Data synthesis for crop variety evaluation. A review

Innovative technology integration: E tongue, near infrared grain tester & machine vision approaches for amylose content & quality characterization

E-tongue, machine vision and NIR systems were used to standardize the quality measurements in twenty rice genotypes grown in Highland Himalayan regions of Kashmir, in order to overcome the constraints of manual measurements. IRCTN-312 showed highest amylose content of 20.74 % and 20.70 % using iodometric method and NIR tester, which was validated by the highest norm value of 34.158 by E-tongue. From these results, genotypes such as GSR-43, GS-103, GSR-23B, GSR-60, SR-4, GSR-46, Koshihikari, GSR-64, GSR-32, GSR-49, GSR-4, GSR-42, GS-459, SKUA-494 and SKUA-540 were classified as low amylose and C-3, K-332, M4-22 and IRCTN-312 were classified as intermediate amylose in the present study. Lowest percentage of damaged grains and chalk ratio was found in GSR-23B. SKUA-494 recorded highest L/W ratio using both the systems. Highest head rice yield and elongation ratio was found in GSR-23B and SKUA-494 genotypes respectively. Highest lightness (L*) value was recorded for Koshihikari genotype.

A comprehensive multivariate approach for GxE interaction analysis in early maturing rice varieties

The genotype evaluation process requires analysis of GxE interactions to ascertain the responsiveness of a genotype to various environments, including the development of early maturing rice. However, the concept of interaction is relatively specific to grain yield. In contrast, grain yield is highly polygenic, so assessment should be carried out with multivariate approaches. Therefore, multivariate assessment in evaluating GxE interactions should be developed, especially for early maturing rice genotypes. The study aimed to develop a comprehensive multivariate approach to improve the comprehensiveness and responsiveness of GxE interaction analysis. The study was conducted in Bone and Soppeng districts, South Sulawesi, Indonesia, in two seasons. The study used a randomized complete block design, where replications were nested across two seasons and locations. Two check varieties and five early maturing varieties were replicated three times in each environment. Based on this study, a new approach to GxE interaction analysis based on multiple regression index analysis, BLUP analysis, factor analysis, and path analysis was considered adequate, especially for evaluating early maturing rice. This approach combined days to harvest, biological yield, and grain yield in multiple linear regression with weighting based on the combination of all analyses. The effectiveness of the GxE interaction assessment was reflected by high coefficient of determination (R2) and gradient (b) values above 0.8 and 0.9, respectively. Inpari 13 (R2 = 0.9; b=1.05), Cakrabuana (R2 = 0.98; b=0.99), and Padjajaran (R2 = 0.95; b=1.07) also have good grain yield with days to harvesting consideration, namely 7.83 ton ha-1, 98.12 days; 7.37 ton ha-1, 95.52 days; and 7.29 ton ha-1, 97.23 days, respectively. Therefore, this index approach can be recommended in GxE interaction analysis to evaluate early maturing rice genotypes. Furthermore, Inpari 13, Cakrabuana, and Padjajaran are recommended as adaptive early maturing varieties.

Economically optimized forage utilization choices in drylands for adapting to economic, ecological, and climate stress

Improving the economic performance of range forage in drylands internationally faces challenges from economic, ecological, and climate stress. Stakeholders in these drylands wish to protect range forage ecosystems while assuring economic viability of ranching. Despite several recent research achievements, little work to date has integrated relationships among precipitation, grazing pressure, animal performance, and forage production to protect ranching incomes faced with economic, ecological, and climate stress in dryland areas. This work addresses that gap by developing an empirical mathematical programming model for optimizing economic performance of livestock grazing on range forage ecosystems that adapt to several stressors. Its unique contribution is to formulate and apply a ranch income optimization model calibrated using positive mathematical programming. The model replicates observed economic, forage, and climate conditions while accounting for interacting relations among stocking rates, forage conditions, grazing pressure, animal performance, and ranch economic productivity. Results show ranch incomes ranging from about $5 to $88 per acre and marginal values of forage ranging from $0.01 to $0.12 per pound of forage, depending on economic, ecological, and climate conditions. Results reveal how all these stressors affect economically optimized choices of grazing levels, ranch income, and economic values of forage for a range of six biomes seen in the US west. Results help livestock ranchers to adjust stocking and forage choices as well as farm policymakers who seek flexible government programs to adapt to changes in economic, ecological, and climate conditions. The work's importance comes from applicability to forage management problems in dry regions internationally.

The tricot approach: an agile framework for decentralized on-farm testing supported by citizen science. A retrospective

Matching crop varieties to their target use context and user preferences is a challenge faced by many plant breeding programs serving smallholder agriculture. Numerous participatory approaches proposed by CGIAR and other research teams over the last four decades have attempted to capture farmers' priorities/preferences and crop variety field performance in representative growing environments through experimental trials with higher external validity. Yet none have overcome the challenges of scalability, data validity and reliability, and difficulties in capturing socio-economic and environmental heterogeneity. Building on the strengths of these attempts, we developed a new data-generation approach, called triadic comparison of technology options (tricot). Tricot is a decentralized experimental approach supported by crowdsourced citizen science. In this article, we review the development, validation, and evolution of the tricot approach, through our own research results and reviewing the literature in which tricot approaches have been successfully applied. The first results indicated that tricot-aggregated farmer-led assessments contained information with adequate validity and that reliability could be achieved with a large sample. Costs were lower than current participatory approaches. Scaling the tricot approach into a large on-farm testing network successfully registered specific climatic effects of crop variety performance in representative growing environments. Tricot's recent application in plant breeding networks in relation to decision-making has (i) advanced plant breeding lines recognizing socio-economic heterogeneity, and (ii) identified consumers' preferences and market demands, generating alternative breeding design priorities. We review lessons learned from tricot applications that have enabled a large scaling effort, which should lead to stronger decision-making in crop improvement and increased use of improved varieties in smallholder agriculture.

Development of a multicriteria decision-making model for evaluating hybrid offspring in the sweetpotato (Ipomoea batatas L.) breeding process

Sweetpotato variety breeding is always a long process. Screening of hybrid offspring is dominated by empirical judgment in this process. Data analysis and decision fatigue have been troubling breeders. In recent years, the low-efficiency screening mode has been unable to meet the requirements of sweetpotato germplasm innovation. Therefore, it is necessary to construct a high-efficiency method that can screen germplasms for different usages, for mining elite genotypes, and to create dedicated sweetpotato varieties. In this article, the multicriteria decision-making (MCDM) model was constructed based on six agronomic traits, including fresh root yield, vine length, vine diameter, branch number, root number and the spatial distribution of storage roots, and five quality traits, including dry matter content, marketable root yield, uniformity of roots, starch content and the edible quality score. Among these, the edible quality score was calculated by using fuzzy comprehensive evaluation to integrate the sensory scores of color, odor, sweetness, stickiness and fibrous taste. The MCDM model was compared with the traditional screening method via an evaluation in 25 sweetpotato materials. The interference of subjective factors on the evaluation results was significantly reduced. The MCDM model is more overall, more accurate and faster than the traditional screening method in the selection of elite sweetpotato materials. It could be programmed to serve the breeders in combination with the traditional screening method.

gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation

To derive insights from data, researchers working on agricultural experiments need appropriate data management and analysis tools. To ensure that workflows are reproducible and can be applied on a routine basis, programmatic tools are needed. Such tools are increasingly necessary for rank-based data, a type of data that is generated in on-farm experimentation and data synthesis exercises, among others. To address this need, we developed the R package gosset, which provides functionality for rank-based data and models. The gosset package facilitates data preparation, modeling and results presentation stages. It introduces novel functions not available in existing R packages for analyzing ranking data. This paper demonstrates the package functionality using the case study of a decentralized on-farm trial of common bean (Phaseolus vulgaris L.) varieties in Nicaragua.

A variety test platform for the standardization and data quality improvement of crop variety tests

Variety testing is an indispensable and essential step in the process of creating new improved varieties from breeding to adoption. The performance of the varieties can be compared and evaluated based on multi-trait data from multi-location variety tests in multiple years. Although high-throughput phenotypic platforms have been used for observing some specific traits, manual phenotyping is still widely used. The efficient management of large amounts of data is still a significant problem for crop variety testing. This study reports a variety test platform (VTP) that was created to manage the whole workflow for the standardization and data quality improvement of crop variety testing. Through the VTP, the phenotype data of varieties can be integrated and reused based on standardized data elements and datasets. Moreover, the information support and automated functions for the whole testing workflow help users conduct tests efficiently through a series of functions such as test design, data acquisition and processing, and statistical analyses. The VTP has been applied to regional variety tests covering more than seven thousand locations across the whole country, and then a standardized and authoritative phenotypic database covering five crops has been generated. In addition, the VTP can be deployed on either privately or publicly available high-performance computing nodes so that test management and data analysis can be conveniently done using a web-based interface or mobile application. In this way, the system can provide variety test management services to more small and medium-sized breeding organizations, and ensures the mutual independence and security of test data. The application of VTP shows that the platform can make variety testing more efficient and can be used to generate a reliable database suitable for meta-analysis in multi-omics breeding and variety development projects.