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

Facile synthesis of a TiO2-Al2O3-GnPs compound and its application in the photocatalytic degradation of Diuron

Alina De J. Zurita Yduarte Diana J. Gallegos Hernández URIEL ALEJANDRO SIERRA GOMEZ GLADIS JUDITH LABRADA DELGADO SALVADOR FERNANDEZ TAVIZON Pedro Jesús Herrera Franco SRINIVAS GODAVARTHI JOSE GILBERTO TORRES TORRES ADRIAN CERVANTES URIBE CLAUDIA GUADALUPE ESPINOSA GONZALEZ (2022, [Artículo])

"New ternary materials TiO2-Al2O3-GnPs (TAG) were prepared by using an innocuous sol-gel method with a slight modification for the addition of graphene nanoplatelets (GnPs), under room temperature and atmospheric pressure. The materials TiO2-Al2O3-GnPs were prepared with variations of concentration between 0.05 and 1 wt % of GnPs. In this study, we analyzed the physicochemical properties by X-ray diffraction (XRD) and UV-Vis spectroscopy, textural properties by N2 physisorption, morphology by scanning and transmission electron microscopy (SEM, TEM) and a chemical species analysis was carried out by X-ray photoelectron spectroscopic (XPS). The photocatalytic activity of each material was evaluated in the degradation of a model molecule, Diuron, a carcinogenic and cytotoxic herbicide used in farm fields. To determine reaction selectivity and mineralization degree, the photocatalytic reaction was monitored by using UV-Vis spectroscopy and total organic carbon (TOC). In samples with higher GnPs’ concentration, a good enough specific surface area of up to 379 m2/g was observed, and reduced band gap energy (2.8 eV) with respect to TiO2 and mixed oxide (3.2 and 3.1 eV respectively), was obtained. These resulting properties were the key indicator so that the materials could be applied as photocatalysts. In the photocatalytic activity determination, TAG-0.75 was the sample that showed the best results with respect to the mixed oxide; the highest photocatalytic conversion, the reduced average life time, and increased mineralization and reaction selectivity."

Graphene nanoplatelets Mixed oxides Sol-gel Photocatalytic degradation BIOLOGÍA Y QUÍMICA QUÍMICA QUÍMICA

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

Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures

GIOVANNY COVARRUBIAS-PAZARAN Hans-Peter Piepho (2023, [Artículo])

Average Semivariance Linear Mixed Model Variance Component Estimation Polygenic Inheritance Oligogenic Inheritance Mendelian Inheritance CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MENDELISM GENETIC VARIANCE GENOME-WIDE ASSOCIATION STUDIES PHENOTYPES CHROMOSOME MAPPING