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Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

40-year history: The development of agricultural research in China-a case study from one long-term experimental station

Jiahui Wang, Xi Liu, Shoubing Huang

Article ID: 2945
Vol 5, Issue 3, 2024

DOI: https://doi.org/10.54517/ama.v5i3.2945
VIEWS - 316 (Abstract)

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Abstract

Over the past few decades, advancements in agricultural science and technology have driven significant changes in China’s agriculture. This study uses the Wuqiao Experimental Station, established in 1983 in the North China Plain, as a case study to explore the trajectory of regional agricultural research in China over the past 40 years. By analyzing the keywords of all articles produced at this station, the study reveals shifts in research focus, methods, and agricultural practices over time. The analysis shows that the focus of agricultural research has shifted from yield-centered agricultural production and management (APM) to sustainability-oriented agroecology and environment (AE). Additionally, crop research has deepened, evolving from basic crop management to more detailed studies of plant anatomy, physiology, and advanced methods, including high-throughput sequencing and big data analysis. Despite these advances, concerns have emerged regarding the narrowing of research scope to a limited number of crops, primarily wheat and maize, which may reduce agricultural resilience. Furthermore, while research output has increased, a disconnect persists between scientific research and real-world agricultural challenges, such as heat stress and crop quality. This gap underscores the importance of ensuring that agricultural research remains relevant and addresses pressing issues related to food security and environmental sustainability. This study highlights the need to expand crop diversity, integrate sustainable practices, and adopt cutting-edge research methods to ensure the future resilience of China’s agricultural systems. The findings provide valuable insights into trends and future directions in agricultural research in China, emphasizing the importance of a balanced approach to addressing current and future challenges.

Keywords

Agricultural research process; long-term experimental station; research focus; crop management; climate change

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Zhejiang University, China

Honorary Editor-in-Chief

Cheng Sun

Academician of World Academy of Productivity Science; Executive Chairman, World Confederation of Productivity Science China Chapter, China

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