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

Testing innovations for adoption of newer and more adapted maize varieties

Michael Ndegwa Pieter Rutsaert Jason Donovan Jordan Chamberlin (2023, [Objeto de congreso])

Changing Production Conditions Genetic Innovations Maize Hybrids CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA TESTING MAIZE VARIETIES YIELDS FARMERS EXPERIMENTATION

Enhancing maize yield in a conservation agriculture-based maize (Zea mays)- wheat (Triticum aestivum) system through efficient nitrogen management

C.M. Parihar Hari Sankar Nayak Dipaka Ranjan Sena Shankar Lal Jat Mahesh Gathala Upendra Singh (2023, [Artículo])

This study evaluated the impact of contrasting tillage and nitrogen management options on the growth, yield attributes, and yield of maize (Zea mays L.) in a conservation agriculture (CA)-based maize-wheat (Triticum aestivum L.) system. The field experiment was conducted during the rainy (kharif) seasons of 2020 and 2021 at the research farm of ICAR-Indian Agricultural Research Institute (IARI), New Delhi. The experiment was conducted in a split plot design with three tillage practices [conventional tillage with residue (CT), zero tillage with residue (ZT) and permanent beds with residue (PB)] as main plot treatments and in sub-plots five nitrogen management options [Control (without N fertilization), recommended dose of N @150 kg N/ha, Green Seeker-GS based application of split applied N, N applied as basal through urea super granules-USG + GS based application and 100% basal application of slow release fertilizer (SRF) @150 kg N/ha] with three replications. Results showed that both tillage and nitrogen management options had a significant impact on maize growth, yield attributes, and yield in both seasons. However, time to anthesis and physiological maturity were not significantly affected. Yield attributes were highest in the permanent beds and zero tillage plots, with similar numbers of grains per cob (486.1 and 468.6). The highest leaf area index (LAI) at 60 DAP was observed in PB (5.79), followed by ZT(5.68) and the lowest was recorded in CT (5.25) plots. The highest grain yield (2-year mean basis) was recorded with permanent beds plots (5516 kg/ha), while the lowest

was observed with conventional tillage (4931 kg/ha). Therefore, the study highlights the importance of CA practices for improving maize growth and yield, and suggests that farmers can achieve better results through the adoption of CA-based permanent beds and use of USG as nitrogen management option.

Green Seeker Urea Super Granules CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE UREA YIELDS ZERO TILLAGE NITROGEN

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

Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India

Nand Lal Kushwaha Paresh Shirsath Dipaka Ranjan Sena (2022, [Artículo])

FResampler1 Seasonal Climate Forecasts Decision Support System for Agrotechnology Transfer CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE DECISION SUPPORT SYSTEMS YIELDS RICE RISK MANAGEMENT

Identification of genetic loci for grain yield-related traits in the wheat population Zhongmai 578/Jimai 22

Jingyang Tong Ming Li xianchun xia Zhonghu He Yong Zhang (2023, [Artículo])

Grain Yield KASP Marker QTL Mapping SNP Chip CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GRAIN YIELDS QUANTITATIVE TRAIT LOCI MAPPING SINGLE NUCLEOTIDE POLYMORPHISMS WHEAT BREEDING

International Winter Wheat nurseries data: Facultative and Winter Wheat Observation Nurseries and International Winter Wheat yield trials for semi-arid and irrigated conditions

Mesut KESER fatih ozdemir Pietro Bartolini (2022, [Artículo])

Germplasm Exchange International Nurseries Multi-Locations CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WINTER WHEAT BREEDING GERMPLASM YIELDS DATA

Emmer wheat eco-geographic and genomic congruence shapes phenotypic performance under mediterranean climate

Dana Fuerst SHAILESH YADAV Rajib Roychowdhury Carolina Sansaloni Sariel Hübner (2022, [Artículo])

Emmer Wheat CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT GENETIC VARIATION CLIMATE PHENOLOGY YIELDS MEDITERRANEAN CLIMATE

Not raised ‘to make big decisions’: young people’s agency and livelihoods in rural Pakistan

Patti Petesch Lone Badstue Dil Bahadur Rahut Akhter Ali (2022, [Artículo])

Social Norms Qualitative Research CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE GENDER LIVELIHOODS SOCIOECONOMIC ASPECTS YOUTH AGENCIES

Los manglares: un hogar para los murciélagos

JUAN ANTONIO PIROD ALAYOLA CELIA ISELA SELEM SALAS (2023, [Artículo])

Los manglares albergan una gran diversidad de especies de insectos, peces, aves y mamíferos; entre estos últimos se encuentran los murciélagos (Chiroptera). Los manglares son de gran importancia para la conservación de la biodiversidad, así como también por los servicios ambientales que nos ofrecen y por las especies que albergan. Reconocer la importancia de los manglares para los murciélagos toma relevancia, ya que, al proteger este ecosistema, conservamos la diversidad de murciélagos y su hábitat, razones por las que se han establecido tres áreas con manglar en la península de Yucatán, como importantes para la conservación de los murciélagos.

BIODIVERSIDAD SERVICIOS AMBIENTALES QUIROPTEROS MEXICO YUCATAN BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) ECOLOGÍA VEGETAL ECOLOGÍA VEGETAL