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Identifying key environmental drivers of chickpea yield and water-use efficiency: a statistical modelling approach

Published online by Cambridge University Press:  10 September 2025

Muhuddin Rajin Anwar Open the ORCID record for Muhuddin Rajin Anwar [Opens in a new window] ,
David John Luckett ,
Ryan H. L. Ip ,
Yashvir Chauhan ,
Neroli Graham ,
Rosy Raman ,
Mark F. Richards ,
Jens Berger  and
Maheswaran Rohan
Muhuddin Rajin Anwar*
Affiliation:
NSW Department of Primary Industries and Regional Development, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia Gulbali Institute (Agriculture, Water and Environment), Charles Sturt University, Locked Bag 588, Wagga Wagga, NSW 2678, Australia
David John Luckett
Affiliation:
Gulbali Institute (Agriculture, Water and Environment), Charles Sturt University, Locked Bag 588, Wagga Wagga, NSW 2678, Australia
Ryan H. L. Ip
Affiliation:
Department of Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, NSW, Australia
Yashvir Chauhan
Affiliation:
Queensland Department of Primary Industries Research Station, Kingaroy, Qld 4610, Australia
Neroli Graham
Affiliation:
NSW Department of Primary Industries and Regional Development, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia
Rosy Raman
Affiliation:
NSW Department of Primary Industries and Regional Development, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia
Mark F. Richards
Affiliation:
NSW Department of Primary Industries and Regional Development, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia
Jens Berger
Affiliation:
CSIRO Agriculture and Food, PMB5, Wembley, WA 6913, Australia
Maheswaran Rohan
Affiliation:
NSW Department of Primary Industries and Regional Development, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia
*
Corresponding author: Muhuddin Rajin Anwar; Emails: muhuddin.anwar@dpi.nsw.gov.au and 1anwar191962@gmail.com
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Abstract

Chickpea (Cicer arietinum L.) is a vital legume crop with significant global importance, yet its productivity is highly sensitive to environmental variability. This study employed advanced statistical modelling to identify key environmental drivers of chickpea yield and water-use efficiency (WUE). Field trial data from 29 experiments across 10 Australian locations were analysed, focusing on 19 climatic variables across four growth stages: sowing to flowering, flowering to podding, podding to maturity and the critical period around flowering. Using correlation analysis and Exclusive LASSO regression, the study quantified relationships between environmental factors, growth stages and chickpea performance metrics. Key findings identified soil evaporation and soil moisture supply-demand ratio during the sowing-to-flowering stage, along with frost during the critical period, as significant determinants of yield. Frost negatively impacted WUE across multiple growth stages, while mean photothermal quotient during early growth positively influenced transpiration-based WUE. Predictive models developed using daily climate data demonstrated strong performance (R2 > 0.68–0.72) for yield and WUE predictions. The study provides actionable insights for optimising chickpea production under varying environmental conditions, offering practical tools for farmers and agronomists to enhance crop management strategies, supporting sustainable and profitable chickpea farming in Australia and beyond.

Keywords

Abiotic stresscrop growth stagesCicer arietinum L.exclusive LASSO predictionshrinkage estimator

Information

Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 163 , Issue 5 , October 2025 , pp. 532 - 546
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Copyright
© The Author(s), 2025. Published by Cambridge University Press

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References

ABARES (2024) Australian crop report. Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra. https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1036803/2 Google Scholar
Ainsworth, EA and Rogers, A (2007) The response of photosynthesis and stomatal conductance to rising [CO2]: Mechanisms and environmental interactions. Plant, Cell & Environment 30, 258–270. https://doi.org/10.1111/j.1365-3040.2007.01641.x CrossRefGoogle ScholarPubMed
Akumaga, U and Alderman, PD (2019) Comparison of Penman–Monteith and Priestley–Taylor evapotranspiration methods for crop modelling in Oklahoma. Agronomy Journal 111, 1171–1180. https://doi.org/10.2134/agronj2018.10.0694 CrossRefGoogle Scholar
Allen, DJ and Ort, DR (2001) Impacts of chilling temperatures on photosynthesis in warm-climate plants. Trends in Plant Science 6, 36–42. https://doi.org/10.1016/S1360-1385(00)01808-2 CrossRefGoogle ScholarPubMed
Anwar, MR, Emebiri, L, Ip, RHL, Luckett, DJ, Chauhan, YS and Zeleke, KT (2024) Least absolute shrinkage and selection operator regression used to select important features when predicting wheat yield from various genotype groups. The Journal of Agricultural Science 162, 245–259. https://doi.org/10.1017/S0021859624000479 CrossRefGoogle Scholar
Anwar, MR, Luckett, DJ, Chauhan, YS, Ip, RHL, Maphosa, L, Simpson, M, Warren, A, Raman, R, Richards, MF, Pengilley, G, Hobson, K and Graham, N (2022) Modelling the effects of cold temperature during the reproductive stage on the yield of chickpea (Cicer arietinum L). International Journal of Biometeorology 66, 111–125. https://doi.org/10.1007/s00484-021-02197-8 CrossRefGoogle ScholarPubMed
Aphalo, PJ (2024) ggpmisc: Miscellaneous extensions to “ggplot2”. https://cran.r-project.org/package=ggpmisc Google Scholar
APSIM (2023) SoilWat. Available online from: https://www.apsim.info/documentation/model-documentation/soil-modules-documentation/soilwat/ (Accessed 4 February 2025).Google Scholar
Aroca, R and Ruiz-Lozano, JM (2012) Regulation of root water uptake under drought stress conditions. In Aroca, R (ed.), Plant Responses to Drought Stress. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32653-0_4 CrossRefGoogle Scholar
Bakin, S (1999) Adaptive regression and model selection in data mining problems. Ph.D. thesis. Australian National Univ. Canberra. https://doi.org/10.25911/5d78db4c25dbb CrossRefGoogle Scholar
Barlow, KM, Christy, BP, O’Leary, GJ, Riffkin, PA and Nuttall, JG (2015) Simulating the impact of extreme heat and frost events on wheat crop production: A review. Field Crops Research 171, 109–119. https://doi.org/10.1016/j.fcr.2014.11.010 CrossRefGoogle Scholar
Berger, JD, Ali, M, Basu, PS, Chaudhary, BD, Chaturvedi, SK, Deshmukh, PS, Dharmaraj, PS, Dwivedi, SK, Gangadhar, GC, Gaur, PM and Kumar, J (2006) Genotype by environment studies demonstrate the critical role of phenology in adaptation of chickpea (Cicer arietinum L.) to high and low yielding environments of India. Field Crops Research 98, 230–244. https://doi.org/10.1016/j.fcr.2006.02.007 CrossRefGoogle Scholar
Bicard, M, Faucon, M, Pedas, PR, Vequaud, D, Pin, PA, Elmerich, C and Lange, B (2025) Unravelling critical climatic factors and phenological stages impacting spring barley yields across Europe. Field Crops Research 321, 109665. https://doi.org/10.1016/j.fcr.2024.109665 CrossRefGoogle Scholar
Breheny, P and Huang, J (2009) Penalized methods for bi-level variable selection. Statistics and Its Interface 2, 369–380. https://doi.org/10.4310/sii.2009.v2.n3.a10 CrossRefGoogle ScholarPubMed
Campbell, F and Allen, AI (2017) Within group variable selection through the Exclusive Lasso. Electronic Journal of Statistics 11, 4220–4257. https://doi.org/10.1214/17-EJS1317 CrossRefGoogle Scholar
Challinor, AJ, Watson, J, Lobell, D, Howden, SM, Smith, DR and Chhetri, N (2014) A meta-analysis of crop yield under climate change and adaptation. Nature Climate Change 4, 287–291. https://doi.org/10.1038/nclimate2153 CrossRefGoogle Scholar
Chauhan, YS, Anwar, MR, Richards, MF, Lake, L, Sadras, VO, Luckett, DJ, Raman, R, Krosch, S and Graham, N (2023). Effect of soil water on flowering and pod-set in chickpea: implications for modelling and managing frost and heat stress. Agronomy for Sustainable Development 43, 49. https://doi.org/10.1007/s13593-023-00903-x CrossRefGoogle Scholar
Chauhan, YS and Ryan, M (2020) Frost risk management in chickpea using a modelling approach. Agronomy 10(4), 460. https://doi.org/10.3390/agronomy10040460 CrossRefGoogle Scholar
Chaves, MM, Flexas, J and Pinheiro, C (2009) Photosynthesis under drought and salt stress: regulation mechanisms from whole plant to cell. Annals of Botany 103, 551–560. https://doi.org/10.1093/aob/mcn125 CrossRefGoogle ScholarPubMed
Chenu, K, Cooper, M, Hammer, GL, Mathews, KL, Dreccer, MF and Chapman, SC (2011) Environment characterization as an aid to wheat improvement: interpreting genotype–environment interactions by modelling water-deficit patterns in North-Eastern Australia. Journal of Experimental Botany 62, 1743–1755. https://doi.org/10.1093/jxb/erq459 CrossRefGoogle Scholar
Ching, T (2024) qs: Quick serialization of r objects. Available online from: https://cran.r-project.org/package=qs CrossRefGoogle Scholar
Clarke, HJ and Siddique, KHM (2004) Response of chickpea genotypes to low temperature stress during reproductive development. Field Crops Research 90, 323–334. https://doi.org/10.1016/j.fcr.2004.04.001 CrossRefGoogle Scholar
Comas, LH, Becker, SR, Cruz, VMV, Byrne, PF and Dierig, DA (2013) Root traits contributing to plant productivity under drought. Frontiers in Plant Science 4, 442. https://doi.org/10.3389/fpls.2013.00442 Google ScholarPubMed
Croser, JS, Clarke, HJ, Siddique, KHM and Khan, TN (2003) Low-temperature stress: implications for chickpea (Cicer arietinum L.) improvement. Critical Reviews in Plant Sciences 22, 185–219. https://doi.org/10.1080/713610855 CrossRefGoogle Scholar
Csárdi, G, Hester, J, Wickham, H, Chang, W, Morgan, M and Tenenbaum, D (2024) remotes: R package installation from remote repositories, including “GitHub”. Available online from: https://cran.r-project.org/package=remotes Google Scholar
Dancho, M (2020) correlationfunnel: Speed up exploratory data analysis (EDA) with the correlation funnel. Available online from: https://cran.r-project.org/package=correlationfunnel CrossRefGoogle Scholar
de Souza, AA, de Carvalho, AJ, Bastos, EA, Cardoso, MJ, Júlio, MPM, Batista, PSC, Julio, BHM, Campolina, CV, Portugal, AF, de Menezes, CB and de Oliveira, SM (2021) Grain sorghum under pre-and post-flowering drought stress in a semiarid environment. Australian Journal of Crop Science 15, 1139–1145. https://doi.org/10.21475/ajcs.21.15.08.p3162 CrossRefGoogle Scholar
Devasirvatham, V and Tan, DKY (2018) Impact of high temperature and drought stresses on chickpea production. Agronomy 8, 145. https://doi.org/10.3390/agronomy8080145 CrossRefGoogle Scholar
Devasirvatham, V, Tan, DKY, Gaur, PM, Raju, TN and Trethowan, RM (2012) High temperature tolerance in chickpea and its implications for plant improvement. Crop and Pasture Science 63, 419–428. https://doi.org/10.1071/CP11218 CrossRefGoogle Scholar
Dreccer, MF, Fainges, J, Whish, J, Ogbonnaya, FC and Sadras, VO (2018) Comparison of sensitive stages of wheat, barley, canola, chickpea and field pea to temperature and water stress across Australia. Agricultural and Forest Meteorology 248, 275–294. http://dx.doi.org/10.1016/j.agrformet.2017.10.006 CrossRefGoogle Scholar
Dreccer, MF, Whish, J, Delahunty, A, Richards, M, Graham, N, Power, S, Clancy, l, Rodriguez, D, De Voil, P and Munford, M (2024) Water use and water use efficiency in chickpea during key stages of yield formation. In Lawes R, Flower K, Michael P, Mwenda G, Singh B (eds.), Adaptive Agronomy for a Resilient Future. Proceedings of the 21st Australian Society of Agronomy Conference, 21-24 October 2024, Albany, WA. https://www.agronomyaustraliaproceedings.org/images/sampledata/2024/Dreccer_etal_ASA__2024_revised-205-740-Dreccer-Fernanda.pdf Google Scholar
Ekstrøm, CT (2023) MESS: Miscellaneous esoteric statistical scripts. Available online from: https://cran.r-project.org/package=MESS Google Scholar
FAOSTAT (2025) Food and Agriculture Organization of the United Nations. Available online from: https://www.fao.org/faostat/en/#data/QCL (Accessed 11 February 2025).Google Scholar
Farooq, M, Gogoi, N, Barthakur, S, Baroowa, B, Bharadwaj, N, Alghamdi, SS and Siddique, KHM (2017) Drought stress in grain legumes during reproduction and grain filling. Journal of Agronomy and Crop Science 203, 81–102. https://doi.org/10.1111/jac.12169 Google Scholar
Firke, S (2023) janitor: Simple tools for examining and cleaning dirty data. Available online from: https://cran.r-project.org/package=janitor Google Scholar
Fischer, RA (1985) Number of grains in wheat crops and the influence of solar radiation and temperature. Journal of Agricultural Science 105, 447–461. https://doi.org/10.1017/S0021859600056495 CrossRefGoogle Scholar
Flexas, J, Díaz-Espejo, A, Conesa, MA, Coopman, RE, Douthe, C, Gago, J, Gallé, A, Galmés, J, Medrano, H, Ribas-Carbo, M and Tomàs, M (2016) Mesophyll conductance to CO2 and Rubisco as targets for improving intrinsic water use efficiency in C3 plants. Plant, Cell & Environment 39, 965–982. https://doi.org/10.1111/pce.12622 CrossRefGoogle ScholarPubMed
Foulkes, MJ, Sylvester-Bradley, R, Weightman, R and Snape, JW (2007) Identifying physiological traits associated with improved drought resistance in winter wheat. Field crops research 103, 11–24. https://doi.org/10.1016/j.fcr.2007.04.007 CrossRefGoogle Scholar
Friedman, JH, Hastie, T and Tibshirani, R (2010) Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33, 1–22. https://doi.org/10.18637/jss.v033.i01 CrossRefGoogle ScholarPubMed
Gallagher, JN, Biscoe, PV and Dennis-Jones, R (1983) Environmental influences on the development, growth and yield of barley. In Wright, GM, Wynn-Williams, RB (eds.), Barley: Production and Marketing. Agronomy Society of New Zealand Special Publication No. 2. pp. 21–49. https://www.agronomysociety.org.nz/files/SP2_3._Environmental_influences_on_barley.pdf Google Scholar
Garg, R, Shankar, R, Thakkar, B, Kudapa, H, Krishnamurthy, L, Mantri, N, Varshney, RK, Bhatia, S and Jain, M (2016) Transcriptome analyses reveal genotype-and developmental stage-specific molecular responses to drought and salinity stresses in chickpea. Scientific Reports 6, 19228. https://doi.org/10.1038/srep19228 CrossRefGoogle ScholarPubMed
Graham, N, Raman, R, Warren, A and Anwar, M (2022) Timing of flowering and pod initiation influences yield potential in chickpeas. GRDC Grains Research Online Update paper, 25 February 2022. Available online from: https://www.icanrural.com.au/documents/Northern%20GRDC%20Grains%20Research%20Updates%20online%202022%20week%202.pdf#page=109 (Accessed 5 February 2025)Google Scholar
GRDC (2011) Choosing rotation crops. GRDC Fact Sheet. Available online from: https://grdc.com.au/__data/assets/pdf_file/0024/223683/grdcfsbreakcropsnorthpdf.pdf.pdf Google Scholar
GRDC (2016) Managing frost risk. Grain Research & Development Corporation, National Frost Initiative. Available online from: https://grdc.com.au/__data/assets/pdf_file/0027/208674/grdc-managing-frost-risk-tips-and-tactics-frost-050216-northen-southern-and-western-region.pdf.pdf Google Scholar
Hatfield, JL, Boote, KJ, Kimball, BA, Ziska, LH, Izaurralde, RC, Ort, D, Thomson, AM and Wolfe, D (2011) Climate impacts on agriculture: implications for crop production. Agronomy Journal 103, 351–370. https://doi.org/10.2134/agronj2010.0303 CrossRefGoogle Scholar
Hatfield, JL and Prueger, JH (2015) Temperature extremes: effect on plant growth and development. Weather and Climate Extremes 10, 4–10. https://doi.org/10.1016/j.wace.2015.08.001 CrossRefGoogle Scholar
Hatfield, JL, Sauer, TJ and Prueger, JH (2001) Managing soils to achieve greater water use efficiency: a review. Agronomy Journal 93, 271–280. https://doi.org/10.2134/agronj2001.932271x CrossRefGoogle Scholar
He, D and Wang, E (2019) On the relation between soil water holding capacity and dryland crop productivity. Geoderma 353, 11–24. https://doi.org/10.1016/j.geoderma.2019.06.022 CrossRefGoogle Scholar
Heilemann, J, Klassert, C, Samaniego, L, Thober, S, Marx, A, Boeing, F, Klauer, B and Gawel, E (2024) Projecting impacts of extreme weather events on crop yields using LASSO regression. Weather and Climate Extremes 46, 100738. https://doi.org/10.1016/j.wace.2024.100738 CrossRefGoogle Scholar
Holzworth, DP, Huth, NI, deVoil, PG, Zurcher, EJ, Herrmann, NI, McLean, G, Chenu, K, van Oosterom, EJ, Snow, V, Murphy, C, Moore, AD, Brown, H, Whish, JPM, Verrall, S, Fainges, J, Bell, LW, Peake, AS, Poulton, PL, Hochman, Z, Thorburn, PJ, Gaydon, DS, Dalgliesh, NP, Rodriguez, D, Cox, H, Chapman, S, Doherty, A, Teixeira, E, Sharp, J, Cichota, R, Vogeler, I, Li, FY, Wang, E, Hammer, GL, Robertson, MJ, Dimes, JP, Whitbread, AM, Hunt, J, van Rees, H, McClelland, T, Carberry, PS, Hargreaves, JNG, MacLeod, N, McDonald, C, Harsdorf, J, Wedgwood, S and Keating, BA (2014) APSIM – evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software 62, 327–350. https://doi.org/10.1016/j.envsoft.2014.07.009 CrossRefGoogle Scholar
Huang, J, Breheny, P and Ma, S (2012) A selective review of group selection in high-dimensional models. Statistical Science 27, 481–499. https://doi.org/10.1214/12-STS392 CrossRefGoogle ScholarPubMed
Huang, J, Ma, S, Xie, H and Zhang, C (2009) A group bridge approach for variable selection. Biometrika 96, 339–355. https://doi.org/10.1093/biomet/asp020 CrossRefGoogle ScholarPubMed
Iannone, R, Cheng, J, Schloerke, B, Hughes, E, Lauer, A, Seo, J, Brevoort, K and Roy, O (2024) gt: Easily create presentation-ready display tables. Available online from: https://cran.r-project.org/package=gt Google Scholar
Jeffrey, SJ, Carter, JO, Moodie, KB and Beswick, AR (2001) Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environmental Modelling Software 16, 309–330. https://doi.org/10.1016/S1364-8152(01)00008-1 CrossRefGoogle Scholar
Joshi, PK and Rao, PP (2017) Global pulses scenario: status and outlook. Annals of the New York Academy of Sciences 1392, 6–17. https://doi.org/10.1111/nyas.13298 CrossRefGoogle Scholar
Kashiwagi, J, Krishnamurthy, L, Crouch, JH and Serraj, R (2006) Variability of root length density and its contributions to seed yield in chickpea (Cicer arietinum L.) under terminal drought stress. Field Crops Research 95, 171–181. https://doi.org/10.1016/j.fcr.2005.02.012 CrossRefGoogle Scholar
Kassambara, A (2023) ggpubr: “ggplot2” based publication ready plots. Available online from: https://cran.r-project.org/package=ggpubr Google Scholar
Kaushal, N, Awasthi, R, Gupta, K, Gaur, PM, Siddique, KHM and Nayyar, H (2013) Heat-stress-induced reproductive failures in chickpea (Cicer arietinum) are associated with impaired sucrose metabolism in leaves and anthers. Functional Plant Biology 40, 1334–1349. https://doi.org/10.1071/FP13082 CrossRefGoogle ScholarPubMed
Kholová, J, Murugesan, T, Kaliamoorthy, S, Malayee, S, Baddam, R, Hammer, GL, McLean, G, Deshpande, S, Hash, CT, Craufurd, PQ and Vadez, V (2014) Modelling the effect of plant water use traits on yield and stay-green expression in sorghum. Functional Plant Biology 41, 1019–1034. https://doi.org/10.1071/FP13355 CrossRefGoogle ScholarPubMed
Kiniry, JR, Jones, CA, O’Toole, JC, Blanchet, R, Cabelguenne, M and Spanel, DA (1989) Radiation-use efficiency in biomass accumulation prior to grain-filling for five grain-crop species. Field Crops Research 20, 51–64. https://doi.org/10.1016/0378-4290(89)90023-3 CrossRefGoogle Scholar
Koehler, T, Wankmüller, FJP, Sadok, W and Carminati, A (2023) Transpiration response to soil drying versus increasing vapor pressure deficit in crops: physical and physiological mechanisms and key plant traits. Journal of Experimental Botany 74, 4789–4807. https://doi.org/10.1093/jxb/erad221 CrossRefGoogle ScholarPubMed
Kovaleski, S, Heldwein, AB, Dalmago, GA and de Gouvêa, JA (2020) Frost damage to canola (Brassica napus L.) during reproductive phase in a controlled environment. Agrometeoros 27(2), 397–407. http://dx.doi.org/10.31062/agrom.v27i2.26463 CrossRefGoogle Scholar
Kumar, J and Abbo, S (2001) Genetics of flowering time in chickpea and its bearing on productivity in semiarid environments. Advances in Agronomy 72, 107–138. https://doi.org/10.1016/S0065-2113(01)72012-3 CrossRefGoogle Scholar
Kumar, S, Attri, SD and Singh, KK (2019) Comparison of Lasso and stepwise regression technique for wheat yield prediction. Journal of Agrometeorology 21, 188–192. https://doi.org/10.54386/jam.v21i2.231 CrossRefGoogle Scholar
Lake, L and Sadras, VO (2014) The critical period for yield determination in chickpea (Cicer arietinum L.). Field Crops Research 168, 1–7. https://doi.org/10.1016/j.fcr.2014.08.003 CrossRefGoogle Scholar
Lenth, RV (2024) emmeans: Estimated marginal means, aka least-squares means. Available online from: https://cran.r-project.org/package=emmeans Google Scholar
Liu, DL, Anwar, MR, O’Leary, G and Conyers, MK (2014) Managing wheat stubble as an effective approach to sequester soil carbon in a semi-arid environment: spatial modelling. Geoderma 214, 50–61. https://doi.org/10.1016/j.geoderma.2013.10.003 CrossRefGoogle Scholar
Liu, K, Bandara, M, Hamel, C, Knight, JD and Gan, Y (2020) Intensifying crop rotations with pulse crops enhances system productivity and soil organic carbon in semi-arid environments. Field Crops Research 248, 107657. https://doi.org/10.1016/j.fcr.2019.107657 CrossRefGoogle Scholar
Lobell, DB, Hammer, GL, McLean, G, Messina, C, Roberts, MJ and Schlenker, W (2013) The critical role of extreme heat for maize production in the United States. Nature Climate Change 3, 497–501. https://doi.org/10.1038/nclimate1832 CrossRefGoogle Scholar
Lorite, IJ, Castilla, A, Cabezas, JM, Alza, J, Santos, C, Porras, R, Gabaldón-Leal, C, Muñoz-Marchal, E and Sillero, JC (2023) Analyzing the impact of extreme heat events and drought on wheat yield and protein concentration, and adaptation strategies using long-term cultivar trials under semi-arid conditions. Agricultural and Forest Meteorology 329, 109279. https://doi.org/10.1016/j.agrformet.2022.109279 CrossRefGoogle Scholar
Muchow, RC, Sinclair, TR and Bennett, JM (1990) Temperature and solar radiation effects on potential maize yield across locations. Agronomy Journal 82, 338–343. https://doi.org/10.2134/agronj1990.00021962008200020033x CrossRefGoogle Scholar
NVT Online (2025) GRDC National Variety Trials. Available online from: https://nvt.grdc.com.au/nvt-protocols (Accessed 4 February 2025).Google Scholar
Palmero, F, Fernandez, JA, Garcia, FO, Haro, RJ, Prasad, PV, Salvagiotti, F and Ciampitti, IA (2022) A quantitative review into the contributions of biological nitrogen fixation to agricultural systems by grain legumes. European Journal of Agronomy 136, 126514. https://doi.org/10.1016/j.eja.2022.126514 CrossRefGoogle Scholar
Paredes, P, Rodrigues, GC, Alves, I and Pereira, LS (2014) Partitioning evapotranspiration, yield prediction, and economic returns of maize under various irrigation management strategies. Agricultural Water Management 135, 27–39. https://doi.org/10.1016/j.agwat.2013.12.010 CrossRefGoogle Scholar
Passioura, J (2006) Increasing crop productivity when water is scarce—from breeding to field management. Agricultural Water Management 80, 176–196. https://doi.org/10.1016/j.agwat.2005.07.012 CrossRefGoogle Scholar
Peake, AS, Dreccer, MF, Whish, JP and Hochman, Z (2020) Final report to the Grains Research and Development Corporation. Project CSP1904-005RXT: The adaptation of pulses (chickpea and lentil) across the northern grains region. CSIRO Agriculture and Food, Australia. https://doi.org/10.25919/5f1f2438cd4e4 CrossRefGoogle Scholar
Pedersen, TL (2024) patchwork: The composer of plots. Available online from: https://cran.r-project.org/package=patchwork Google Scholar
Piñeiro, G, Perelman, S, Guerschman, JP and Paruelo, JM (2008) How to evaluate models: observed vs. predicted or predicted vs. observed? Ecological modelling 216, 316–322. https://doi.org/10.1016/j.ecolmodel.2008.05.006 CrossRefGoogle Scholar
Posit team (2024) RStudio: Integrated development environment for r. Posit Software, PBC, Boston, MA. Available online from: http://www.posit.co/ Google Scholar
Prasad, PV, Boote, KJ and Allen, LH (2006) Adverse high temperature effects on pollen viability, seed-set, seed yield and harvest index of grain-sorghum [Sorghum bicolor (L.) Moench] are more severe at elevated carbon dioxide due to higher tissue temperatures. Agricultural and Forest Meteorology 139, 237–251. https://doi.org/10.1016/j.agrformet.2006.07.003 CrossRefGoogle Scholar
R Core Team (2024) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available online from: https://www.R-project.org/ Google Scholar
Rani, BS and Krishna, TG (2016) Response of chickpea (Cicer arietinum L.) varieties to nitrogen on a calcareous vertisols. Indian Journal of Agricultural Research 50, 278–281. https://doi.org/10.18805/ijare.v50i3.10749 Google Scholar
Richards, RA (2000) Selectable traits to increase crop photosynthesis and yield of grain crops. Journal of Experimental Botany 51, 447–458. https://doi.org/10.1093/jexbot/51.suppl_1.447 CrossRefGoogle ScholarPubMed
Richards, RA, Rebetzke, GJ, Watt, M, Condon, AG, Spielmeyer, W and Dolferus, R (2010) Breeding for improved water productivity in temperate cereals: Phenotyping, quantitative trait loci, markers, and the selection environment. Functional Plant Biology 37, 85–97. https://doi.org/10.1071/FP09219 CrossRefGoogle Scholar
Rodriguez-Sanchez, F and Jackson, CP (2023) grateful: Facilitate citation of r packages. Available online from: https://pakillo.github.io/grateful/ CrossRefGoogle Scholar
Sadras, VO, Cassman, KGG, Grassini, P, Hall, AJ, Bastiaanssen, WGM, Laborte, AG, Milne, AG, Sileshi, G and Steduto, P (2015) Yield gap analysis of field crops: Methods and case studies. FAO Water Reports, 41. Available online from: https://digitalcommons.unl.edu/wffdocs/87/ Google Scholar
Sadras, VO and McDonald, G (2011) Water use efficiency of grain crops in Australia: principles, benchmarks and management. Grains Research and Development Corporation, South Australian Research and Development Institute and University of Adelaide. GRDC Project Code: DAS00089; https://www.pir.sa.gov.au/__data/assets/pdf_file/0003/238413/SARDI-Water-Use-Efficiency-Grain-Crops-Australia.pdf Google Scholar
Sadras, VO and Milroy, SP (1996) Soil-water thresholds for the responses of leaf expansion and gas exchange: a review. Field Crops Research 47, 253–266. https://doi.org/10.1016/0378-4290(96)00014-7 CrossRefGoogle Scholar
Sage, RF and Kubien, DS (2007) The temperature response of C3 and C4 photosynthesis. Plant, Cell & Environment 30, 1086–1106. https://doi.org/10.1111/j.1365-3040.2007.01682.x CrossRefGoogle Scholar
Saini, R, Das, R, Adhikary, A, Kumar, R, Singh, I, Nayyar, H and Kumar, S (2022) Drought priming induces chilling tolerance and improves reproductive functioning in chickpea (Cicer arietinum L.). Plant Cell Reports 41, 2005–2022. https://doi.org/10.1007/s00299-022-02905-7 CrossRefGoogle ScholarPubMed
Samarah, NH (2005) Effects of drought stress on growth and yield of barley. Agronomy for sustainable development 25, 145–149. https://doi.org/10.1051/agro:2004064 CrossRefGoogle Scholar
Schwarz, G (1978) Estimating the dimension of a model. Annals of Statistics 6, 461–464. https://doi.org/10.1214/aos/1176344136 CrossRefGoogle Scholar
Siddique, KHM, Johansen, C, Turner, NC, Jeuffroy, MH, Hashem, A, Sakar, D, Gan, Y and Alghamdi, SS (2012) Innovations in agronomy for food legumes. A review. Agronomy for Sustainable Development 32, 45–64. https://doi.org/10.1007/s13593-011-0021-5 CrossRefGoogle Scholar
Simon, N, Friedman, JH, Hastie, T and Tibshirani, R (2011) Regularization paths for cox’s proportional hazards model via coordinate descent. Journal of Statistical Software 39, 1–13. https://doi.org/10.18637/jss.v039.i05 Google ScholarPubMed
Sinclair, TR, Hammer, GL and van Oosterom, EJ (2005) Potential yield and water-use efficiency benefits in sorghum from limited maximum transpiration rate. Functional Plant Biology 32, 945–952. https://doi.org/10.1071/FP05047 CrossRefGoogle ScholarPubMed
Sinclair, TR and Muchow, RC (1999) Radiation use efficiency. Advances in Agronomy 65, 215–265. https://doi.org/10.1016/S0065-2113(08)60914-1 CrossRefGoogle Scholar
Sinclair, TR and Muchow, RC (2001) System analysis of plant traits to increase grain yield on limited water supplies. Agronomy Journal 93, 263–270. https://doi.org/10.2134/agronj2001.932263x CrossRefGoogle Scholar
Singh, KB, Malhotra, RS, Halila, MH, Knights, EJ and Verma, MM (1993) Current status and future strategy in breeding chickpea for resistance to biotic and abiotic stresses. Euphytica 73, 137–149. https://doi.org/10.1007/BF00027190 CrossRefGoogle Scholar
Singh, KB and Saxena, MC (1993) Breeding for stress tolerance in cool-season food legumes. John Wiley & Sons. https://doi.org/10.1002/jpln.19941570222 Google Scholar
Singh, R, Sharma, P, Varshney, RK, Sharma, SK and Singh, NK (2008) Chickpea improvement: role of wild species and genetic markers. Biotechnology and Genetic Engineering Reviews 25, 267–314. https://doi.org/10.5661/bger-25-267 CrossRefGoogle ScholarPubMed
Soltani, A and Sinclair, TR (2011) A simple model for chickpea development, growth and yield. Field Crops Research 124, 252–260. https://doi.org/10.1016/j.fcr.2011.06.021 CrossRefGoogle Scholar
Tay, JK, Narasimhan, B and Hastie, T (2023) Elastic net regularization paths for all generalized linear models. Journal of statistical software 106, 1–31. https://doi.org/10.18637/jss.v106.i01 CrossRefGoogle ScholarPubMed
Thomson, BD, Siddique, KHM, Barr, MD and Wilson, JM (1997) Grain legume species in low rainfall Mediterranean-type environments I. Phenology and seed yield. Field Crops Research 54, 173–187. https://doi.org/10.1016/S0378-4290(97)00047-6 Google Scholar
Tibshirani, R (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x CrossRefGoogle Scholar
Tierney, N and Cook, D (2023) Expanding tidy data principles to facilitate missing data exploration, visualization and assessment of imputations. Journal of Statistical Software 105, 1–31. https://doi.org/10.18637/jss.v105.i07 CrossRefGoogle Scholar
Trout, TJ and DeJonge, KC (2017) Water productivity of maize in the US high plains. Irrigation Science 35, 251–266. https://doi.org/10.1007/s00271-017-0540-1 CrossRefGoogle Scholar
Unkovich, M, Baldock, J and Farquharson, R (2018) Field measurements of bare soil evaporation and crop transpiration, and transpiration efficiency, for rainfed grain crops in Australia – a review. Agricultural Water Management 205, 72–80; https://doi.org/10.1016/j.agwat.2018.04.016 CrossRefGoogle Scholar
Unkovich, M, McBeath, T, Moodie, M and Macdonald, LM (2023) High soil strength and cereal crop responses to deeper tillage on sandy soils in a semiarid environment. Field Crops Research 291, 108792. https://doi.org/10.1016/j.fcr.2022.108792 Google Scholar
Vadez, V, Berger, JD, Warkentin, T, Asseng, S, Ratnakumar, P, Rao, KPC, Gaur, PM, Munier-Jolain, N, Larmure, A, Voisin, AS and Sharma, HC (2012) Adaptation of grain legumes to climate change: a review. Agronomy for Sustainable Development 32, 31–44. https://doi.org/10.1007/s13593-011-0020-6 Google Scholar
Vadez, V, Grondin, A, Chenu, K, Henry, A, Laplaze, L, Millet, EJ and Carminati, A (2024) Crop traits and production under drought. Nature Reviews Earth & Environment 5, 211–225. https://doi.org/10.1038/s43017-023-00514-w Google Scholar
Verghis, BA, McKenzie, BA and Hill, GD (1999) Phenological development of chickpeas (Cicer arietinum) in Canterbury, New Zealand. New Zealand Journal of Crop and Horticultural Science 27, 249–256; https://doi.org/10.1080/01140671.1999.9514103 CrossRefGoogle Scholar
Vogel, E, Donat, MG, Alexander, LV, Meinshausen, M, Ray, DK, Karoly, D, Meinshausen, N and Frieler, K (2019) The effects of climate extremes on global agricultural yields. Environmental Research Letters 14, 054010. https://doi.org/10.1088/1748-9326/ab154b CrossRefGoogle Scholar
Wang, B, Liu, DL, Asseng, S, Macadam, I and Yu, Q (2017) Modelling wheat yield change under CO2 increase, heat and water stress in relation to plant available water capacity in eastern Australia. European Journal of Agronomy 90, 152–161. https://doi.org/10.1016/j.eja.2017.08.005 CrossRefGoogle Scholar
Waqas, M, Azhar, MT, Rana, IA, Arif, A and Atif, RM (2019) Drought stress in chickpea: physiological, breeding, and omics perspectives. In Wani, S (ed.), Recent Approaches in Omics for Plant Resilience to Climate Change. Springer. https://doi.org/10.1007/978-3-030-21687-0_9 Google Scholar
Weylandt, M, Campbell, F and Allen, G (2018) ExclusiveLasso: Generalized Linear Models with the Exclusive Lasso Penalty. Available online from: https://github.com/DataSlingers/ExclusiveLasso Google Scholar
Wickham, H (2007) Reshaping data with the reshape package. Journal of Statistical Software 21, 1–20. https://doi.org/10.18637/jss.v021.i12 CrossRefGoogle Scholar
Wickham, H, Averick, M, Bryan, J, Chang, W, McGowan, LDA, François, R, Grolemund, G, Hayes, A, Henry, L, Hester, J and Kuhn, M (2019) Welcome to the tidyverse. Journal of Open Source Software 4, 1686. https://doi.org/10.21105/joss.01686 CrossRefGoogle Scholar
Willmott, CJ (1982) Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society 63, 1309–1313. https://doi.org/10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;22.0.CO;2>CrossRefGoogle Scholar
Xing, H, Liu, DL, Li, G, Wang, B, Anwar, MR, Crean, J, Lines-Kelly, R and Yu, G (2017) Incorporating grain legumes in cereal-based cropping systems to improve profitability in southern New South Wales, Australia. Agricultural Systems 154, 112–123; https://doi.org/10.1016/j.agsy.2017.03.010 CrossRefGoogle Scholar
Yang, Y, Liu, DL, Anwar, MR, O’Leary, G, Macadam, I and Yang, Y (2016) Water use efficiency and crop water balance of rainfed wheat in a semi-arid environment: sensitivity of future changes to projected climate changes and soil type. Theoretical and Applied Climatology 123, 565–579. https://doi.org/10.1007/s00704-015-1376-3 CrossRefGoogle Scholar
Yuan, M and Lin, Y (2006) Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society Series B: Statistical Methodology 68, 49–67. https://doi.org/10.1111/j.1467-9868.2005.00532.x CrossRefGoogle Scholar
Zaman-Allah, M, Jenkinson, DM and Vadez, V (2011) Chickpea genotypes contrasting for seed yield under terminal drought stress in the field differ for traits related to the control of water use. Functional Plant Biology 38, 270–281. https://doi.org/10.1071/FP10244 CrossRefGoogle Scholar
Zeleke, K and Nendel, C (2019) Growth and yield response of faba bean to soil moisture regimes and sowing dates: field experiment and modelling study. Agricultural Water Management 213, 1063–1077; https://doi.org/10.1016/j.agwat.2018.12.023 CrossRefGoogle Scholar
Zeleke, KT, Anwar, MR, Emebiri, L and Luckett, D (2023) Weather indices during reproductive phase explain wheat yield variability. The Journal of Agricultural Science 161, 617–632. https://doi.org/10.1017/S0021859623000503 CrossRefGoogle Scholar
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