Integrating NDVI and agronomic data to optimize the variable-rate nitrogen fertilization

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Nicola Silvestri, Leonardo Ercolini, Nicola Grossi, Massimiliano Ruggeri
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Abstract

The success of Variable Rate Application (VRA) techniques is closely linked to the algorithm used to calculate the different fertilizer rates. In this study, we proposed an algorithm based on the integration between some estimated agronomic inputs and crop radiometric data acquired by using a multispectral sensor. Generally, VRA algorithms are evaluated by comparing the yields, but they can often be affected by factors acting in the final phase of the crop cycle and not dependent on the fertilization treatments. Therefore, we decided to compare our algorithm (ALG) versus the traditional application of fertilizer (TRD) by evaluating the crop growth 1.5 months after the fertilization time. The algorithm was tested on a sorghum crop under organic farming, managed with or without manure. The saving of N obtained with ALG was equal to 14 and 5 kg ha− 1 (-14 and − 10% for the non-manure and fertilized treatments, respectively). The NDVI values acquired after fertilization showed a remarkable reduction of relative standard deviation for ALG system (from 22 to 9% and from 34 to 14% for manured and not manured, respectively), which was not found for TRD system (from 16 to 17% and from 29 to 18% for manured and not manured, respectively). The above ground biomass produced was statistically equivalent for the two systems in the manured plots and significant higher for ALG in not-manured plots (+ 0.74 t ha− 1 of dm, equal to + 23%). Finally, the indices calculated to evaluate the Nitrogen Use Efficiency (NUE) were consistently better in the ALG theses.

Abstract Image

整合 NDVI 和农艺数据,优化变量氮肥施用
可变施肥量(VRA)技术的成功与否与计算不同施肥量的算法密切相关。在这项研究中,我们提出了一种基于农艺投入估算与多光谱传感器获取的作物辐射测量数据相结合的算法。一般来说,VRA 算法是通过比较产量来评估的,但它们往往会受到作物周期最后阶段的因素影响,而与施肥处理无关。因此,我们决定将我们的算法(ALG)与传统施肥方法(TRD)进行比较,在施肥时间后 1.5 个月评估作物生长情况。该算法在有机耕作条件下的高粱作物上进行了测试,无论是否施肥。使用 ALG 所节省的氮分别为 14 和 5 千克/公顷-1(未施肥和施肥处理分别为-14%和-10%)。施肥后获得的 NDVI 值显示,ALG 系统的相对标准偏差显著降低(施肥和不施肥分别从 22% 和 34% 降至 9%),而 TRD 系统则没有这种情况(施肥和不施肥分别从 16% 和 29% 降至 17%)。据统计,在施肥的地块上,两种方法产生的地上生物量相当,而在未施肥的地块上,ALG 方法产生的地上生物量显著较高(+ 0.74 t ha- 1 dm,相当于 + 23%)。最后,为评估氮利用效率(NUE)而计算的指数在 ALG 论文中一直较好。
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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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