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Gerald Blasch (2023, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA REMOTE SENSING WHEAT CROPS DISEASES
Genomic prediction of hybrid crops accounting for non additive genetic effects
David González-Diéguez (2022, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MARKER-ASSISTED SELECTION HYBRIDS MAIZE GENOMES GENETICS GENETIC VARIANCE CROPS
Rachel Voss Jill Cairns Michael Olsen Esnath Tatenda Hamadziripi (2023, [Artículo])
The integration of gender concerns in crop breeding programs aims to improve the suitability and appeal of new varieties to both women and men, in response to concerns about unequal adoption of improved seed. However, few conventional breeding programs have sought to center social inclusion concerns. This community case study documents efforts to integrate gender into the maize-focused Seed Production Technology for Africa (SPTA) project using innovation history analysis drawing on project documents and the authors’ experiences. These efforts included deliberate exploration of potential gendered impacts of project technologies and innovations in the project’s approach to variety evaluation, culminating in the use of decentralized on-farm trials using the tricot approach. Through this case study, we illustrate the power of active and respectful collaborations between breeders and social scientists, spurred by donor mandates to address gender and social inclusion. Gender integration in this case was further facilitated by open-minded project leaders and allocation of funding for gender research. SPTA proved to be fertile ground for experimentation and interdisciplinary collaboration around gender and maize breeding, and has provided proof of concept for larger breeding projects seeking to integrate gender considerations.
Crop Breeding On-Farm Trials Tricot CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENDER CROPS BREEDING ON-FARM RESEARCH SOCIAL INCLUSION CITIZEN SCIENCE MAIZE
UTTAM KUMAR Rajeev Ranjan Kumar Philomin Juliana Sundeep Kumar (2022, [Artículo])
Genomic Selection Single-Trait Genomic Selection Multi-Trait Genomic Selection Genomic Estimated Breeding Value Climate-Resilient Crops CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MARKER-ASSISTED SELECTION CLIMATE CHANGE STRESS CLIMATE RESILIENCE CROPS ABIOTIC STRESS BIOTIC STRESS
Santiago Lopez-Ridaura (2023, [Artículo])
CONTEXT: Integration of farms into markets with adoption of maize as a cash crop can significantly increase income of farms of the developing world. However, in some cases, the income generated may still be very low and maize production may also have strong negative environmental and social impacts. OBJECTIVE: Maize production in northern Laos is taken as a case to study how far can farms' performance be improved with improved crop management of maize with the following changes at field level: good timing and optimal soil preparation and sowing, allowing optimal crop establishment and low weed infestation. METHODS: We compared different farm types' performance on locally relevant criteria and indicators embodying the three pillars of sustainability (environmental, economic and social). An integrated assessment approach was combined with direct measurement of indicators in farmers' fields to assess eleven criteria of local farm sustainability. A bio-economic farm model was used for scenario assessment in which changes in crop management and the economic environment of farms were compared to present situation. The farm model was based on mathematical programming maximizing income under constraints related to i) household composition, initial cash and rice stocks and land type, and ii) seasonal balances of cash, labour and food. The crop management scenarios were built based on a diagnosis of the causes of variations in the agronomic and environmental performances of cropping systems, carried out in farmers' fields. RESULTS AND CONCLUSIONS: Our study showed that moderate changes in crop management on maize would improve substantially farm performance on 4 to 6 criteria out of the 11 assessed, depending on farm types. The improved crop management of maize had a high economic attractiveness for every farm type simulated (low, medium and high resource endowed farms) even at simulated production costs more than doubling current costs of farmers' practices. However, while an improvement of the systems performance was attained in terms of agricultural productivity, income generation, work and ease of work, herbicide leaching, improved soil quality and nitrogen balance, trade-offs were identified with other indicators such as erosion control and cash outflow needed at the beginning of the cropping season. SIGNIFICANCE: Using farm modelling for multicriteria assessment of current and improved maize cropping systems for contrasted farm types helped capture main opportunities and constraints on local farm sustainability, and assess the trade-offs that new options at field level may generate at farm level.
Bio-Economic Farm Model Smallholder Farms CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CASH CROPS INDICATORS SMALLHOLDERS CROPPING SYSTEMS MAIZE
A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm.
Ali Mirzazadeh Afshin Azizi Yousef Abbaspour_Gilandeh José Luis Hernández-Hernández Mario Hernández Hernández Iván Gallardo Bernal (2021, [Artículo])
Estimation of crop damage plays a vital role in the management of fields in the agricultura sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds¿ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of Deep learning-based models to classify other damaged crops.
rapeseed classification damaged crops deep neural networks INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ALIMENTOS
Lesley Boyd sridhar bhavani Cristobal Uauy Annemarie Fejer Justesen Mogens Hovmoller (2022, [Artículo])
Cereals and Grains Pathogen Diversity Puccinia f. sp. tritici Stripe Rust Yellow Rust CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CEREALS FIELD CROPS FUNGI PATHOGENICITY RUSTS TRITICUM AESTIVUM
Frédéric Baudron Ken Giller (2022, [Artículo])
Land Sparing Land Sharing Human-Wildlife Conflicts CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BIODIVERSITY HOUSEHOLD SURVEYS LAND COVER LANDSCAPE MAMMALS CASH CROPS HUMAN-WILDLIFE RELATIONS LAND USE CHANGE
Editorial: Conservation agriculture: knowledge frontiers around the world
Stéphane Cordeau ML JAT Cameron Pittelkow Christian Thierfelder (2023, [Artículo])
No-Tillage Direct Seeding Cover Crops Crop Diversification CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA COVER PLANTS DIVERSIFICATION CROPPING SYSTEMS DIRECT SOWING ZERO TILLAGE