Proceedings of the 4th International Crop Science Congress. Brisbane, Australia. ISBN 1 920842209 | ||||
Fujine K (2014). Elemental Analyzer (CHNS): User Guide Manual Information, International Ocean Discovery Program FlashEA 1112, P. 12. | ||||
Gaiser T, de Barros I, Sereke F, Lange FM (2010). Validation and reliability of the EPIC model to simulate maize production in small-holder farming systems in tropical sub-humid West Africa and semi-arid Brazil. Agriculture, Ecosyst. Environ.135:318–327 Crossref |
||||
Hargreaves GH, Samani ZA (1985). Reference crop evapotranspiration from temperature. Appl. Eng. Agric. 1:96–99. Crossref |
||||
Hartkamp AD, White JW, Hoogenboom G (1999). Interfacing geographic information system with agronomic modeling: a review. Agron. J. 91:761-77 Crossref |
||||
Heuberger H (1998). Nitrogen efficiency in tropical maize (Zea mays L.)- indirect selection criteria with special emphasis on morphological root characteristics. PhD thesis university of Hannover. P. 182. | ||||
Igué AM (2000). The use of a soil and terrain database for land evaluation procedures: Case study of Central Benin. Hohenheimer Bodenkundliche Hefte 58. University of Hohenheim. P. 235. | ||||
Izaurralde RC, Williams JR, McGill WB, Rosenberg NJ, Quiroga Jakas MC (2006). Simulating soil C dynamics with EPIC: Model description and testing against long-term data. Ecol. Model. 192: 362–384. Crossref |
||||
Jaicaf (2007). Summary of Jaicaf –Inrab, Cooperative NERICA Trials, the Wet Season, 2007, in Benin. Cotonou Benin P. 7. | ||||
Jones CA (1984). Estimation of percent aluminum saturation from soil chemical data. Commun. Soil Sci. Plant Anal. 15:327-33 Crossref |
||||
Kawano N, Ito O, Sakagami JI (2009). Flash flooding resistance of rice genotypes of Oryza sativa L., O. glaberrima Steud., and Interspecific hybridization progeny. Environ. Exp. Bot. 63:9–18. Crossref |
||||
Koné B, Ettien JB, Amadji G, Diatta S (2008). Caractérisation de la tolérance de NERICA à la sécheresse de mi-saison en riziculture pluviale. Afr. Crop Sci. J. 16:133-114. | ||||
Leenhardt D, Wallach D, Le Moigne P, Guérif, M, Bruand A, Casterad MA (2007). Using crop models for multiple fields. In: Wallach, D, Makowski D, Jones JW, working with dynamic crop models, evaluation, analysis, parameterization and applications. Elservier, UK. | ||||
Mandel NP, Sinha PK, Variar M, Shukla VD, Perraju P, Mehta A, Pathak AR, Dwivedi JL, Rathi SPS, Bhandarkar S, Singh BN, Singh DN, Panda S, Mishra NC, Singh YV, Pandya R, Singh MK, Sanger RBS, Bhatt JC, Sharma RK, Raman A, Kumar A, Atlin G (2010). Implications of genotype×input interactions in breeding superior genotypes for favorable and unfavorable rainfed upland environments. Field Crops Res. 118:135–144. Crossref |
||||
Niu X, Easterling W, Hays C J, Jacobs A, Mearns L (2009.) Reliability and input-data induced uncertainty of the EPIC model to estimate climate change impact on sorghum yields in the U.S. Great Plains. Agric. Ecosyst. Environ. 129:268–276. Crossref |
||||
Ogunremi LT, Lal R, Babalola O (1986). Effects of tillage and seeding methods on soil physical properties and yield of upland rice for an Ultisols in southeast Nigeria. Soil. Tillage Res. 6:305-324. | ||||
Oikeh SO, Nwilene F, Diatta S, Osiname O, Touré A, Okeleye KA (2008). Responses of upland NERICA rice to nitrogen and phosphorus in forest agroecosystems. Agron. J. 100:735-741. Crossref |
||||
Oikeh SO, Touré A, Sidibé B, Niang A, Semon M, Sokei Y, Mariko, M (2009). Responses of upland NERICA rice cultivars to nitrogen and plant density. Arch. Agron. Soil Sci. 55:301-314. Crossref |
||||
O’Toole JC, Moya TB (1981). Water deficits and yield in upland rice. Field Crops Res. 4:247-259. Crossref |
||||
Probert ME (2004). A Capability in APSIM to Model. Phosphorus Responses in Crops. CSIRO. | ||||
Rahimi S, Gholami Sefidkouhi MA, Raeini-Sarjaz M, Valipour M (2015). Estimation of actual evapotranspiration by using MODIS images (a case study: Tajan catchment). Arch. Agron. Soil Sci. 61(5):695–709. Crossref |
||||
Röhrig J (2008). Evaluation of agricultural land resources in Benin by regionalisation of the marginality index using satellite data. PhD thesis, University of Bonn. P. 174. | ||||
Sahrawat KL, Jones MP, Diatta S (1995). Response of upland rice to phosphorus in an Ultisol in humid forest zone, West Africa. Fertil. Res. 41:11-17. Crossref |
||||
Saito K Futakuchi K (2009). Performance of diverse upland cultivars in low and high soil fertility conditions in West Africa. Field Crops Res. 111:243-250. Crossref |
||||
Sharpley AN (1985). The selective erosion of plant nutrients in runoff. Soil Sci. Soc. Am. J. 49:1527–1534. Crossref |
||||
Sokei Y, Akintayo I, Doumbia Y, Gibba A, Keita S, Assigbe P (2010). Growth and yield performance of upland NERICA cultivars in West Africa . Japanese J. Crop Sci. 79:2–3. | ||||
Valipour M (2014a). Application of new mass transfer formulae for computation of evapotranspiration. J. Appl. Water Eng. Res. 2(1):33–46. Crossref |
||||
Valipour M. (2014b). Study of different climatic conditions to assess the role of solar radiation in reference crop evapotranspiration equations. Arch. Agron. Soil Sci. 61:5. | ||||
Valipour M (2014c). Evaluation of radiation methods to study potential evapotranspiration of 31 provinces. Meteorol. Atmos. Phys. 127(3):289-303. Crossref |
||||
Valipour M (2014d). Investigation of Valiantzas' evapotranspiration equation in Iran. Theor. Appl. Climatol. Crossref |
||||
Valipour M (2014e). Temperature analysis of reference evapotranspiration models. Meteorol. Appl. doi:10.1002/met.1465 Crossref |
||||
Valipour M (2014f). Evaluation of radiation methods to study potential evapotranspiration of 31 provinces. Meteorol. AtmosPhys. Crossref |
||||
Valipour M (2014g). Assessment of different equations to estimate potential evapotranspiration versus FAO Penman-Monteith method. Acta Advan. Agric. Sci. 2(11):14–27. | ||||
Valipour M (2014h). Use of average data of 181 synoptic stations for estimation of reference crop evapotranspiration by temperature based methods. Water Resour. Manage. 28(12):4237–4255. Crossref |
||||
Valipour M (2015). Importance of solar radiation, temperature, relative humidity, and wind speed for calculation of reference evapotranspiration. Arch. Agron. Soil Sci. 61(2):239–255. | ||||
Walkley A, Black IA (1934). An examination of the Degtjareff method for determining organic carbon in soils: Effect of variations in digestion conditions and of inorganic soil constituents Soil Sci. 63:251–263. Crossref |
||||
Williams JR, Jones CA, Dyke PT (1990). The EPIC Model. In: Williams, J.R. (Ed.), EPIC—Erosion Productivity Impact Calculator. 1. Model Documentation. U.S. Depart. Agric. Technol. Bull. 1768:3–86. | ||||
Williams, JR (1995). The EPIC model. In: V.P. Singh, Editor, Computer Models of Watershed Hydrology, Water Resources Publications, Highlands Ranch, Colorado. | ||||
Wisawapipat W, Kheoruenromne I, Suddhiprakarn, A, Gilkes, RJ, (2009). Phosphate sorption and desorption by Thai upland soils. Geoderma, 153:408-415. Crossref |
Copyright © 2025 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0