Plants of the Popol vuh, the sacred book of the Maya

Plantas del Popol Vuh, el libro sagrado de los Mayas

CANDELARIA ISABEL PEREZ MARTIN SIGFREDO EDMUNDO ESCALANTE REBOLLEDO SILVIA VERGARA YOISURA FRANCISCO ALFONSO LARQUE SAAVEDRA (2022, [Artículo])

A compilation of the plants mentioned in the sacred book of the Mayans Popol Vuh, recognized as the framework of their cosmogony that was written in the K’iche’ area in Guatemala around the year 1550 was made. Thirty-two different species were identified, from 21 plant families, all native to Mesoamerica. The largest number of species was in Fabaceae with four species, Moraceae and Solanaceae with three species each, in addition to Bromeliaceae, Cucurbitaceae, Malvaceae and Poaceae with two species each. The five most important and frequently named species are maize (Zea mays), jícaro (Crescentia cujete), copal (Protium copal), rubber (Castilla elastica) and cocoa (Theobroma cacao). Eleven species were identified, such as pito wood (Erythrina berteroana) and zibak (Cyperus canus), considered of cosmogonic significance and food plants that are integrated into five groups: fruit trees [zapote (Manilkara zapota), nance (Byrsonima crassifolia), jocote (Spondias mombin), anona (Annona reticulata), matasano (Casimiroa edulis)], grains and seeds [maize, beans (Phaseolus lunatus), cocoa, pataxte (Theobroma bicolor)], vegetables [squash (Cucurbita moschata), tomato (Solanum lycopersicum), chilacayote (Cucurbita ficifolia)], flavourings [chili (Capsicum annuum)], and beverages [maguey (Agave americana)]. Seven species were domesticated: maize, squash, chilacayote, tobacco (Nicotiana tabacum), beans, tomato and cocoa and others for medicinal purposes, fuel and instruments. The description of the creation of man asserts the close relationship of the Mayan culture with plant biodiversity; moreover, the famous milpa, a multi-species Mesoamerican agroecosystem is mentioned that was practiced by their gods © 2022. Revista Fitotecnia Mexicana.All Rights Reserved.

COSMOGONY MILPA PLANT FAMILIES POPOL VUH SACRED BOOK SPECIES BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) PALEOBOTÁNICA PALEOBOTÁNICA

Optimizing nitrogen fertilizer and planting density levels for maize production under current climate conditions in Northwest Ethiopian midlands

Kindie Tesfaye Dereje Ademe Enyew Adgo (2023, [Artículo])

This study determined the most effective plating density (PD) and nitrogen (N) fertilizer rate for well-adapted BH540 medium-maturing maize cultivars for current climate condition in north west Ethiopia midlands. The Decision Support System for Agrotechnology Transfer (DSSAT)-Crop Environment Resource Synthesis (CERES)-Maize model has been utilized to determine the appropriate PD and N-fertilizer rate. An experimental study of PD (55,555, 62500, and 76,900 plants ha−1) and N (138, 207, and 276 kg N ha−1) levels was conducted for 3 years at 4 distinct sites. The DSSAT-CERES-Maize model was calibrated using climate data from 1987 to 2018, physicochemical soil profiling data (wilting point, field capacity, saturation, saturated hydraulic conductivity, root growth factor, bulk density, soil texture, organic carbon, total nitrogen; and soil pH), and agronomic management data from the experiment. After calibration, the DSSAT-CERES-Maize model was able to simulate the phenology and growth parameters of maize in the evaluation data set. The results from analysis of variance revealed that the maximum observed and simulated grain yield, biomass, and leaf area index were recorded from 276 kg N ha−1 and 76,900 plants ha−1 for the BH540 maize variety under the current climate condition. The application of 76,900 plants ha−1 combined with 276 kg N ha−1 significantly increased observed and simulated yield by 25% and 15%, respectively, compared with recommendation. Finally, future research on different N and PD levels in various agroecological zones with different varieties of mature maize types could be conducted for the current and future climate periods.

Maize Model Planting Density CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE MODELS SPACING NITROGEN FERTILIZERS YIELDS

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Martin van Ittersum (2023, [Artículo])

Context: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective: The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods: A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results: Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion: Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance: The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

Model Accuracy Model Precision Linear Mixed Models CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MACHINE LEARNING SUSTAINABLE INTENSIFICATION BIG DATA YIELDS MODELS AGRONOMY

Shopping for Ecological Indices? On the Use of Incidence-Based Species Compositional Similarity Measures

IAN MACGREGOR FORS FEDERICO ESCOBAR SARRIA JUAN FERNANDO ESCOBAR IBAÑEZ NATALIA MESA SIERRA FREDY ALEXANDER ALVARADO ROBERTO Rafael Rueda Hernández CLAUDIA ELIZABETH MORENO ORTEGA Ina Falfán ERICK JOAQUIN CORRO MENDEZ Eduardo Octavio Pineda Arredondo Amandine Bourg JOSE LUIS AGUILAR LOPEZ (2022, [Artículo])

"β-diversity has been under continuous debate, with a current need to better understand the way in which a new wave of measures work. We assessed the results of 12 incidence-based β-diversity indices. Our results of gradual species composition overlap between paired assemblages considering progressive differences in species richness show the following: (i) four indices (β-2, β-3, β-3.s, and βr) should be used cautiously given that results with no shared species retrieve results that could be misinterpreted; (ii) all measures conceived specifically as partitioned components of species compositional dissimilarities ought to be used as such and not as independent measures per se; (iii) the non-linear response of some indices to gradual species composition overlap should be interpreted carefully, and further analysis using their results as dependent variables should be performed cautiously; and (iv) two metrics (βsim and βsor) behave predictably and linearly to gradual species composition overlap. We encourage ecologists using measures of β-diversity to fully understand their mathematical nature and type of results under the scenario to be used in order to avoid inappropriate and misleading inferences."

Beta diversity Nestedness Replacement Richness difference Species turnover BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) ECOLOGÍA VEGETAL ECOLOGÍA VEGETAL

Polydomus karssenii gen. nov. sp. nov. is a dark septate endophyte with a bifunctional lifestyle parasitising eggs of plant parasitic cyst nematodes (Heterodera spp.)

Samad Ashrafi Abdelfattah DABABAT Maria Finckh Marc Stadler Wolfgang Maier (2023, [Artículo])

Plant Parasitic Nematodes Nematode's Egg Parasites CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ENDOPHYTES NEMATOPHAGOUS FUNGI NEW SPECIES PHYLOGENY PLANT NEMATODES TAXONOMY

Point placement of late vegetative stage nitrogen splits increase the productivity, N-use efficiency and profitability of tropical maize under decade long conservation agriculture

Hari Sankar Nayak C.M. Parihar Shankar Lal Jat ML JAT Ahmed Abdallah (2022, [Artículo])

Non-Linear Growth Model Nitrogen Remobilization Right Placement Precision Nitrogen Management CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GROWTH MODELS NITROGEN NUTRIENT MANAGEMENT

Seed integrity, effect of temperature and storage time on germination of Populus luziarum and P. primaveralepensis, endangered subtropical species from Mexico

César Jacobo Pereira MIGUEL ANGEL MUÑIZ CASTRO JOSE ANTONIO VAZQUEZ GARCIA Joel David Flores Rivas ALEJANDRO MUÑOZ URIAS FRANCISCO MARTIN HUERTA MARTINEZ (2022, [Artículo])

"Background: Populus luziarum and P. primaveralepensis are endemic species of western Mexico; growing in riparian forests they are critically endangered. The best way to conserve their seeds is unknown, which could be limiting for their conservation.

Hypothesis: The germinability of both subtropical species is like that of boreal and template Salicaceae species that disperse seeds in spring and early summer, as they germinate quickly with high percentages, and rapidly lose their viability when stored at ambient temperature.

Studied species: Populus luziarum and P. primaveralepensis. Study site and dates: Western Trans-Mexican Volcanic Belt. Jalisco, Mexico. October 2019.

Methods: The physical integrity of the seeds was assessed by X-ray imaging and compared with germinability. In addition, the effect of storage time (nine weeks) under two temperatures (4 and 21 °C) on the percentage and mean germination rate was evaluated.

Results: No significant differences were found between physical integrity and germination in freshly collected seeds for both species. Germination in the first 24 hrs was 91 and 95 % for Populus luziarum and P. primaveralepensis, respectively (week 0). Germination percentages were lower when stored at 21 °C, but P. primaveralepensis was decreased more slowly.

Conclusions: Seeds of subtropical Populus respond similarly to those of species from temperate and boreal climates with early seed dispersal, a crucial condition for establishing ex situ reforestation and conservation programs."

Salicaceae Seed physical integrity Seed storage conditions Subtropical endemic species White poplars BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) BIOLOGÍA VEGETAL (BOTÁNICA)

Algorithmic differentiation of linear mixed models with variance-covariance structures

Fernando Henrique Toledo Jose Crossa Juan Burgueño Keith Gardner Rosa Angela Pacheco Gil (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MATHEMATICAL MODELS ALGORITHMS LINEAR MODELS

Alternative cropping and feeding options to enhance sustainability of mixed crop-livestock farms in Bangladesh

Timothy Joseph Krupnik Jeroen Groot (2024, [Artículo])

We investigated alternative cropping and feeding options for large (>10 cows), medium (5–10 cows) and small (≤4 cows) mixed crop – livestock farm types, to enhance economic and environmental performance in Jhenaidha and Meherpur districts – locations with increasing dairy production – in south western Bangladesh. Following focus group discussions with farmers on constraints and opportunities, we collected baseline data from one representative farm from each farm size class per district (six in total) to parameterize the whole-farm model FarmDESIGN. The six modelled farms were subjected to Pareto-based multi-objective (differential evolution algorithm) optimization to generate alternative dairy farm and fodder configurations. The objectives were to maximize farm profit, soil organic matter balance, and feed self-reliance, in addition to minimizing feed costs and soil nitrogen losses as indicators of sustainability. The cropped areas of the six baseline farms ranged from 0.6 to 4.0 ha and milk production per cow was between 1,640 and 3,560 kg year−1. Feed self-reliance was low (17%–57%) and soil N losses were high (74–342 kg ha−1 year−1). Subsequent trade-off analysis showed that increasing profit and soil organic matter balance was associated with higher risks of N losses. However, we found opportunities to improve economic and environmental performance simultaneously. Feed self-reliance could be increased by intensifying cropping and substituting fallow periods with appropriate fodder crops. For the farm type with the largest opportunity space and room to manoeuvre, we identified four strategies. Three strategies could be economically and environmentally benign, showing different opportunities for farm development with locally available resources.

Ruminant Feed Pareto-Based Optimization Farm Bioeconomic Model CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUMINANT FEEDING BIOECONOMIC MODELS MIXED CROPPING FARMS LIVESTOCK