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NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.

2024

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Abstract

Increasing planting densities and nitrogen (N) application rates are two practices commonly used in high-yield maize (Zea mays L.) production systems to increase crop yield, but have resulted in lower N use efficiency, increased lodging, and negative environmental problems. Crop sensing-based precision N management (PNM) strategies have been developed to optimize maize yield, N use efficiency, and reduce environmental footprints, however, PNM strategies to balance grain yield and lodging risks are still very limited. The objectives of this study were to: (1) propose a N nutrition index (NNI)-based algorithm for in-season estimation of maize N demand; and (2) develop a sensor-based PNM strategy to balance grain yield and lodging risk for maize. Field experiments were conducted in Northeast China from 2017 to 2019, using a split-plot design with three planting densities (5.5, 7.0 and 8.5 plants m−2) as main plots and six N rates (0–300 kg ha−1) as subplots. Based on previous studies, a leaf fluorescence sensor Dualex 4 good for estimating plant N concentration and a canopy reflectance sensor Crop Circle ACS 430 good for estimating plant aboveground biomass were used to estimate maize NNI and predict lodging risk. Total N rates to achieve low lodging risk were determined based on wind velocity causing maize stalk lodging and historical actual natural wind speed, as well as the response of a lodging risk indicator (stem failure moment, Bs) to N supply. In-season side-dress N rates were determined based on theoretical amount of preplant N fertilizer estimated using NNI and a target total N rate. The final recommended sidedress N rates were adjusted based on the sensor-predicted lodging risk. The results indicated that NNI could be used for estimating the theoretical amount of preplant N fertilizer required to reach the current N status. It’s feasible to estimate maize side-dress N demand based on the difference of a target total N rate (to achieve an optimal grain yield or low lodging risk) and the current theoretical N supply. Total N rate to ensure low lodging risk was suggested to be adopted under low and medium planting densities. Medium planting density of 70,000 plants ha−1 matched with the corresponding optimal N rate would be recommended for the study area to balance economic return and lodging risk. In general, high planting density is not recommended because it has high lodging risk. More studies are needed to further improve the developed crop sensing-based PNM strategy with more site-years of data and multi-source data fusion using machine learning models for practical on-farm applications.

Abstract

Forest age structure is one of the most important ecological indicators of forest sustainability in terms of biodiversity, forest history, harvesting potentials, carbon storage, and recreational values. The available information on the forest age is most often stand age from forest management plans or national forest inventories. Depending on the definition, stand age is often not a good indicator for the biological age of the dominant trees in a stand. Here, we used 6,998 increment cores from dominant Norway spruce (Picea abies L.) and Scots pine (Pinus sylvestris L.) sampled on National Forest Inventory (NFI) plots throughout Norway to gain a better understanding of the age structure of Norway spruce and Scots pine stands in Norway, and on the relationship between the recorded stand age and the biological age of dominant trees on the NFI plots. In forest with stand ages indicating that the stand was established after the abandonment of selective harvesting in favor of even-aged management dominated by clear-cutting methods (ca.1940 C.E.), we found no systematic difference between the biological age of the sampled trees and the stand age assessed by the NFI. In older stands, there was a large difference between the stand age and the age of the overstory trees with the sampled age trees occasionally being hundreds of years older than the stand age. Our study also reveals that the area of forest with old Norway spruce and Scots pine trees ≥ 160 years old is considerably higher than the corresponding area estimate based on information derived from the stand age only. These results are important as the stand age is often used to characterize status with respect to forest naturalness, biodiversity, guide protection efforts, and describe the appropriate and allowed management activities.