Hostname: page-component-848d4c4894-pftt2 Total loading time: 0 Render date: 2024-05-19T04:41:10.630Z Has data issue: false hasContentIssue false
  • English
  • Français

Identification of weak links in production technology for bridging the canola yield-gap in Punjab, Pakistan

Published online by Cambridge University Press:  22 February 2023

Shakeel Ahmad Open the ORCID record for Shakeel Ahmad [Opens in a new window] ,
Muhammad Ali Raza ,
Sajjad Hussain ,
Ghulam Abbas ,
Zartash Fatima ,
Mukhtar Ahmed ,
Muhammad Arif Goheer ,
Carol Jo Wilkerson ,
Axel Garcia y Garcia Open the ORCID record for Axel Garcia y Garcia [Opens in a new window]  and
Gerrit Hoogenboom Open the ORCID record for Gerrit Hoogenboom [Opens in a new window]
Shakeel Ahmad*
Affiliation:
Department of Agronomy, Bahauddin Zakariya University, Multan 60800, Pakistan
Muhammad Ali Raza
Affiliation:
The Islamia University of Bahawalpur, Bahawalpur, Pakistan Gansu Academy of Agricultural Sciences, Lanzhou, China
Sajjad Hussain
Affiliation:
Department of Agronomy, Bahauddin Zakariya University, Multan 60800, Pakistan
Ghulam Abbas
Affiliation:
Department of Agronomy, Bahauddin Zakariya University, Multan 60800, Pakistan The Islamia University of Bahawalpur, Bahawalpur, Pakistan
Zartash Fatima
Affiliation:
Department of Agronomy, Bahauddin Zakariya University, Multan 60800, Pakistan
Mukhtar Ahmed
Affiliation:
Department of Agronomy, Pir Meher Ali Shah, Arid Agriculture University Rawalpindi 46300, Pakistan
Muhammad Arif Goheer
Affiliation:
Global Change Impact Studies Centre, Islamabad, Pakistan
Carol Jo Wilkerson
Affiliation:
Independent Scholar, Gainesville, Florida 32614, USA
Axel Garcia y Garcia
Affiliation:
Department of Agronomy and Plant Genetics, University of Minnesota, Lamberton, MN 56152, USA
Gerrit Hoogenboom
Affiliation:
Department of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida, USA Global Food Systems Institute, University of Florida, Gainesville, Florida, USA
*
Author for correspondence: Shakeel Ahmad, E-mail: shakeelahmad@bzu.edu.pk
Get access Rights & Permissions [Opens in a new window]

Abstract

Understanding the reasons for the yield gap between potential and actual yield can provide insights for enhancing canola production by adapting measures for ensuring food security. The canola yield gap under different management practices (e.g. water, nitrogen, N- and sowing dates) was quantified using research trials that were conducted at on-station and historical data (1980–2016) and the CROPGRO-Canola model for Punjab, Pakistan. The integrated approach revealed that low inputs of N, the amount of irrigation, sowing date and the use of seeds from home stocks were the principal causes for a low yield. The CROPGRO-Canola model was able to simulate the canola yield from research trials (R2 = >0.90) and farm survey data (R2 = 0.63). The average yield gap between potential (YP), N-limited (YNL), water-limited (YWL), N- and water-limited (YNWL), and overall farmer field yield (YOFF) was 50, 46, 62 and 72%, respectively. The yield-gap with achievable yield (YA) for YNL, YWL, YNWL and YOFF was 34, 28, 49 and 63%, respectively. Overall, the results showed that a high canola yield for farmers’ fields can be obtained by selecting appropriate varieties and sowing dates with N rate of 120 kg/ha and efficient irrigation management. However, further studies are necessary to fully comprehend the underlying causes for the low actual yield and the high yield variability of farmers’ fields.

Keywords

Achievable yieldcrop simulationCSM-CROPGRO-Canolaoilseedpotential yield DSSAT
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 161 , Issue 2 , April 2023 , pp. 241 - 253
Check for updates
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abbas, G, Ahmad, S, Ahmad, A, Nasim, W, Fatima, Z, Hussain, S, Habib ur Rehman, M, Khan, MA, Hasanuzzaman, M, Fahad, S, Boote, KJ and Hoogenboom, G (2017) Quantification the impacts of climate change and crop management on phenology of maize-based cropping system in Punjab, Pakistan. Agriculture and Forest Meteorology 247, 42–55.CrossRefGoogle Scholar
Affholder, F, Poeydebat, C, Corbeels, M, Scopel, E and Tittonell, P (2013) The yield gap of major food crops in family agriculture in the tropics: assessment and analysis through field surveys and modelling. Field Crops Research 143, 106–118.CrossRefGoogle Scholar
Ahmad, S, Ali, H, Ismail, M, Shahzad, MI, Nadeem, M, Anjum, MA, Zia-Ul-Haq, M, Firdous, N and Khan, MA (2012) Radiation and nitrogen use efficiencies of C3 winter cereals to nitrogen split application. Pakistan Journal of Botany 44, 139–149.Google Scholar
Ahmad, S, Raza, I, Ali, H, Shahzad, AN, Atique-ur-Rehman, and Sarwar, N (2014) Response of cotton crop to exogenous application of glycinebetaine under sufficient and scarce water conditions. Brazilian Journal of Botany 37, 407–415.CrossRefGoogle Scholar
Ahmad, A, Ashfaq, M, Rasul, G, Wajid, SA, Khaliq, T, Rasul, F, Saeed, U, Habib ur Rahman, M, Hussain, J, Baig, IA, Naqvi, SAA, Bokhari, SAA, Ahmad, S, Naseem, W and Hoogenboom, G (2015) Impact of climate change on the rice-wheat cropping system of Pakistan. In Hillel, D and Rosenzweig, C (eds). Handbook of Climate Change and Agro-Ecosystems, Vol. 3. London: Imperial College Press and the American Society of Agronomy, pp. 219–258.CrossRefGoogle Scholar
Ahmad, S, Ali, H, Farooq, U, Khan, SU, Atique-ur-Rehman, , Sarwar, N, Shahzad, AN, Dogan, H, Hussain, S, Sultan, MT, Waheed, A, Zia-ul-Haq, M, Hussain, K and Khan, MA (2016 a) Improving nitrogen and radiation-use-efficiencies of C4 summer cereals by split nitrogen applications under irrigated arid environment. Turkish Journal of Agriculture and Forestry 40, 280–289.CrossRefGoogle Scholar
Ahmad, S, Nadeem, M, Abbas, G, Fatima, Z, Khan, RJZ, Ahmed, M, Ahmad, A, Rasul, G and Khan, MA (2016 b) Quantification of the effects of climate warming and crop management on sugarcane phenology. Climate Research 71, 47–61.CrossRefGoogle Scholar
Ahmad, S, Abbas, Q, Abbas, G, Fatima, Z, Atique-ur-Rehman, , Naz, S, Younis, H, Khan, RJ, Nasim, W, Habib ur Rehman, M, Ahmad, A, Rasul, G, Khan, MA and Hasanuzzaman, M (2017 a) Quantification of climate warming and crop management impacts on cotton phenology. Plants 6, 1–16.CrossRefGoogle ScholarPubMed
Ahmad, S, Abbas, G, Fatima, Z, Khan, RJ, Anjum, MA, Ahmed, M, Khan, MA, Porter, CH and Hoogenboom, G (2017 b) Quantification of the impacts of climate warming and crop management on canola phenology in Punjab, Pakistan. Journal of Agronomy and Crop Science 203, 442–452.CrossRefGoogle Scholar
Ali, MH and Ullah, MJ (1995) Effect of different levels and timing of nitrogen application on growth and yield of rapeseed. Annals of Bangladesh Agriculture 5, 115–120.Google Scholar
Anderson, W, Johansen, C and Siddique, KHM (2016) Addressing the yield gap in rainfed crops: a review. Agronomy for Sustainable Development 36, 18.CrossRefGoogle Scholar
Bhatia, VS, Singh, P, Wani, SP, Chauhan, GS, Kesava Rao, AVR, Mishra, AK and Srinivas, K (2008) Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-soybean model. Agricultural and Forest Meteorology 148, 1252–1265.CrossRefGoogle Scholar
Bindraban, PS, Stoorvogel, JJ, Jansen, DM, Vlaming, J and Groot, JJR (2000) Land quality indicators for sustainable land management: proposed method for yield gap and soil nutrient balance. Agriculture, Ecosystem and Environment 81, 103–112.CrossRefGoogle Scholar
Boote, KJ, Jones, JW and Pickering, NB (1996) Potential uses and limitations of crop models. Agronomy Journal 88, 704–716.CrossRefGoogle Scholar
Bouman, BAM and van Laar, HH (2006) Description, evaluation of the rice growth model ORYZA 2000 under nitrogen-limited conditions. Agricultural Systems 87, 249–273.CrossRefGoogle Scholar
Deligios, PA, Farci, R, Sulas, L, Hoogenboom, G and Ledda, L (2013) Predicting growth and yield of winter rapeseed in a Mediterranean environment: model adaptation at a field scale. Field Crops Research 144, 100–112.CrossRefGoogle Scholar
Dias, HB and Sentelhas, PC (2018) Sugarcane yield gap analysis in Brazil – a multi-model approach for determining magnitudes and causes. Science of Total Environment 637–638, 1127–1136.CrossRefGoogle Scholar
Dogan, E, Copur, O, Kahraman, A, Kirnak, H and Guldur, ME (2011) Supplemental irrigation effect on canola yield components under semiarid climatic conditions. Agricultural Water Management 98, 1403–1408.CrossRefGoogle Scholar
Faramarzi, A, Barzegar, A, Zolleh, HH, Mohammadi, H, Ardakani, MR and Normohammadi, G (2009 a) Response of canola (Brassica napus cultivars to rate and split application of nitrogen fertilizer. Australian Journal of Basic and Applied Sciences 3, 2030–2037.Google Scholar
Faramarzi, M, Abbaspour, KC, Schulin, R and Yang, H (2009 b) Modelling blue and green water resources availability in Iran. Hydrological Processes 23, 486–501.CrossRefGoogle Scholar
Farre, I, Robertson, MJ, Walton, GH and Asseng, S (2002) Simulating phenology and yield response of canola to sowing date in Western Australia using the APSIM model. Australian Journal of Agricultural Research 53, 1155–1164.CrossRefGoogle Scholar
Fatima, Z, Abbas, Q, Khan, A, Hussain, S, Ali, MA, Abbas, G, Younis, H, Naz, S, Ismail, M, Shahzad, MI, Nadeem, M, Farooq, U, Khan, SU, Javed, K, Khan, AA, Ahmed, M, Khan, MA and Ahmad, S (2018) Resource use efficiencies of C3 and C4 cereals under split nitrogen regimes. Agronomy 8, 69.CrossRefGoogle Scholar
Fermont, AM, van Asten, PJA, Tittonell, P, van Wijk, MT and Giller, KE (2009) Closing the cassava yield gap: an analysis from smallholder farms in East Africa. Field Crops Research 112, 24–36.CrossRefGoogle Scholar
Government of Pakistan (2018) Economic Survey of Pakistan, 2017–18, Economic Advisory Wing, Finance Division, Govt. of Pakistan, Islamabad, pp. 13–32.Google Scholar
Grant, CA, Wu, R, Selles, F, Harker, KN, Clayton, GW, Bittman, S, Zebarth, BJ and Lupwayi, NZ (2012) Crop yield and nitrogen concentration with controlled release urea and split application of nitrogen as compared to non-coated urea applied at seeding. Field Crops Research 127, 170–180.CrossRefGoogle Scholar
Hoogenboom, G, Porter, CH, Boote, KJ, Shelia, V, Wilkens, PW, Singh, U, White, JW, Asseng, S, Lizaso, JI, Moreno, LP, Pavan, W, Ogoshi, R, Hunt, LA, Tsuji, GY and Jones, JW (2019 a) The DSSAT crop modeling ecosystem. In Boote, KJ (ed.), Advances in Crop Modeling for a Sustainable Agriculture. Cambridge, United Kingdom: Burleigh Dodds Science Publishing, pp. 173–216. http://dx.doi.org/10.19103/AS.2019.0061.10.CrossRefGoogle Scholar
Hoogenboom, G, Porter, CH, Shelia, V, Boote, KJ, Singh, U, White, JW, Hunt, LA, Ogoshi, R, Lizaso, JI, Koo, J, Asseng, S, Singels, A, Moreno, LP and Jones, JW (2019 b) Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.7.5 (https://DSSAT.net). DSSAT Foundation, Gainesville, Florida, USA.Google Scholar
Istanbulluoglu, A, Arslan, B, Gocmen, E, Gezer, E and Pasa, C (2010) Effects of deficit irrigation regimes on the yield and growth of oilseed rape (Brassica napusL). Biosystems Engineering 105, 388–394.CrossRefGoogle Scholar
Jing, Q, Shang, J, Qian, B, Hoogenboom, G, Huffman, T, Liu, J, Ma, B, Geng, X, Jiao, X, Kovacs, J and Walters, D (2016) Evaluation of the CSM-CROPGRO-Canola model for simulating canola growth and yield at west Nipissing in Eastern Canada. Agronomy Journal 108, 1–10.CrossRefGoogle Scholar
Johnston, AM, Tanaka, DL, Miller, PR, Brandt, SA, Nielsen, DC, Lafond, GP and Rieveland, NR (2002) Oilseed crops for semiarid cropping systems in the Northern Great Plains. Agronomy Journal 94, 231–241.CrossRefGoogle Scholar
Jones, JW, Hoogenboom, G, Porter, CH, Boote, KJ, Batchelor, WD, Hunt, LA, Wilkens, PW, Singh, U, Gijsman, AJ and Ritchie, JT (2003) DSSAT cropping system model. European Journal of Agronomy 18, 235–265.CrossRefGoogle Scholar
Loague, K and Green, RE (1991) Statistical and graphical methods for evaluating solute transport models: overview and application. Journal of Contaminant Hydrology 7, 51–73.CrossRefGoogle Scholar
Lobell, DB, Cassman, KG and Field, CB (2009) Crop yield gaps: their importance, magnitudes, and causes. Annual Review of Environmental Resources 34, 179–204.CrossRefGoogle Scholar
Mahmood, T, Ejaz-ul-Hasan, , Ali, M and Hussain, M (2012) Faisal canola: a new high yielding canola variety for general cultivation in Punjab. Journal of Agricultural Research 50, 321–328.Google Scholar
Mourice, SK, Rweyemamu, CL, Tumbo, SD and Amuri, N (2014) Maize cultivars specific parameters for decision support system for agrotechnology transfer (DSSAT) application in Tanzania. American Journal of Plant Sciences 5, 821–833.CrossRefGoogle Scholar
Noreen, S, Noor, S, Ahmad, S, Bibi, F and Hasanuzzaman, M (2016) Quantifying some physiological and productivity indices of canola (Brassica napus L.) crop under an arid environment. Notulae Botanicae Horti Agrobotanici 44, 272–279.CrossRefGoogle Scholar
Ozer, H (2003) Sowing date and nitrogen rate effects on growth, yield and yield components of two summer rapeseed cultivars. European Journal of Agronomy 19, 453–463.CrossRefGoogle Scholar
Poulton, PL, Vesna, T, Dalgliesh, NP and Seng, V (2015) Applying simulation to improve rice varieties in reducing the on-farm yield gap in Cambodian lowland rice ecosystems. Experimental Agriculture 51, 264–284.CrossRefGoogle Scholar
Probert, ME, Keating, BA, Thompson, JP and Parton, WJ (1995) Modelling water, nitrogen, and crop yield for a long-term fallow management experiment. Australian Journal of Experimental Agriculture 35, 941–950.CrossRefGoogle Scholar
Qian, B, Jing, Q, Bélanger, G, Shang, J, Huffman, T, Liu, J and Hoogenboom, G (2018) Simulated canola yield responses to climate change and adaptation in Canada. Agronomy Journal 110, 133–146.CrossRefGoogle Scholar
Rathke, GW and Schuster, C (2001) Yield and quality of winter oilseed rape related to nitrogen supply. In Horst, WJ, Schenk, MK, Burkert, A, Claassen, N, Flessa, H, Frommer, WB, Goldbach, H, Olfs, HW, Romheld, V, Sattelmacher, B, Schmidhalter, U, Schubert, S, Wiren, N and Wittenmayer, L (eds). Plant Nutrition: Food Security and Sustainability of Agro-Ecosystems through Basic and Applied Research. Dordrecht: Kluwer Academic Publishers, pp. 798–799.CrossRefGoogle Scholar
Robertson, MJ and Lilley, JM (2016) Simulation of growth, development and yield of canola (Brassica napus) in APSIM. Crop and Pasture Science 67, 332–344.CrossRefGoogle Scholar
Robertson, M, Kirkegaard, J, Peake, A, Creelman, Z, Bell, L, Lilley, J, Midwood, J, Zhang, H, Kleven, S, Duff, C, Lawes, R and Riffkin, P (2016) Trends in grain production and yield gaps in the high-rainfall zone of southern Australia. Crop and Pasture Science 67, 921–937.CrossRefGoogle Scholar
Schulthess, U, Timsina, J, Herrera, JM and McDonald, A (2013) Mapping field-scale yield gaps for maize: an example from Bangladesh. Field Crops Research 143, 151–156.CrossRefGoogle Scholar
Steel, RGD, Torrie, JH and Dickey, DA (1997) Principles and Procedures of Statistics: A Biometrical Approach, 3rd Edn. New York, NY, USA: McGraw-Hill.Google Scholar
Tamagno, LN, Chamorro, AM and Sarandon, SJ (1999) Split nitrogen application on rapeseed; effects on seed yield and seed quality. Revista de la Facultad de Agronomical La Plata 104, 25–34.Google Scholar
Tariq, M, Ahmad, S, Fahad, S, Abbas, G, Hussain, S, Fatima, Z, Nasim, W, Mubeen, M, Habib ur Rehman, M, Khan, MA, Adnan, M, Wilkerson, CJ and Hoogenboom, G (2018) The impact of climate warming and crop management on phenology of sunflower-based cropping systems in Punjab, Pakistan. Agricultural and Forest Meteorology 256–257, 270–282.CrossRefGoogle Scholar
Thornton, PK and Hoogenboom, G (1994) A computer program to analyze single-season crop model outputs. Agronomy Journal 86, 860–868.CrossRefGoogle Scholar
Thornton, PK, Hoogenboom, G, Wilkens, PW and Bowen, WT (1995) A computer program to analyze multiple-season crop model outputs. Agronomy Journal 87, 131–136.CrossRefGoogle Scholar
Thornton, PK, Hoogenboom, G, Wilkens, PW and Jones, JW (1998) Seasonal analysis. In Tsuji, GY, Uehara, G and Balas, S (eds), DSSAT version 3, Vol. 3–2. Honolulu: University of Hawaii, pp. 1–65.Google Scholar
Timsina, J and Humphreys, E (2006) Performance of CERES-rice and CERES-wheat models in rice–wheat systems: a review. Agricultural Systems 90, 5–31.CrossRefGoogle Scholar
Tsuji, GY, Hoogenboom, G and Thornton, PK (1998) Understanding Options for Agricultural Production. Systems approaches for sustainable agricultural development. Dordrecht: Kluwer Academic.CrossRefGoogle Scholar
van Ittersum, MK, Cassman, KG, Grassini, P, Wolf, J, Tittonell, P and Hochman, Z (2013) Yield gap analysis with local to global relevance – a review. Field Crops Research 143, 4–17.CrossRefGoogle Scholar
Wallach, D and Goffinet, B (1987) Mean squared error of prediction in models for studying ecological and agronomic systems. Biometrics 43, 561–573.CrossRefGoogle Scholar
Willmott, CJ (1981) On the validation of models. Physical Geography 2, 184–194.CrossRefGoogle Scholar
Yanga, JM, Yangb, JY, Liub, S and Hoogenboom, G (2014) An evaluation of the statistical methods for testing the performance of crop models with observed data. Agricultural Systems 127, 81–89.CrossRefGoogle Scholar
Zu, Q, Mi, C, Liu, DL, He, L, Kuang, Z, Fang, Q, Ramp, D, Li, L, Wang, B, Chen, Y, Li, J, Jin, N and Yu, Q (2018) Spatio-temporal distribution of sugarcane potential yields and yield gaps in Southern China. European Journal of Agronomy 92, 72–83.CrossRefGoogle Scholar
Supplementary material: File

Ahmad et al. supplementary material

Ahmad et al. supplementary material

Download Ahmad et al. supplementary material(File)
File 1 MB