PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 11538968

Consideration in selecting crops for the human-rated life support system: a Linear Programming model.

E F Wheeler·J Kossowski·E Goto·R W Langhans·G White·L D Albright·D Wilcox
View original source ↗Export metadata ↓

Abstract

A Linear Programming model has been constructed which aids in selecting appropriate crops for CELSS (Controlled Environment Life Support System) food production. A team of Controlled Environment Agriculture (CEA) faculty, staff, graduate students and invited experts representing more than a dozen disciplines, provided a wide range of expertise in developing the model and the crop production program. The model incorporates nutritional content and controlled-environment based production yields of carefully chosen crops into a framework where a crop mix can be constructed to suit the astronauts' needs. The crew's nutritional requirements can be adequately satisfied with only a few crops (assuming vitamin mineral supplements are provided) but this will not be satisfactory from a culinary standpoint. This model is flexible enough that taste and variety driven food choices can be built into the model.

Explore related subjects

Crops, AgriculturalDietEcological Systems, ClosedEnvironment, ControlledHumansLife Support SystemsLinear ModelsNutritional RequirementsNutritive ValueSoftwareSpace FlightNASA Center JSCNASA Discipline Life Support SystemsNASA Discipline Number 61-10NASA Program Advanced Life SupportJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

E F Wheeler, J Kossowski, E Goto, R W Langhans, G White, L D Albright, D Wilcox. 1996. Consideration in selecting crops for the human-rated life support system: a Linear Programming model.. https://doi.org/10.1016/0273-1177(95)00813-t

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Genetic transformation of forage crops: comparative barriers, evidence, and emerging strategies.

Forage crops include phylogenetically and biologically distinct legumes and grasses, and their genetic transformation is constrained by different combinations of host response, DNA-delivery efficiency, regeneration competence, genotype dependence, and genome stability. This review critically compares evidence from forage legumes and forage grasses rather than treating these groups as a single transformation category. We evaluate Agrobacterium-mediated transformation, protoplast-based delivery, particle bombardment, CRISPR/Cas-enabled applications, developmental regulators (DRs), viral vectors, and nanomaterial-mediated delivery according to four practical outcomes: reproducibility across genotypes, recovery of regenerated plants, heritable transmission, and genetic stability. Direct evidence in forage crops shows that protocol performance is strongly species-, genotype-, explant-, and endpoint-dependent; efficiencies based on transient reporters or resistant callus therefore cannot be directly equated with stable, fertile events. DR-assisted regeneration has direct proof of concept in recalcitrant forage grasses, whereas stable nanomaterial-mediated transformation and virus-induced heritable editing remain unvalidated in forage crops. We conclude that current progress is best interpreted as the engineering of interacting delivery and regeneration constraints, not as a universal transition to genotype-independent transformation. Priority should be given to standardized outcome reporting, multi-genotype and inter-laboratory validation, controlled DR expression, and rigorous molecular and phenotypic assessment of regenerated plants.

Crops, Agricultural↗
PubMed2026

Translating functional molecular knowledge into crop-breeding success.

Historical plant breeding, which optimizes phenotypes through selective crossing guided by phenotypic evaluation and molecular markers, is limited by evolutionary constraints that hinder rapid crop improvement. A new paradigm, precision breeding, circumvents these limitations by targeting genetic variants through functional molecular knowledge. To generate this knowledge at scale, sequence-based deep learning leverages high-quality genome sequence data to predict variant effects at base-pair resolution. When linked to agronomically important traits, these predictions enable breeders to prioritize variants for precision selection or editing. Although it is still in the early stages of development, we foresee three key applications for this approach: introgressing genes from distant breeding pools, purging deleterious mutations and designing new plant ideotypes. Looking ahead, refined computational models will facilitate targeted editing and the systematic redesign of complex physiological processes to address emerging breeding goals under shifting environmental conditions.

Crops, Agricultural↗
PubMed2026

Uncovering the early and conserved molecular mechanisms of root nitrogen foraging in model and crops.

BACKGROUND: Nitrogen (N) foraging, the ability of plants to promote preferential root growth in N-rich patches of soil, is fundamental to the competitiveness and wellbeing of plants. A unique “split-root” system, where a heterogenous N environment stimulates root foraging, provides a powerful experimental model to study the mechanisms underlying root foraging in model (Arabidopsis) and/or crop plants. RESULTS: We used the split-root set up to capture early molecular events involved in systemic N-signaling after exposure to a heterogeneous N signal, through time-course transcriptomic analysis across shoots and roots of Arabidopsis. We found that a histone methyltransferase, SET DOMAIN GROUP 8 (SDG8), is necessary for root N-foraging, suggesting a previously unknown role for chromatin regulation in mediating the preferential root growth response to colonize N-rich patches. To determine if the underlying molecular mechanism is conserved in evolution, we compared the root foraging behavior from model-to-crop (Arabidopsis, tomato and maize). Our analysis showed the model and crop species shared a root N-foraging growth response, with some variation among specific genotypes. Interestingly, we observed both shared and distinct transcriptional responses to heterogenous N environments among these three species. CONCLUSIONS: Our study has generated insights into the molecular basis of root N-foraging, with the potential to improve nutrient use efficiency in crop plants in a heterogeneous field environment.

Crops, Agricultural↗