Appraisal of complementarity of subsurface drip fertigation and conservation agriculture for physiological performance and water economy of maize

C.M. Parihar Hari Sankar Nayak Dipaka Ranjan Sena Renu Pandey Mahesh Gathala ML JAT (2023, [Artículo])

The Indo-Gangetic Plains (IGP) in north-west (NW) India are facing a severe decline in ground water due to prevalent rice-based cropping systems. To combat this issue, conservation agriculture (CA) with an alternative crop/s, such as maize, is being promoted. Recently, surface drip fertigation has also been evaluated as a viable option to address low-nutrient use efficiency and water scarcity problems for cereals. While the individual benefits of CA and sub-surface drip (SSD) irrigation on water economy are well-established, information regarding their combined effect in cereal-based systems is lacking. Therefore, we conducted a two-year field experiment in maize, under an ongoing CA-based maize-wheat system, to evaluate the complementarity of CA with SSD irrigation through two technological interventions–– CA+ (residue retained CA + SSD), PCA+ (partial CA without residue + SSD) – at different N rates (0, 120 and 150 kg N ha-1) in comparison to traditional furrow irrigated (FI) CA and conventional tillage (CT) at 120 kg N ha-1. Our results showed that CA+ had the highest grain yield (8.2 t ha-1), followed by PCA+ (8.1 t ha-1). The grain yield under CA+ at 150 kg N ha-1 was 27% and 30% higher than CA and CT, respectively. Even at the same N level (120 kg N ha-1), CA+ outperformed CA and CT by 16% and 18%, respectively. The physiological performance of maize also revealed that CA+ based plots with 120 kg N ha-1 had 12% and 3% higher photosynthesis rate at knee-high and silking, respectively compared to FI-CA and CT. Overall, compared to the FI-CA and CT, SSD-based CA+ and PCA+ saved 54% irrigation water and increased water productivity (WP) by more than twice. Similarly, a greater number of split N application through fertigation in PCA+ and CA+ increased agronomic nitrogen use efficiency (NUE) and recover efficiency by 8–19% and 14–25%, respectively. Net returns from PCA+ and CA+ at 150 kg N ha-1 were significantly higher by US$ 491 and 456, respectively than the FI-CA and CT treatments. Therefore, CA coupled with SSD provided tangible benefits in terms of yield, irrigation water saving, WP, NUE and profitability. Efforts should be directed towards increasing farmers’ awareness of the benefits of such promising technology for the cultivating food grains and commercial crops such as maize. Concurrently, government support and strict policies are required to enhance the system adaptability.

Net Returns Subsurface Drip Irrigation Subsurface Drip Fertigation CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA EFFICIENCY GRAIN NITROGEN PHOTOSYNTHESIS PHYSIOLOGY WATER SUPPLY CONSERVATION AGRICULTURE CONVENTIONAL TILLAGE FERTIGATION GROUNDWATER NITROGEN-USE EFFICIENCY WATER PRODUCTIVITY

Modeling spatiotemporal patterns of land use/land cover change in Central Malawi using a neural network model

Leah Mungai Joseph Messina Leo Zulu Jiaguo Qi Sieglinde Snapp (2022, [Artículo])

Multilayer Perceptrons CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE LAND USE POPULATION SATELLITE IMAGERY TEXTURE LAND COVER NEURAL NETWORKS REMOTE SENSING

Difusión de cursos que la Fundación Carlos Slim ofrece en aprende.org

Cesar Petroli (2021, [Poster de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA TRAINING AGRICULTURAL TRAINING SOCIAL NETWORKS TRAINING COURSES SUSTAINABLE AGRICULTURE

E. African spring wheat breeding pipeline and Network (CIMMYT-KALRO)

sridhar bhavani (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT PLANT BREEDING RESEARCH NETWORKS

The contribution of strong and weak ties to resilience: The case of small-scale maize farming systems in Mexico

Tania Carolina Camacho Villa Ernesto Adair Zepeda Villarreal Julio Díaz-José Roberto Rendon-Medel Bram Govaerts (2023, [Artículo])

Social Network Analysis Farm Typologies Social Ties Strong Ties CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA INNOVATION NETWORKS PERSISTENCE SOCIAL NETWORK ANALYSIS MAIZE FARMING SYSTEMS

Chapter 9. Genome-informed discovery of genes and framework of functional genes in wheat

awais rasheed Rudi Appels (2024, [Capítulo de libro])

Wheat Genomics KASP Markers Gene Discovery Functional Markers Gene Networks CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT GENOMICS SINGLE NUCLEOTIDE POLYMORPHISMS FUNCTIONAL GENOMICS

Multi-environment genomic prediction of plant traits using deep learners with dense architecture

Osval Antonio Montesinos-Lopez Jose Crossa (2018, [Artículo])

Shared Data Resources Deep Learning Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ACCURACY GENOMICS NEURAL NETWORKS FORECASTING DATA MARKER-ASSISTED SELECTION

Sorghum value chain analysis in semi-arid Zimbabwe

Abbyssinia Mushunje Munyaradzi Junia Mutenje Charles Pfukwa (2019, [Artículo])

Small Scale Farmers Extension Networks CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRO-INDUSTRIAL SECTOR MARKETING MARGINS SORGHUM VALUE CHAINS

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

Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting.

Sajad Sabzi Razieh Pourdarbani Mohammad Hossein Rohban Alejandro Fuentes_Penna José Luis Hernández-Hernández Mario Hernández Hernández (2021, [Artículo])

Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in a non-destructive manner. The goal of the present paper was to identify excess nitrogen in cucumber plants. To obtain a reliable result, the majority voting method was used, which takes into account the unanimity of five classifiers, namely, the hybrid artificial neural network¿imperialism competitive algorithm (ANN-ICA), the hybrid artificial neural network¿harmonic search (ANN-HS) algorithm, linear discrimination analysis (LDA), the radial basis function network (RBF), and the Knearest- neighborhood (KNN). The wavelengths of 723, 781, and 901 nm were determined as optimal wavelengths using the hybrid artificial neural network¿biogeography-based optimization (ANNBBO) algorithm, and the performance of classifiers was investigated using the optimal spectrum. The results of a t-test showed that there was no significant difference in the precision of the algorithm when using the optimal wavelengths and wavelengths of the whole range. The correct classification rate of the classifiers ANN-ICA, ANN-HS, LDA, RBF, and KNN were 96.14%, 96.11%, 95.73%, 64.03%, and 95.24%, respectively. The correct classification rate of majority voting (MV) was 95.55% for test data in 200 iterations, which indicates the system was successful in distinguishing nitrogen-rich leaves from leaves with a standard content of nitrogen.

artificial neural network cucumber hyperspectral imaging majority voting nitrogen INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ALIMENTOS