Precision Agriculture最新文献

The design and analysis of on-farm experimentation (OFE) have received growing attention because of the availability of precision machinery that promotes data collection. Even though replicated trials are the most recommended designs, on-farm trials with no replication are used in scenarios where variable rate technology is not available. Despite the abundance of georeferenced data within each plot harvested with yield monitor, treatments are not replicated. This paper presents an approach to statistically analyze unreplicated OFE promoting field-specific inference of treatment effects. Statistical tools for spatial data are coupled with permutation tests to determine the statistical significance between treatment means. The new methodology (OFE-mean test) involves: (1) calculation of effective sample size (ESS) given the underlying spatial structure, (2) ANOVA permutation test on a random sample of ESS, and (3) generation of the empirical distribution of p-values from repetition of step two. The median of this empirical distribution is regarded as the p-value associated with the no treatment effect hypothesis. The OFE-mean test is illustrated using several OFE trials comparing two treatments under different scenarios: with and without treatment differences. Additional assessment is carried out under simulated scenarios with different levels of spatial correlation, variability, and mean differences between treatments. The OFE-mean test had high power to detect mean differences higher than 15% for all spatial structures when total variability was lower than 30%. After treatment effects were removed, no type I error occurred in real data. The test can be easily extended to cover scenarios with more than two treatments. R scripts and sample files to run the OFE-mean test are provided.

Investigating soil properties and yield variability in farming systems is crucial for delineating Management Zones (MZs). The objectives of study were to investigate the spatiotemporal variability of soil properties, identify spatial and temporal yield-limiting factors of soil and delineate MZs based on these factors. This study was conducted at the Xinghua Rice Smart Farm (33.08°E, 119.98°N) in Jiangsu Province, China, and the experiment covered five consecutive years of soil and rice yield testing from 2017 to 2021, with 933 geo-referenced soil samples and 140 rice yield samples collected annually. Soil samples were analyzed for pH, soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), available potassium (AK), and apparent soil conductivity (ECa). Spatial and temporal variability of soil properties and RY were analyzed using statistical and geostatistical methods. Ordinary Kriging (OK) interpolation characterized these distributions, and the random forest (RF) algorithm identified key yield-limiting factors. Subsequently, the effectiveness of using all variables to delineate the MZ was compared against the approach of defining MZs based solely on the identified yield-limiting factors. The study also compared Fuzzy C Means (FCM) and Spatial Fuzzy C-Means (sFCM) clustering to evaluate MZs and their temporal stability. Results showed that the coefficients of variation for soil properties ranged from low to medium (7.7-77.4%), with semi-variational function analyses showing moderate to high spatial dependence for most properties. Temporally, soil nutrients and ECa exhibited a slow increase, whereas pH decreased, showing the highest temporal stability for pH and the lowest for AP. RF analysis identified SOM, TN, and ECa as primary influencers of spatial variability of RY, and SOM, pH, and TN as main contributors to its temporal variability. The integration of yield-limiting factors with the sFCM method improves performance of MZ delineation, maintaining stability over the five-year period.

Rapid rangeland monitoring is critical for implementing management actions effectively and therefore, various remote sensing methods are used for rangeland monitoring. Prices of high-resolution imagery and cloud problems could avoid practicing satellite based-methods. UAV- or ground-based high resolution RGB imagery suggested as an alternative to monitor rangelands. In this study, the performance of smartphone RGB imagery was evaluated over prediction of biomass yield and forage quality of steppe rangelands. Besides, the performance of a mobile application (Canopeo) over rangeland cover was evaluated. RGB band reflection values of smartphone images were determined using a simple open-source software, ImageJ. A total of thirteen different vegetation indices (eleven commonly used and two newly introduced) were estimated and their relations with ground data were evaluated over simple linear and quadratic regression models. AGB and DMY were predicted with moderate accuracy via the newly introduced modified blue-red-green index (MBRGI) (R² = 0.5 for AGB) and recently used normalized difference blue-red index (NDBRI) (R² = 0.46 for DMY) through quadratic regression models. Green leaf index (Gli), visible atmospheric resistant index (Vari), and red green blue vegetation index (RGBVI) gave better results for forage quality predictions among the other VI’s. Gli was an accurate predictor (R² = 0.78) of forage dry matter content. However, prediction performances of VI’s were low for CP (Vari, R² = 0.26), NDF, and ADF contents (RGBVI, R² = 0.31 and 0.37 respectively). Cover data of Canopeo highly correlated both with transect (R² = 0.99) and modified wheel loop (R² = 0.73) data. These results showed that Canopeo might be a useful tool for cover predictions and smartphone-based RGB imagery has good potential for managing rangeland in terms of yield and dry matter content but the accuracy of both yield and forage quality predictions still needs to be improved.

Nitrogen Sufficiency Index (NSI) is an important nitrogen (N) stress indicator for precision N management. It is usually calculated using variables such as leaf chlorophyll meter readings (SPAD) and vegetation indices (VIs). However, no consensus has been reached on the most preferred variable. Additionally, conventional NSI (NSI_uni) calculation assumes N being the sole yield-limiting factor, neglecting other factors such as soil water variability. To tackle these issues, this study compared various variables for NSI calculation and evaluated two new N stress indicators in minimizing the impact of confounding water treatment. The following ground- and aerial-derived variables were compared for NSI_uni calculation: SPAD, sampled leaf and canopy N content (LNC, CNC), LNC and CNC estimated using hyperspectral images acquired by an Unmanned Aerial Vehicle, and three VIs (Normalized Difference Vegetation Index (NDVI), Normalized Red Edge Index (NDRE), and Chlorophyll Index) from the hyperspectral images. Results demonstrated that ground-measured variables outperformed aerial-based variables in deriving N-responsive NSI. Especially, LNC derived NSI_uni responded to N treatment significantly in ten out of thirteen site-date datasets. For the second objective, a modified NSI (NSI_w) and the NDRE/NDVI ratio were compared to NSI_uni. NSI_w reduced water treatment effects in over 80% of the datasets where NSI_uni showed evident impacts. NDRE/NDVI performed similarly to NSI_w, with the notable advantage of not requiring prior knowledge of soil water spatial distribution. This research pioneers the optimization of N stress indicators by identifying the best variables for NSI and mitigating the effects of soil water variability. These advancements significantly contribute to precision N management in complex field conditions.

In-season crop growth and yield prediction at high spatial resolution are essential for informing decision-making for precise crop management, logistics and market planning in horticultural crop production. This research aimed to establish a plant-level cabbage yield prediction system by assimilating the leaf area index (LAI) estimated from UAV imagery and a segmentation model into a crop simulation model, the WOrld FOod STudies (WOFOST). The data assimilation approach was applied for one cultivar in five fields and for another cultivar in three fields to assess the yield prediction accuracy and robustness. The results showed that the root mean square error (RMSE) in the prediction of cabbage yield ranged from 1,314 to 2,532 kg ha^–1 (15.8–30.9% of the relative RMSE). Parameter optimisation via data assimilation revealed that the reduction factor in the gross assimilation rate was consistently attributed to a primary yield-limiting factor. This research further explored the effect of reducing the number of LAI observations on the data assimilation performance. The RMSE of yield was only 107 kg ha^–1 higher in the four LAI observations obtained from the early to mid-growing season than for the nine LAI observations over the entire growing season for cultivar ‘TCA 422’. These results highlighted the great possibility of assimilating UAV-derived LAI data into crop simulation models for plant-level cabbage yield prediction even with LAI observations only in the early and mid-growing seasons.

This study presents the development and the evaluation of a low-cost sensor-based system to optimize the management of surface irrigation at the field level. During a surface irrigation event, water flows according to the slope of the field and it is difficult and time-consuming to predict the optimal time when inflow should be stopped. In such systems, measurement tools are uncommon and those existing are far too complex and expensive to be used as decision support tools on small farms. This article presents the development of an Open Source system, based on low-cost technologies, Internet of Things and LoRaWAN network, that allows: (i) detection of water at the sensor location in the field, (ii) sending an alert by phone to the user and (iii) remote control of surface irrigation gates. The metrological characteristics of the system and its suitability were tested in real conditions during one irrigation season of hay fields in the Mediterranean region. The results highlighted the reliability of the low-cost sensor system for detecting water and transmitting information remotely, with a 100% success rate. Remote control of irrigation gates was successful in 89% of trials carried out in the field, and adjustments resulted in a 100% success rate. The savings in labour time for the farmer and in irrigation water volumes made possible by the use of this system, as well as the inevitable trade-offs between accessibility, reliability and robustness of new technologies for agriculture, are finally discussed.

Introduction

This study explores the integration of precision agriculture technologies (PATs) in rainfed cereal production within semi-arid regions.

Methods

utilizing the Veris 3100 sensor for apparent soil electrical conductivity (ECa) mapping, differentiated management zones (MZs) were established in experimental plots in Valsalada, NE Spain. Site-specific variable dose technology was applied for seed and fertilizer applications, tailoring inputs to distinct fertility levels within each MZ. Emphasizing nitrogen (N) management, the study evaluated the impact of variable-rate applications on crop growth, yield, nitrogen use efficiency (NUE), and economic returns. For the 2021/2022 and 2022/2023 seasons, seeding rates ranged from 350 to 450 grains/m², and basal fertilizer dosages varied between high and low levels. Additionally, the total nitrogen units were distributed differently between the two seasons, while maintaining a uniform topdressing fertilizer dose across all treatments.

Results

Results revealed a significant increase in yield in MZ 2 (higher fertility) compared to MZ 1 (lower fertility). NUE demonstrated notable improvement in MZ 2, emphasizing the effectiveness of variable-rate N applications. Economic returns, calculated as partial net income, showed a considerable advantage in MZ 2 over MZ 1, resulting in negative outcomes for low-fertility areas in several of the analyzed scenarios, and highlighting the financial benefits of tailored input management.

Conclusion

This research provides quantitative evidence supporting the viability and advantages of adopting PATs in rainfed cereal production. The study contributes valuable insights into optimizing input strategies, enhancing N management, and improving economic returns in semi-arid regions.

Olive groves, often characterized by complex topography and highly variable soils, present challenges for delineating irrigation management zones (MZs). This study addresses this issue by examining the relevance of apparent electrical conductivity (ECa), elevation (Z), topographic wetness index (TWI) and time-series of Sentinel-2 NDVI imagery for delimiting MZs for variable rate irrigation (VRI) in a 40-ha olive grove in southern Spain. Principal Component Analysis (PCA) was employed to disentangle olive and grass cover NDVI patterns. PC1 represented the olive tree development patten and showed little relationship with soil properties, while PC2 was associated with the grass cover growth pattern and considered a proxy for water storage-related soil properties that are relevant for irrigation scheduling. An alternative analysis using NDVI percentiles yielded similar results but favored PCA for distinguishing between grass cover and olive tree development patterns. Correlation between NDVI and ECa varied seasonally (r > 0.60), driven by the grass cover dynamics. To assess also possible non-linear relationships, regression trees were used to estimate NDVI percentiles, emphasizing the importance of ECa, ECa_ratio, Z, and slope in predicting different NDVI percentiles. Fuzzy k-means zoning using ECa + Z resulted in four classes that best classified variables that are relevant for irrigation scheduling due to their relationship with soil water storage (e.g. clay content, P_0.95 and PC2). Zonings based on ECa, ECa + Z + TWI and ECa + Z + TWI + NDVI yielded two zones that classified P_0.95 and PC2 well, but not clay content. Therefore, the zoning based on ECa + Z was chosen as optimal in the context of this VRI applications. Our analysis showed how NDVI series can be used in combination with ECa and elevation to evaluate the effectiveness of different zoning approaches for developing VRI prescriptions in olive groves.