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Navarrete I, Andrade-Piedra JL, López V, Yue X, Herrera J, Barzallo M, Quimbiulco K, Almekinders CJM, Struik PC. Farmers Experiencing Potato Seed Degeneration Respond but Do Not Adjust Their Seed Replacement Strategies in Ecuador. Am J Potato Res 2022; 100:39-51. [PMID: 36573140 PMCID: PMC9769468 DOI: 10.1007/s12230-022-09893-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 11/11/2022] [Indexed: 06/17/2023]
Abstract
In Ecuador, farmers poorly adopt practices to manage potato seed degeneration. This could be related to the deficient understanding of the farmers' capacity to experience seed degeneration and respond to it. We contribute to this understanding by answering: How do farmers experience seed degeneration?; What practices do farmers implement when their seed is degenerated?; and Is experiencing degeneration the pivotal factor determining how farmers replace their seed regardless their income? We analysed data collected in Ecuador through farmers' focus group discussions, farmers' surveys and interviews, and the Ecuadorian employment status survey. We found that approximately half of the farmers experienced degeneration. Farmers experienced it through low yields, change in seed appearance, crop weakening, and seed physiological problems. When farmers experienced degeneration, they replaced their seed, sought for technical advice, applied more agricultural inputs, or grew other crops. Income was an important trigger for farmers to change their seed replacement practices.
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Affiliation(s)
- Israel Navarrete
- International Potato Center, Quito, Ecuador
- Centre for Crop Systems Analysis, Wageningen University and Research, Wageningen, The Netherlands
- Knowledge, Technology and Innovation, Wageningen University and Research, Wageningen, The Netherlands
- CGIAR Research Program on Roots, Tubers and Bananas (RTB), Lima, Perú
| | - Jorge L. Andrade-Piedra
- CGIAR Research Program on Roots, Tubers and Bananas (RTB), Lima, Perú
- International Potato Center, Lima, Perú
| | - Victoria López
- Instituto Nacional de Investigaciones Agropecuarias del Ecuador, Quito, Ecuador
| | - Xuanyu Yue
- Centre for Crop Systems Analysis, Wageningen University and Research, Wageningen, The Netherlands
| | | | - Mayra Barzallo
- Ministerio de Agricultura y Ganadería, Latacunga, Ecuador
| | - Klever Quimbiulco
- Universidad Técnica de Cotopaxi, Latacunga, Ecuador
- Universidad de Investigación de Tecnología Experimental Yachay, Urcuquí, Imbabura Ecuador
| | - Conny J. M. Almekinders
- Knowledge, Technology and Innovation, Wageningen University and Research, Wageningen, The Netherlands
- CGIAR Research Program on Roots, Tubers and Bananas (RTB), Lima, Perú
| | - Paul C. Struik
- Centre for Crop Systems Analysis, Wageningen University and Research, Wageningen, The Netherlands
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Seely JS, Kaufman MT, Ryu SI, Shenoy KV, Cunningham JP, Churchland MM. Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1. PLoS Comput Biol 2016; 12:e1005164. [PMID: 27814353 PMCID: PMC5096707 DOI: 10.1371/journal.pcbi.1005164] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 09/21/2016] [Indexed: 01/08/2023] Open
Abstract
Cortical firing rates frequently display elaborate and heterogeneous temporal structure. One often wishes to compute quantitative summaries of such structure—a basic example is the frequency spectrum—and compare with model-based predictions. The advent of large-scale population recordings affords the opportunity to do so in new ways, with the hope of distinguishing between potential explanations for why responses vary with time. We introduce a method that assesses a basic but previously unexplored form of population-level structure: when data contain responses across multiple neurons, conditions, and times, they are naturally expressed as a third-order tensor. We examined tensor structure for multiple datasets from primary visual cortex (V1) and primary motor cortex (M1). All V1 datasets were ‘simplest’ (there were relatively few degrees of freedom) along the neuron mode, while all M1 datasets were simplest along the condition mode. These differences could not be inferred from surface-level response features. Formal considerations suggest why tensor structure might differ across modes. For idealized linear models, structure is simplest across the neuron mode when responses reflect external variables, and simplest across the condition mode when responses reflect population dynamics. This same pattern was present for existing models that seek to explain motor cortex responses. Critically, only dynamical models displayed tensor structure that agreed with the empirical M1 data. These results illustrate that tensor structure is a basic feature of the data. For M1 the tensor structure was compatible with only a subset of existing models. Neuroscientists commonly measure the time-varying activity of neurons in the brain. Early studies explored how such activity directly encodes sensory stimuli. Since then neural responses have also been found to encode abstract parameters such as expected reward. Yet not all aspects of neural activity directly encode identifiable parameters: patterns of activity sometimes reflect the evolution of underlying internal computations, and may be only obliquely related to specific parameters. For example, it remains debated whether cortical activity during movement relates to parameters such as reach velocity, to parameters such as muscle activity, or to underlying computations that culminate in the production of muscle activity. To address this question we exploited an unexpected fact. When activity directly encodes a parameter it tends to be mathematically simple in a very particular way. When activity reflects the evolution of a computation being performed by the network, it tends to be mathematically simple in a different way. We found that responses in a visual area were simple in the first way, consistent with encoding of parameters. We found that responses in a motor area were simple in the second way, consistent with participation in the underlying computations that culminate in movement.
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Affiliation(s)
- Jeffrey S. Seely
- Department of Neuroscience, Columbia University Medical Center, New York, NY, United States of America
| | - Matthew T. Kaufman
- Neurosciences Program,Stanford University, Stanford, CA, United States of America
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States of America
| | - Stephen I. Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America
- Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, United States of America
| | - Krishna V. Shenoy
- Neurosciences Program,Stanford University, Stanford, CA, United States of America
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America
- Department of Bioengineering, Stanford University, Stanford, CA, United States of America
- Department of Neurobiology, Stanford University, Stanford, CA, United States of America
- Stanford Neurosciences Institute, Stanford University, Stanford, CA, United States of America
- Howard Hughes Medical Institute Stanford University, Stanford, CA, United States of America
| | - John P. Cunningham
- Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York, NY, United States of America
- Department of Statistics, Columbia University, New York, NY, United States of America
| | - Mark M. Churchland
- Department of Neuroscience, Columbia University Medical Center, New York, NY, United States of America
- Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York, NY, United States of America
- David Mahoney Center for Brain and Behavior Research, Columbia University Medical Center, New York, NY, United States of America
- Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, United States of America
- * E-mail:
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