Crop Improvement for Circular Bioeconomy Systems

Messina, Carlos D.; Eeuwijk, Fred van; Tang, Tom; Truong, Sandra K.; McCormick, Ryan F.; Technow, Frank; Powell, Owen; Mayor, L.; Gutterson, Neal; Jones, James W.; Hammer, Graeme; Cooper, Mark

doi:10.13031/ja.14912

Cited by 21 publications

(75 citation statements)

References 69 publications

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“…At intermediate levels of ET, the interaction of traits as trait networks underpins most of the phenotypic variation. In the presence of these trait interactions and interactions with the environment, we have observed that a CGM can perform better than empirical models (Technow et al 2015, Messina et al 2018, Diepenbrock et al 2022, Messina et al 2022c). Cooper et al (2009) using simulation approaches argued that the benefit of a molecular breeding strategy over phenotypic selection increases with increasing complexity of the genotype and environment system.…”

Section: Models That Predict Emergent Phenotypesmentioning

confidence: 99%

Section: Models That Predict Emergent Phenotypesmentioning

confidence: 99%

“…However, if we articulate the problem within a circular economy framework, we can foresee agriculture as part of the solution to climate change rather than the cause of the problem (Bummer et al 2011; Messina et al, 2022c). Surprisingly, very few efforts were dedicated to answer the question how to breed crops for climate change while regenerating natural resources and reducing greenhouse gas emissions (Brummer et al 2011, Messina et al 2022c; Cooper & Messina, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Historical records indicate that past rate of change in climate was slow relative to the ability of current breeding systems to drive genetic gain as shown for maize in the United States since the onset of a rapid increase in minimum temperatures (Cooper et al, 2014; Messina et al, 2022c). Similar outcomes were achieved for soybeans in the Americas (de Felipe et al 2016), and wheat despite genotype x environment interactions imposing limits on the rate of genetic gain (Xiong et al 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, they would enable breeders to incorporate effective water use, nitrogen losses and emissions, soil carbon and other metrics as components of the breeding objectives. Unlike the past, breeders would have the information needed to create cultivars to minimize externalities, maximize effective water and nitrogen use, soil carbon accumulation, or a combination that contributes to produce food while combating climate change(Messina et al 2022c; …”

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confidence: 99%

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Plant Breeding in the face of climate change

Messina

Cooper

2022

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Climate change will have a net negative and inequitable impact on agriculture. Genetics for crop improvement ranks in the top set of technologies that can contribute to human adaptation to climate change. However, a framework for how to breed crops and for climate change adaptation, including the discovery of cultivars to enable new cropping systems is lacking. Here we propose a framework to develop new genotype (G) x management (M) (G x M) technologies to adapt to climate change, and to transition from current to future G x M technologies in a way that future food security does not come at the expense of current food security. The framework integrate genomic, agronomic, and environmental (E) predictors to accomplish two critical goals: 1-predict emergent phenotypes that stems from the dynamic interplay between G, E and M, and thus enable the breeder to consider the behavior of new genetic and trait combinations in environments that plants have not been exposed or tested before, and 2-identify G x M technologies that could increase food and nutritional security while regenerating natural and production resources. We highlight the need to invest in artificial intelligence and information technologies for breeders to harness multiple sources of information to create G x M technologies to address the diverse cultural and geographically granular societal needs.

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Section: Models That Predict Emergent Phenotypesmentioning

confidence: 99%

Section: Models That Predict Emergent Phenotypesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Plant Breeding in the face of climate change

Messina

Cooper

2022

Preprint

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Utility of anthesis–silking interval information to predict grain yield under water and nitrogen limited conditions

2022

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Delayed silking relative to pollen shed, measured as the anthesis-silking interval (ASI, the period between pollen shed and silking), is a good indicator of response to abiotic stresses in maize (Zea mays L.). This research was conducted to investigate how ASI is affected by nitrogen (N) and water availability and to assess the utility of ASI to indirectly predict grain yield (GY) under contrasting water and N treatments. Two experiments were conducted in Hancock, WI, in 2018 and 2019. One experiment (Diverse hybrids) included 302 hybrids resulting from the cross of diverse inbred lines by a single tester evaluated at four different treatment levels resulting from combining nonlimited and low N with nonlimited and low water treatments.The second experiment (NSS FAC) included a set of 408 hybrids derived from the cross of biparental doubled-haploid lines from 13 factorial populations and evaluated under nonlimited and low N treatments. Anthesis and silk time in growing degree days, and GY (Mg ha −1 ) were measured. Genomic prediction was assessed using a genomic best linear unbiased prediction model, and predictive ability was calculated as the correlation between genomic predictions and adjusted means in the different treatments. Predictive ability ranged from .15 to .49 for NSS FAC and from .06 to .51 for Diverse hybrids across traits and treatments. The ASI was a good indicator of stress and showed higher heritability than GY in the limited treatments for both experiments; however, it did not improve yield predictability.

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Retrospective study in US commercial sorghum breeding: I. Genetic gain in relation to relative maturity

et al. 2023

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Sorghum [Sorghum bicolor (L.) Moench] is an important staple food for human consumption and a source of animal feed in the semiarid regions of the world. Sustained positive rates of crop improvement are necessary to supply food and feed to a growing population. However, land allocated to sorghum and its inclusion in production systems has been in constant decline. Here we report the rate of sorghum genetic gain in a commercial breeding program in the United States and provide evidence that a modest yield improvement is an important factor limiting land allocation to this crop. A 6‐year study that evaluated 50 sorghum genotypes commercialized between the decades of 1960 and 2010 was conducted in 19 environments within the US Sorghum Belt region. Yield varied between 500 and 850 g m−2. Here we show a positive rate of genetic gain of 2.63 g m−2 y−1 on average across three different maturity groups grown in the United States. Rates ranged from 2.1 to 4.3 g m−2 y−1 across maturity groups. This result contrasts with a stagnant rate of crop improvement for many regions of the world, yet the rates are insufficient to reverse the negative trend in planted area. Breeding technologies are proposed to hasten genetic gain in sorghum to reverse the loss of on‐farm agricultural biodiversity.

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Crop Improvement for Circular Bioeconomy Systems

Cited by 21 publications

References 69 publications

Plant Breeding in the face of climate change

Plant Breeding in the face of climate change

Utility of anthesis–silking interval information to predict grain yield under water and nitrogen limited conditions

Retrospective study in US commercial sorghum breeding: I. Genetic gain in relation to relative maturity

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