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

Redesigning crop varieties to win the race between climate change and food security

Kevin Pixley Jill Cairns Santiago Lopez-Ridaura Chris Ojiewo Baloua Nébié Godfrey Asea Biswanath Das Benoit Joseph Batieno Clare Mukankusi Sarah Hearne Kanwarpal Dhugga Sieglinde Snapp Ernesto Adair Zepeda Villarreal (2023, [Artículo])

Crop Breeding Expert Survey CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE CROPPING SYSTEMS FOOD SECURITY CROPS

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

Energy and economic efficiency of climate-smart agriculture practices in a rice–wheat cropping system of India

Manish Kakraliya Deepak Bijarniya Parbodh Chander Sharma ML JAT (2022, [Artículo])

Intensive Tillage Conventional Rice–Wheat Systems Energy Efficiency On-Farm Studies Climate-Smart Agricultural Practices CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE-SMART AGRICULTURE RICE WHEAT CROPPING SYSTEMS

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

Multicriteria assessment of alternative cropping systems at farm level. A case with maize on family farms of South East Asia

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

Impact of manures and fertilizers on yield and soil properties in a rice-wheat cropping system

Alison Laing Akbar Hossain (2023, [Artículo])

The use of chemical fertilizers under a rice-wheat cropping system (RWCS) has led to the emergence of micronutrient deficiency and decreased crop productivity. Thus, the experiment was conducted with the aim that the use of organic amendments would sustain productivity and improve the soil nutrient status under RWCS. A three-year experiment was conducted with different organic manures i.e. no manure (M0), farmyard manure@15 t ha-1 (M1), poultry manure@6 t ha-1(M2), press mud@15 t ha-1(M3), rice straw compost@6 t ha-1(M4) along with different levels of the recommended dose of fertilizer (RDF) i.e. 0% (F1), 75% (F2 and 100% (F3 in a split-plot design with three replications and plot size of 6 m x 1.2 m. Laboratory-based analysis of different soil as well as plant parameters was done using standard methodologies. The use of manures considerably improved the crop yield, macronutrients viz. nitrogen, phosphorus, potassium and micronutrients such as zinc, iron, manganese and copper, uptake in both the crops because of nutrient release from decomposed organic matter. Additionally, the increase in fertilizer dose increased these parameters. The system productivity was maximum recorded under F3M1 (13,052 kg ha-1) and results were statistically identical with F3M2 and F3M3. The significant upsurge of macro and micro-nutrients in soil and its correlation with yield outcomes was also observed through the combined use of manures as well as fertilizers. This study concluded that the use of 100% RDF integrated with organic manures, particularly farmyard manure would be a beneficial resource for increased crop yield, soil nutrient status and system productivity in RWCS in different regions of India.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ORGANIC FERTILIZERS YIELDS SOIL PROPERTIES RICE WHEAT CROPPING SYSTEMS

How diverse are farming systems on the Eastern Gangetic Plains of South Asia? A multi-metric and multi-country assessment

Brendan Brown Pragya Timsina Emma Karki (2023, [Artículo])

While crop diversification has many benefits and is a stated government objective across the Eastern Gangetic Plains (EGP) of South Asia, the complexity of assessment has led to a rather limited understanding on the progress towards, and status of, smallholder crop diversification. Most studies focus on specific commodities or report as part of a singular index, use outdated secondary data, or implement highly localized studies, leading to broad generalisations and a lack of regional comparison. We collected representative primary data with more than 5000 households in 55 communities in Eastern Nepal, West Bengal (India) and Northwest Bangladesh to explore seasonally based diversification experiences and applied novel metrics to understand the nuanced status of farm diversification. While 66 crops were commercially grown across the region, only five crops and three crop families were widely grown (Poaceae, Malvaceae, and Brassicaceae). Non-cereal diversification across the region was limited (1.5 crops per household), though regional differentiation were evident particularly relating to livestock and off-farm activities, highlighting the importance of cross border studies. In terms of farmer's largest commercial plots, 20% of systems contained only rice, and 57% contained only rice/wheat/maize, with substantial regional diversity present. This raises concerns regarding the extent of commercially oriented high value and non-cereal diversification, alongside opportunities for diversification in the under-diversified pre-monsoon and monsoon seasons. Future promotional efforts may need to focus particularly on legumes to ensure the future sustainability and viability of farming systems.

Agricultural Production Systems Farming Systems Change CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURAL PRODUCTION CROPPING SYSTEMS DIVERSIFICATION FARMING SYSTEMS SUSTAINABLE INTENSIFICATION

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