Integrating crop growth models with remote sensing for predicting biomass yield of sorghum

Journal Article · Fri Jan 08 00:00:00 EST 2021 · in silico Plants

DOI:https://doi.org/10.1093/insilicoplants/diab001· OSTI ID:1768102

Yang, Kai-Wei ^[1]; Chapman, Scott ^[2]; Carpenter, Neal ^[1]; Hammer, Graeme  ^[3]; McLean, Greg ^[3]; Zheng, Bangyou ^[4]; Chen, Yuhao ^[5]; Delp, Edward ^[5]; Masjedi, Ali ^[6]; Crawford, Melba ^[7]; Ebert, David ^[5]; Habib, Ayman ^[6]; Thompson, Addie  ^[8]; Weil, Clifford ^[1]; Tuinstra, Mitchell R.  ^[1]; Yin, ed., Xinyou; Long, ed., Stephen P.

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1. Department of Agronomy, Purdue University, West Lafayette, IN, USA
2. School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD, Australia, CSIRO Agriculture and Food, Queensland Biosciences Precinct, St Lucia, QLD, Australia
3. Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
4. CSIRO Agriculture and Food, Queensland Biosciences Precinct, St Lucia, QLD, Australia
5. School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
6. Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, USA
7. Department of Agronomy, Purdue University, West Lafayette, IN, USA, Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, USA
8. Department of Agronomy, Purdue University, West Lafayette, IN, USA, Plant Soil and Microbial Sciences Department, Michigan State University, East Lansing, MI, USA

Abstract Plant phenotypes are often descriptive, rather than predictive of crop performance. As a result, extensive testing is required in plant breeding programmes to develop varieties aimed at performance in the target environments. Crop models can improve this testing regime by providing a predictive framework to (i) augment field phenotyping data and derive hard-to-measure phenotypes and (ii) estimate performance across geographical regions using historical weather data. The goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote-sensing and ground-reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids. Genotype parameters for each hybrid were estimated using remote-sensing measurements combined with manual phenotyping in West Lafayette, IN, in 2018. The models were validated in hybrid performance trials in two additional seasons at that site and against yield trials conducted in Bushland, TX, between 2001 and 2018. These trials demonstrated that (i) maximum plant height, final dry biomass and radiation use efficiency (RUE) of photoperiod-sensitive and -insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum, (ii) photoperiod-sensitive sorghum hybrids exhibited greater biomass production in longer growing environments and (iii) the parameterized and validated models perform well in above-ground biomass simulations across years and locations. Crop growth models that integrate remote-sensing data offer an efficient approach to parameterize larger plant breeding populations.

• Sponsoring Organization: USDOE
• Grant/Contract Number: AR0001135
• OSTI ID: 1768102
• Journal Information: in silico Plants, Journal Name: in silico Plants Vol. 3 Journal Issue: 1; ISSN 2517-5025
• Publisher: Oxford University Press
• Country of Publication: United Kingdom
• Language: English