About

Dr. Carlos D. Messina is a professor of predictive breeding in the Department of Horticultural Sciences at the University of Florida, Institute of Food and Agricultural Sciences. His work involves collaborating with breeders to enhance the nutritional value of Florida produce and reimagining agriculture as a solution to climate change. He specializes in developing artificial intelligence for plant breeding, aiming to enable society to harmonize crop improvement efforts for regenerative agricultural systems that improve human health, nutrient security, and adaptation to climate change. Dr. Messina's expertise encompasses climate change, artificial intelligence, plant breeding, crop systems, crop physiology, crop modeling, and crop improvement, with a particular focus on maize and drought tolerance.

Research topics

• Biology
• Computer Science
• Agronomy
• Mathematics
• Genetics
• Biotechnology
• Environmental science
• Statistics
• Ecology
• Computational biology
• Environmental resource management
• Economics
• Geography
• Engineering
• Agroforestry
• Chemistry
• Agricultural engineering
• Cell biology

Selected publications

• Root system growth and function respond to soil temperature in maize ( <i>Zea mays</i> L.)

  PLANT PHYSIOLOGY · 2026-02-27

  articleSenior author

  Crop adaptation to the mixture of environments that defines the target population of environments is the result of balanced resource allocation between roots, shoots, and reproductive organs. Root growth plays a critical role in the determination of this delicate balance. The responses of root growth and function to temperature can determine the strength of roots as sinks but also influence a crop's ability to uptake water and nutrients. Surprisingly, this behavior has not been studied in maize (Zea mays) since the middle of the last century, and the genetic determinants are unknown. Low temperatures recorded frequently in deep soil layers limit root growth and soil exploration and may constitute a bottleneck for increasing drought tolerance, nitrogen recovery, sequestration of carbon, and productivity in maize. We developed high-throughput phenotyping systems to investigate these responses and to examine genetic variability therein across diverse maize germplasm. Here, we show that there is (i) genetic variation in root growth under low temperature below a previously set threshold of 10 °C and (ii) genotypic variation in water transport under low temperature. The trait set examined herein and the high-throughput phenotyping platform developed for its characterization provide a unique opportunity for removing a major bottleneck for crop improvement and adaptation to climate change.

  PublisherDOI

• A case for breeding heat-tolerant broccoli

  npj Sustainable Agriculture · 2025-09-23

  articleOpen accessSenior author

  Poor farming and human nutrition cost lives and $10 trillion/yr. Broccoli is rich in phytochemicals implicated in reduced morbidity, but maladapted to the tropics and subtropics, where human illness is severe. Here we review advances in biology and breeding to propose a blueprint for a global broccoli breeding program— designed to improve adaptation to high heat environments, harness its current health benefits, and reduce impacts of long carbon-intensive supply chains.

  PublisherOA PDFDOI

• Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction

  Theoretical and Applied Genetics · 2025-07-01 · 9 citations

  reviewOpen accessSenior author

  KEY MESSAGE: Trait Genome-to-Phenome (G2P) dimensionality and "breeding context" combine to influence the realised prediction skill of different whole genome prediction (WGP) methods. Theory and empirical evidence both suggest there is likely to be "No Free Lunch" for prediction-based breeding. Ensembles of diverse sets of G2P models provide a framework to expose and investigate the high G2P dimensionality of trait genetic architecture for WGP applications. Artificial Intelligence and Machine Learning (AI-ML) prediction algorithms contribute novel trait G2P model diversity to ensemble-based WGP. Prediction-based breeding leveraging ensembles of G2P models creates new opportunities to identify and design novel paths for genetic gain. Improving our understanding of trait genetic architecture is motivated by creating new opportunities to enhance breeding methodology, create new selection trajectories for crop improvement, and accelerate rates of genetic gain. With access to high-throughput sequencing, phenotyping and envirotyping technologies we can model the complex multidimensional relationships between sequence variation and trait phenotypic variation that are under the influences of selection. Using the framework of the diversity prediction theorem, we consider applications of ensembles of diverse trait genome-to-phenome (G2P) models. Crop growth models (CGM) are an example of a hierarchical framework for studying the influences of quantitative trait loci (QTL) within trait networks and their interactions with different environments to determine yield. Hybrid CGM-G2P models combine elements of CGMs, to understand how trait networks influence crop yield performance, with trait G2P models, to understand influences of trait genetic architecture on selection trajectories. We discuss hybrid CGM-G2P models and their potential applications to enhance ensemble-based prediction. Multi-environment trials conducted across breeding cycles can be designed to include contrasting environments to expose the different CGM-G2P dimensions of the trait by environment interactions that are influential on selection trajectories. Artificial intelligence and machine learning (AI-ML) algorithms can be applied as components of ensembles to improve gene discovery and quantification of allele effects for traits to enhance G2P prediction applications. We use the trait flowering time in the maize TeoNAM experiment to illustrate and motivate further investigations of how to leverage ensembles of G2P models for prediction-based breeding.

  PublisherOA PDFDOI

• The Big BIT maize experiment: A large multi‐location, multi‐year, multi‐tester, multi‐population predictive breeding validation study

  The Plant Genome · 2025-10-31 · 1 citations

  articleOpen access

  The Big Breeding Innovation Team (Big BIT) maize (Zea mays L.) experiment was one of the largest genomic data-informed predictive breeding validation studies ever conducted. The experiment was a multi-location, multi-year, multi-tester, multi-population study involving F1 maize hybrids created by crossing individual doubled haploids to inbred testers. The purpose of the study, performed by DuPont Pioneer/Corteva Agriscience in 2017, 2018, and 2019, was to build comprehensive datasets to help answer a wide range of practical questions focused on optimizing predictive breeding strategies in maize. The purpose of our study is to (1) describe the design and unique features of our study and (2) discuss learnings with practical implications for plant breeders. Since the same F1 maize hybrids were grown across three distinct years, we use basic descriptive summary statistics to discuss our learnings. We provide a technical justification for the use of basic statistics and discuss the expected theoretical prediction accuracy of genomic estimated breeding values (GEBVs) of Big BIT individuals and families, and predictive abilities obtained by performing large-scale cross-validations. Our study provides multi-year field data-based evidence that, for inbred/variety development focused plant improvement efforts, early-stage genetic evaluation should be based on GEBVs generated from wide-area testing training datasets. This holds true for candidates for selection with or without own phenotypic records.

  PublisherOA PDFDOI

• Toward a general framework for AI-enabled prediction in crop improvement

  Theoretical and Applied Genetics · 2025-06-12 · 19 citations

  articleOpen access1st authorCorresponding

  KEY MESSAGE: A theoretical framework for AI and ensembled prediction for crop improvement is introduced and demonstrated using the logistic map. Symbolic/sub-symbolic AI-based prediction can increase predictive skill with increase in system complexity. The curse of dimensionality in genomic prediction has been established and hampers genetic gain for complex traits. Artificial intelligence (AI) that fuses symbolic and sub-symbolic approaches to prediction is emerging as an approach that can deal effectively with this problem. By leveraging information across physiological and genetic networks, it is plausible to increase prediction accuracy by harnessing prior knowledge and computation approaches. Ensembles of models de facto implement the diversity prediction theorem, and thus enable breeders identify subnetworks of genetic and physiological networks underpinning crop response to management (M) and environment (E). Here, we introduce a theoretical framework for AI-enabled prediction in crop improvement. This framework brings together elements of dynamical systems modeling, ensembles, Bayesian statistics and optimization. We demonstrate properties of this framework and limits to predictability using a simple logistic map. We show that heritability and level of predictability decrease with increase in system complexity that conforms well with prior empirical evidence. We show that predicting systems states is an inferior strategy to predicting system process rates for complex systems. This holds for both the level of predictability and for the ability to use the data generating functions to produce a view of the system state space that can help breeders develop an intuition for how biological interventions can affect the performance of the crops. By integrating biological knowledge and computational approaches to prediction, it is feasible to increase predictive accuracy in breeding systems and therefore hasten the rate of genetic gain.

  PublisherOA PDFDOI

• Understanding rates of genetic gain in sorghum [ <i>Sorghum bicolor</i>  (L.) Moench] in the United States

  The Plant Genome · 2025-09-01 · 3 citations

  articleOpen accessSenior authorCorresponding

  The loss of agricultural biodiversity will compromise societal ability to proof the food system against abiotic and biotic perturbations. The steady decrease in planted area of sorghum [Sorghum bicolor (L.) Moench] in the United States is alarming. Recent studies attributed this decline to a lower rate of genetic gain in sorghum relative to maize due to the lower investment in grain sorghum breeding. While this is a reasonable interpretation, it is also plausible that sorghum breeding has reached a peak in the adaptation landscape for drought within the genetic and physiological boundaries imposed by the germplasm currently used by breeders. To test this hypothesis, we have conducted a breeding gap analysis. CERES-Sorghum was used to run a simulation experiment comprised of ∼1 billion genotype × environment × management combinations for the US sorghum belt. We estimated the 0.99 quantile of the response of yield to evapotranspiration (ET); this boundary defines the biophysical limits to yield based on water availability. We then projected data from multienvironment trials onto this yield-trait space. When trials were conducted in managed stress environments in the absence of water deficit at flowering time, we observed that modern sorghum hybrids reached the biophysical boundary. This result can explain the observed lack of genetic gain, which could be reverted by increasing investments in breeding efforts that harness novel sources of genetic diversity, phenomics, and genome-to-phenome technologies. We hypothesize that there are transfer learning opportunities to inform sorghum breeding strategies that can shift the yield-ET production front from successful crop improvement pathways identified in maize.

  PublisherOA PDFDOI

• Designing a nitrogen-efficient cold-tolerant maize for modern agricultural systems

  The Plant Cell · 2025-07-01 · 6 citations

  reviewOpen access

  Maize (Zea mays L.) is the world's most productive grain crop and a cornerstone of global food supply. However, in temperate agricultural systems, maize exhibits 2 key anomalies. First, as a tropical species, maize cannot be planted in the cold conditions of early spring when light and natural soil nitrogen are available, resulting in a shorter growing season and creating a seasonal mismatch between nitrogen accessibility and demand. Second, maize kernel protein is a major nitrogen sink, driving fertilizer demand because of the scale of cultivation. This inefficient mismatch stems from modern maize's uses and the modest nutritional value of storage proteins. To address these anomalies, we established the Circular Economy that Reimagines Corn Agriculture initiative. Our vision requires advances in 3 research areas: (ⅰ) developing cold and frost tolerance during germination and early growth to enable the use of spring nitrogen and light resources; (ⅱ) reducing nitrogen allocation to grain by reducing low-quality storage proteins and developing alternative nitrogen sinks; and (ⅲ) stabilizing soil nitrogen by enhancing biological nitrification inhibition. We present blueprints for a nitrogen-efficient, cold-tolerant maize designed to utilize the full growing season, enabling farmers in temperate regions to fully leverage maize's C4 photosynthesis, reduce fertilizer inputs, increase yields, and minimize environmental impact.

  PublisherOA PDFDOI

• Leveraging Crop Wild Relatives and Ploidy Level for Climate-Resilient Annual Ryegrass

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-18

  preprintOpen access

  ABSTRACT Leveraging crop wild relatives and chromosome manipulations are powerful tools for developing climate-resilient agroecosystems. Yet, the combined effects of polyploidy and diverse genetic makeup on plant responses to climate-induced stresses remain generally underexplored, particularly in annual ryegrass ( Lolium multiflorum Lam.), a major cool-season forage species. In a two-year controlled environment study, we assessed phenotypic responses of a wildtype and the cultivar ‘Marshall’ at diploid (2x = 14) and tetraploid (4x = 28) levels. Plants were grown under 540 and 800 ppm [CO 2 ] and at full and 50% evapotranspiration regimes. Anatomical and physiological differences between populations and ploidy levels were limited. Despite genetic background, total biomass production increased by 44% from 540 to 800 ppm [CO 2 ], driven by enhanced aboveground growth. While the 2x-wildtype showed a lower leaf-to-stem ratio (a proxy for forage quality) than 2x-Marshall, this gap diminished at the tetraploid condition. These differences, and the lack thereof, highlight the importance of considering both chromosome manipulation and the genetic sourcing of crop wild relatives to expand diversity in cultivated species. Our findings reveal that wild populations can achieve comparable productivity and efficiency to improved populations under climate-change induced environments, while contributing adaptive traits valuable for resilient cropping systems. HIGHLIGHT Wildtype ryegrass matched improved high-yield cultivar under stress and elevated [CO 2 ], suggesting the need for both local adaptation potential and for new breeding targets under climate change.

  PublisherOA PDFDOI

• Translating weighted probabilistic bits to synthetic genetic circuits

  The Plant Genome · 2024-10-18 · 1 citations

  articleOpen accessSenior author

  Synthetic genetic circuits in plants could be the next technological horizon in plant breeding, showcasing potential for precise patterned control over expression. Nevertheless, uncertainty in metabolic environments prevents robust scaling of traditional genetic circuits for agricultural use, and studies show that a deterministic system is at odds with biological randomness. We analyze the necessary requirements for assuring Boolean logic gate sequences can function in unpredictable intracellular conditions, followed by interpreted pathways by which a mathematical representation of probabilistic circuits can be translated to biological implementation. This pathway is utilized through translation of a probabilistic circuit model presented by Pervaiz that works through a series of bits; each composed of a weighted matrix that reads inputs from the environment and a random number generator that takes the matrix as bias and outputs a positive or negative signal. The weighted matrix can be biologically represented as the regulatory elements that affect transcription near promotors, allowing for an electrical bit to biological bit translation that can be refined through tuning using invertible logic prediction of the input to output relationship of a genetic response. Failsafe mechanisms should be introduced, possibly through the use of self-eliminating CRISPR-Cas9, dosage compensation, or cybernetic modeling (where CRISPR is clustered regularly interspaced short palindromic repeats and Cas9 is clustered regularly interspaced short palindromic repeat-associated protein 9). These safety measures are needed for all biological circuits, and their implementation is needed alongside work with this specific model. With applied responses to external factors, these circuits could allow fine-tuning of organism adaptation to stress while providing a framework for faster complex expression design in the field.

  PublisherOA PDFDOI

• Transgene effects vary among maize populations with implications for improving quantitative traits

  Crop Science · 2024-11-11 · 1 citations

  articleOpen access

  Abstract The goal of transgenesis in plant breeding is to make step‐change improvements in traits of interest. However, improving quantitative traits, such as yield in maize ( Zea mays L.), with transgenes has been difficult. Traditionally, transgene testing is done on a few isogenic lines, and results are extrapolated to entire breeding populations. Testing on limited germplasm does not provide a robust estimate of a transgene's value. Incorporating transgenes directly into breeding populations could increase genetic variance and the rate of genetic gain. Here, we used a transgene that reduces ethylene as a case study and investigated event, transgene, family, and environment effects and their interactions. We also determined whether introduction of the transgene into a breeding population would result in transgenic lines being preferentially selected over nontransgenic lines for yield. We found significant variation in transgene effects across clustered environments and families for multiple traits including yield. In environmental Cluster 2, the transgenic lines yielded 0.4 Mg ha −1 more than nontransgenic lines in family KC22; yet, in family QY43, transgenic lines yielded 0.3 Mg ha −1 less. Similarly, within Cluster 4, the QY43 family had preferential selection of transgenic over nontransgenic lines, whereas in families YE41 and AY91, nontransgenic lines were selected more frequently. These results show the critical importance of evaluating transgenes across broad germplasm diversity to assess their general value to a program. Integrating transgenes, or using gene editing, directly in a breeding program can expand genetic variation for quantitative traits and potentially accelerate genetic gain.

  PublisherOA PDFDOI

Frequent coauthors

• Mark Cooper

  Agriculture and Food

  153 shared

• Graeme Hammer

  Agriculture and Food

  101 shared

• Carla Gho

  28 shared

• Ignacio A. Ciampitti

  Kansas State University

  27 shared

• Erik van Oosterom

  Agriculture and Food

  27 shared

• Tom Tang

  Corteva (United States)

  23 shared

• Alex Wu

  ARC Centre of Excellence for Plant Success in Nature and Agriculture

  18 shared

• Owen Powell

  16 shared

Awards & honors

• 2020 Crop Science Outstanding Associate Editor. Crop Science…
• 2020 Plant Breeding AC.E. Award. Technologies and Innovation…
• 2016 Achievements in Research. The Henry A. Wallace Agricult…
• 2011 Business Achievement Award. Contribution to drought tol…
• 2006 Crop Genetics Research and Development Contribution Awa…