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Dispersive soils limit crop growth and significantly impact world food production. Although numerous reviews have examined soil dispersion, many focus on irrigated systems and fail to differentiate the approaches required for rainfed agriculture. This review seeks to fill this gap by focusing on the impact, identification and management of dispersive soils in rainfed areas. Dispersive soils can have large impacts on crop production because of their adverse physical, chemical and biological effects, with this impact particularly large in rainfed systems where irrigation water is unavailable to supplement crop water supply and assist with amelioration. However, the identification of these soils is challenging and tests that can reliably relate soil characteristics to crop performance are lacking. Recent work has found that first identifying consistently lower yielding locations (using yield mapping or proximal/remote sensing) and then using traditional soil testing to identify the potential cause/s of the yield loss may be a promising approach, although this requires refinement. Knowledge of the type of dispersive soil (e.g., saline/non-saline, acidic/alkaline/neutral) and where constraints occur in the profile (surface or subsoil) must also be determined during identification as this will affect management approaches, particularly where multiple constraints need to be treated together to achieve yield increase. Improved understanding of how to economically use ameliorants and combine them to achieve maximum benefit in the presence of multiple constraints is needed. Greater appreciation of how to use agronomic management to improve crop growth in the presence of dispersive behaviour is also likely to increase profitability in rainfed systems where amelioration is often impractical or uneconomical. Dispersive soils are a major challenge for rainfed cropping, and so the refinement of our management approach can help improve profitability and productivity. Highlights: Dispersive soils limit crop growth and significantly impact world food production We examine the impact, identification and management of dispersive soils in rainfed agriculture Improvements in the identification of dispersive soils are required to improve management Refinement of ameliorant use and agronomic management will improve profitability and productivity.

Yield mapping for citrus fruit is a challenging task due to factors such as varying illumination conditions, clustering, and occlusions. Mapping the yield for immature citrus fruit presents an additional challenge that is the colours of fruit and leaves are almost identical. Commonly used machine vision techniques using colour cameras become less effective for immature citrus fruit detection. This study explores a novel active thermal imaging method to tackle the problem of colour similarity between immature citrus fruit and leaves. In this study, a thermal camera was combined with a water spray system that applied water mist to citrus trees. The water mist caused temperatures of both the fruit and leaf surfaces to change but at different rates. Multiple parameters of the spray system were experimented with the goal to induce as much temperature differences as possible between fruit and leaf surfaces. The combined system was tested in a citrus grove for fruit detections. Deep learning models were built based on the active thermal imaging system and tracking and fruit counting algorithms were created to count fruit in thermal videos. A mean average precision of 87.2% was achieved by the models and an accuracy of 96% was achieved when comparing the number of fruit counted by the algorithms with the true number of fruit counted manually in the field.

Sugarcane harvesters have a high level of embedded technology and the opportunity to expand applications to become an important high-density data collection machine. The acquired data, such as yield, losses, and quality, would provide valuable information for site-specific management of sugarcane. This review describes the current instrumentation used by sugarcane harvesters to map yield with the current technologies that have the highest potential applications for losses and quality mapping. In sugarcane cultivation, the harvester cuts and processes the product along a single or double row, which makes it easier to evaluate yield, losses, and quality at the row level. However, studies that explore the holistic potential of the sensors and data processing from the harvester for within-row detailed spatial information are scarce. Over the years, several studies focused on sugarcane yield mapping; however, yield monitors are far from being popular among sugarcane producers due to its low accuracy. Yield monitors need to be robust and accurate; the data generated from default factory-installed sensors is a potential alternative to improve data collection. Also, the integration of the current data available on the harvesters and the use of sensors to estimate sugarcane parameters (i.e., yield, losses, and quality) may represent an important advance in the agricultural operations. This article provides some information regarding the automation of the machines and the operational improvements, such as logistics planning and machine performance.

The goal of this research was to compare traditional and variable rate technology (VRT) nitrogen (N) fertilization in winter wheat (Triticum aestivum L.). The study was developed over two years in two different fields (one field per year). Three different N fertilization approaches applied to the second fertilization were compared by integrating NDVI (Normalized Difference Vegetation Index) data from Sentinel 2 satellites (S2), grain yield, and protein content. In both fields used for the experimentation, the three treatments were defined as follows: 1) a standard rate (Flat-N) derived by an N balance approach; 2) a variable rate based on S2 NDVI, where the maximum rate was equal to the standard rate (Var-N-low); 3) a variable rate based on S2 NDVI, where the average rate was equal to the standard rate (Var-N-high). An inverse linear relationship between NDVI and N-rates was applied to calculate VRT doses on the assumption that NDVI and other correlated VIs, before the second N fertilization, are directly related to crop N nutritional status. Results show that differences between treatments in terms of NDVI, grain yield, and protein content were very low and generally not significant, suggesting that a low-N management approach, even using simple linear models based on NDVI and VRT, may considerably improve the economic and environmental sustainability of N fertilization in winter wheat. Further experiments are necessary to better explore the proposed approaches and compare them, by example, with the NDVI proportional methods that could be more suitable when the crop growth is mainly influenced by limiting factors other than N nutrition status.

Frequent chilling injury has serious impacts on national food security and in northeastern China heavily affects grain yields. Timely and accurate measures are desirable for assessing associated large-scale impacts and are prerequisites to disaster reduction. Therefore, we propose a novel means to efficiently assess the impacts of chilling injury on soybean. Specific chilling injury events were diagnosed in 1989, 1995, 2003, 2009, and 2018 in Oroqen community. In total, 512 combinations scenarios were established using the localized CROPGRO-Soybean model. Furthermore, we determined the maximum wide dynamic vegetation index (WDRVI) and corresponding date of critical windows of the early and late growing seasons using the GEE (Google Earth Engine) platform, then constructed 1600 cold vulnerability models on CDD (Cold Degree Days), the simulated LAI (Leaf Area Index) and yields from the CROPGRO-Soybean model. Finally, we calculated pixel yields losses according to the corresponding vulnerability models. The findings show that simulated historical yield losses in 1989, 1995, 2003 and 2009 were measured at 9.6%, 29.8%, 50.5%, and 15.7%, respectively, closely (all errors are within one standard deviation) reflecting actual losses (6.4%, 39.2%, 47.7%, and 13.2%, respectively). The above proposed method was applied to evaluate the yield loss for 2018 at the pixel scale. Specifically, a sentinel-2A image was used for 10-m high precision yield mapping, and the estimated losses were found to characterize the actual yield losses from 2018 cold events. The results highlight that the proposed method can efficiently and accurately assess the effects of chilling injury on soybean crops.

The accurate and reliable fruit detection in orchards is one of the most crucial tasks for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. However, detecting and counting small fruit is a very challenging task under variable lighting conditions, low-resolutions and heavy occlusion by neighboring fruits or foliage. To robustly detect small fruits, an improved method is proposed based on multiple scale faster region-based convolutional neural networks (MS-FRCNN) approach using the color and depth images acquired with an RGB-D camera. The architecture of MS-FRCNN is improved to detect lower-level features by incorporating feature maps from shallower convolution feature maps for regions of interest (ROI) pooling. The detection framework consists of three phases. Firstly, multiple scale feature extractors are used to extract low and high features from RGB and depth images respectively. Then, RGB-detector and depth-detector are trained separately using MS-FRCNN. Finally, late fusion methods are explored for combining the RGB and depth detector. The detection framework was demonstrated and evaluated on two datasets that include passion fruit images under variable illumination conditions and occlusion. Compared with the faster R-CNN detector of RGB-D images, the recall, the precision and F1-score of MS-FRCNN method increased from 0.922 to 0.962, 0.850 to 0.931 and 0.885 to 0.946, respectively. Furthermore, the MS-FRCNN method effectively improves small passion fruit detection by achieving 0.909 of the F1 score. It is concluded that the detector based on MS-FRCNN can be applied practically in the actual orchard environment.

Vineyards are variable. In spite of this, conventional approaches to winegrape production infer that the optimal practice is to apply uniform management strategies in vineyards on the assumption of homogeneity. The availability of a suite of technologies, which have collectively become known as Precision Viticulture, provides grapegrowers and winemakers with the means to move away from this ‘one-size-fits all’ approach. Instead, management may be targeted within vineyards according to variation in their inherent characteristics and particular goals in terms of grape yield and quality. Thus, better patches can be exploited whilst weaker areas may be improved. This chapter provides a summary and review of recent research into vineyard variability and the development and adoption of Precision Viticulture. A particular feature of this work has been its focus on improved fruit quality and wine flavour and aroma outcomes.

Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems.

Data-driven decisions can be performed based on crop yield values, essential information for precision agriculture practices. Technical solutions for yield mapping have been increasing for the sugarcane crop. However, the adoption of a yield monitor is low among farmers. An alternative is associating the amount of sugarcane harvested with the yield. The objective of this study was to evaluate the accuracy of the sugarcane mass prediction by a hydraulic oil pressure sensor installed in the chopper of the harvester. A commercial sugarcane field was used for the field trial with four harvesters and an in-field wagon instrumented with the load cells. All equipment at the harvesting front were equipped with ZigBee technology for data transfer to the sugar mill's Remote control center. The redistribution of the total weight of sugarcane harvested within each field was based on the chopper hydraulic pressure variation. The yield monitor had a low prediction error (4.5%) compared to the total measured by the in-field wagon. The results suggest enhancing the frequency of data collection by the harvester improves the spatial variability detection of sugarcane yield at the field level. The distribution of the total mass of sugarcane harvested indicated that neither empirical model nor sensors calibration is required to estimate yield regardless of the harvester. In future, the application of telemetry and distribution of the total harvest within the field should be studied for other crops, e.g., grains, which already use this technology for the management of equipment in the field.

Karte prinosa predstavljaju skup informacija, podataka i rezultat poljoprivredne proizvodnje u pojedinom polju, na točno određenoj lokaciji odnosno dijelu polja. Karta prinosa sadrži informacije koje značajno mogu doprinjeti u donošenju racionalnih odluka prilikom proizvodnog procesa pojedine kulture. Prinos usjeva se koristi kao rezultat djelovanja različitih čimbenika koji imaju direktan utjecaj na prinos, kao što su vrsta tla, hranjiva, vlažnost i štetnici. Sustav za mjerenje prinosa sastavljen je od podsustava za pozicioniranje i podsustava za praćenje prinosa. Sustav za praćenje prinosa sastavljen je od niza senzora koji su postavljeni unutar kombajna i hedera. Karte prinosa se izrađuju na temelju informacija koje su nastale praćenjem prinosa, odnosno informacija koje su očitali senzori za praćenje prinosa. Podatci o prinosu se referenciraju s točnom lokacijom u polju koja je izračunata pomoću GNSS uređaja. Karte prinosa se izrađuju u specijaliziranim geoinformacijskim sustavima koji sadrže niz alata koji omogućuju izradu precizne i točne karte prinosa koja pruža relevantne informacije o urodu usjeva na pojedinom položaju te varijabilnost uroda., Yield maps represent a set of information, data and the result of agricultural production in a particular field, at a specific location or part of the field. The yield map contains information that can significantly contribute to making rational decisions during the production process of a particular crop. Crop yield is used as a result of the action of various factors that have a direct impact on yield, such as soil type, nutrients, moisture and pests. The yield measurement system consists of a positioning subsystem and a yield monitoring subsystem. The yield monitoring system consists of a series of sensors placed inside the combine and the header. Yield maps are made on the basis of information generated by yield monitoring, ie information read by yield monitoring sensors. Yield data is referenced with the exact location in the field calculated using the GNSS device. Yield maps are made in specialized geoinformation systems that contain a number of tools that allow the creation of a precise and accurate yield map that provides relevant information on crop yields at a particular location and yield variability.

The bienniality of production and the incidence of pests and diseases, such as coffee leaf miner and coffee leaf rust, stands out among the factors that limit coffee crop yield. Obtaining cultivars with greater stability in production and resistance to these biotic agents are among the main objectives of coffee breeding programs. In this way, biotechnological tools such as Genomic Wide Association Studies (GWAS) can increase these programs' efficacy since they allow the identification of molecular markers significantly associated with phenotypes of interest. In this context, the aim here is to identify genomic regions associated with yield, bienniality, and resistance to coffee leaf miner and coffee leaf rust in arabica coffee progenies. Thus, a population (n=597) was evaluated for resistance to biotic stresses and for the eight designed scenarios to study yield and bienniality. A matrix of 4,666 SNPs (Single Nucleotide Polymorphism) was built through Genotyping by Sequencing (GBS). After the genomic association analyses, we identified 12 potential SNPs markers associated with resistance to coffee leaf miner and coffee leaf rust, 32 associated with the eight designed scenarios to study yield and bienniality. Of the 44 SNPs significantly associated with this study's traits, 36 were noted in genomic regions responsible for biological processes related to plant response to biotic and abiotic stresses. In addition, four markers were coincident with yield and traits related to coffee leaf rust resistance. The genomic regions identified in this study can be incorporated into the coffee breeding program, through assisted selection, leading to more efficient breeding strategies in coffee. Made available in DSpace on 2022-01-19T01:57:04Z (GMT). No. of bitstreams: 1 Association-mapping-reveals-genomic.pdf: 4980072 bytes, checksum: 34399cb2ad7148a41da800f9c630e3bc (MD5) Previous issue date: 2021

Rice grain yield was estimated from a locally made Thai combine harvester using a specially developed sensing and monitoring system. The yield monitoring and sensing system, mounted on the rice combine harvester, collected and logged grain mass flow rate and moisture content, as well as pertinent information related to field, position and navigation. The developed system comprised a yield meter, GNSS receiver and a computer installed with customized software, which, when assembled on a local rice combine, mapped real-time rice yield along with grain moisture content. The performance of the developed system was evaluated at three neighboring (identically managed) rice fields. ArcGIS® software was used to create grain yield map with geographical information of the fields. The average grain yield values recorded were 3.63, 3.84 and 3.60 t ha−1, and grain moisture contents (w.b.) were 22.42%, 23.50% and 24.71% from the three fields, respectively. Overall average grain yield was 3.84 t ha−1 (CV = 63.68%) with 578.10 and 7761.58 kg ha−1 as the minimum and maximum values, respectively. The coefficients of variation in grain yield of the three fields were 57.44%, 63.68% and 60.41%, respectively. The system performance was evaluated at four different cutter bar heights (0.18, 0.25, 0.35 and 0.40 m) during the test. As expected, the tallest cutter bar height (0.40 m) offered the least error of 12.50% in yield estimation. The results confirmed that the developed grain yield sensor could be successfully used with the local rice combine harvester, hence, offers and ‘up-gradation’ potential in Thai agricultural mechanization.

Agriculture is the backbone of India's economy as 70% of the population's household is dependent on agriculture. Crop yield prediction is a crucial step for food security, market planning, and yield mapping. Machine learning with remote sensing has shown magnificent results for crop yield prediction. In this research, the authors implemented machine learning techniques along with the remote sensing data to extract features. Support Vector Machine and K-Nearest Neighbor- techniques have shown outstanding results - are implemented to predict the crop yield using remote sensing data. Authors focused on distinguish parts of a satellite image based on the vegetation for a few months and then finding the relation between their Normalized Difference Vegetation Index values and vegetation similarly, and also found out that vegetation of the same crop field varies over the months and on further computations, we can predict the crop growth in that particular area. For this research, K-Nearest Neighbor outperformed Support Vector Machine.

Crop yield maps are valuable for many applications in precision agriculture, but are often inaccessible to growers and researchers wishing to better understand yield determinants and improve site-specific management strategies. A method for mapping sub-field crop yields from remote sensing imagery could increase the availability of crop yield maps. A variation of the scalable crop yield mapping approach (SCYM, Lobell et al. in Remote Sensing of Environment 164:324–333, 2015) was developed and tested for estimating sub-field maize (Zea mays L.) yields at 10–30 m without the use of site-specific input data. The method was validated using harvester yield monitor records for 21 site-years for irrigated and rainfed fields in eastern Nebraska, USA. Prediction error ranged greatly across site-years, with relative RMSE scores of 10.8 to 38.5%, and R2 values of 0.003 to 0.37. Significant proportional bias was detected in the predictions, but could be corrected with a small amount of ground truth data. Crop yield prediction accuracies without calibration were suitable for some precision applications such as mapping relative yields and delineating management zones, but model improvements or calibration datasets are needed for applications requiring absolute yield estimates.

Current planting technology possesses the ability to increase crop productivity and improve field efficiency by precisely metering and placing crop seeds. Planter performance depends on determining and utilizing optimal settings for different planting variables such as seed depth, down pressure, and seed metering unit. The evolution of “Big Data” in agriculture today brings focus on the need for quality as-planted and yield mapping data. Therefore, an investigation was conducted to evaluate the performance of current planting technology for accurate placement of seeds while understanding the accuracy of as-planted data. Two studies consisting of two different setups on a 6-row, John Deere planter for seeding of maize (Zea mays L.) were conducted. The first study aimed at assessing planter performance at 2 depth settings (25 and 51 mm) and four different down pressure settings (varying from none to high), while the second study focused on evaluating planter performance during variable-rate seeding with treatments consisting of two seed metering units (John Deere Standard and Precision Planting’s eSet setups) with five different seeding rates and four ground speed treatments which provided a combination of 20 different meter speeds. Field data collection consisted of measuring plant emergence, plant population and seed depth whereas plant spacing, plant population after emergence along with distance and location for rate changes within the field were also recorded for the variable-rate seeding study. Results indicated that both depth setting and downforce affected final seeding depth. Measured seed depth was significantly different from the target depth even though time was spent adjusting the units to achieve the desired prior to planting. Crop emergence did not vary significantly for the different depth and downforce settings except for target depth in Field 1. Results from the variable-rate study indicated that seeding rate changes were accomplished within a quick response time (

Electromagnetic induction (EMI) is a non-destructive technique to measure the apparent electrical conductivity (ECa) of agricultural soils. It has become popular in precision agriculture applications to estimate crop yield from ECa. This study was conducted to relate ECa with soil moisture content (θv) and potato tuber yield under Prince Edward Island (PEI), Canada growing conditions and to establish a two-layered soil model for ECa. During year one, horizontal co-planar (HCP), perpendicular co-planar (PRP), and θv were measured with DUALEM-2 sensor and time domain reflectometry (TDR) probe and correlated with yield measured from 12 m2 grids at 40 locations in each of the two potato fields on PEI. During 2018, vertical electrical sounding was undertaken by raising the DUALEM-2 from ground-level at 0.2 m intervals to a height of 1.6 m to investigate variations in ECa with soil depth. A two-layered soil model was developed using generalized reduction gradients to determine the surficial conductivity and thickness, and subsurface conductivity. ECa variables (HCP and PRP), θv, and yield were significantly correlated and remained consistent throughout the study period, indicating their potential in predicting potato tuber yield. ECa in the fields varied both spatially and laterally with depth. GIS interpolated maps of a representative set of data for HCP and tuber yield (2017 data) showed structured spatial patterns and thus feasibility for delineating management zones. Further studies should examine the potato root zone (0.15–0.3 m) ECa specifically for developing higher layered models, which may be useful for accurate prediction of tuber yield. Non-destructive and accurate yield mapping using DUALEM-2 sensor combined with mathematical modelling of soil ECa layering has potential for use in developing management zones in potato fields.

Normalized difference vegetation index (NDVI)-based models have been developed to derive wheat grain yields with multispectral images. In this regard, field measurements and Landsat 8 Opera...

Fruit detection and localization will be essential for future agronomic management of fruit crops, with applications in yield prediction, yield mapping and automated harvesting. RGB-D cameras are promising sensors for fruit detection given that they provide geometrical information with color data. Some of these sensors work on the principle of time-of-flight (ToF) and, besides color and depth, provide the backscatter signal intensity. However, this radiometric capability has not been exploited for fruit detection applications. This work presents the KFuji RGB-DS database, composed of 967 multi-modal images containing a total of 12,839 Fuji apples. Compilation of the database allowed a study of the usefulness of fusing RGB-D and radiometric information obtained with Kinect v2 for fruit detection. To do so, the signal intensity was range corrected to overcome signal attenuation, obtaining an image that was proportional to the reflectance of the scene. A registration between RGB, depth and intensity images was then carried out. The Faster R-CNN model was adapted for use with five-channel input images: color (RGB), depth (D) and range-corrected intensity signal (S). Results show an improvement of 4.46% in F1-score when adding depth and range-corrected intensity channels, obtaining an F1-score of 0.898 and an AP of 94.8% when all channels are used. From our experimental results, it can be concluded that the radiometric capabilities of ToF sensors give valuable information for fruit detection. This work was partly funded by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya, the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (ERDF) under Grants 2017SGR 646, AGL2013-48297-C2-2-R and MALEGRA, TEC2016-75976-R. The Spanish Ministry of Education is thanked for Mr. J. Gené’s predoctoral fellowships (FPU15/03355). We would also like to thank Nufri and Vicens Maquinària Agrícola S.A. for their support during data acquisition, and Adria Carbó for his assistance in Faster R-CNN implementation.

Accurate measurements of maize yields at field or subfield scales are useful for guiding agronomic practices and investments and policies for improving food security. Data on smallholder maize systems are currently sparse, but satellite remote sensing offers promise for accelerating learning about these systems. Here we document the use of Google Earth Engine (GEE) to build “wall-to-wall” 10 m resolution maps of (i) cropland presence, (ii) maize presence, and (iii) maize yields for the main 2017 maize season in Kenya and Tanzania. Mapping these outcomes at this scale is extremely challenging because of very heterogeneous landscapes, lack of cloud-free satellite imagery, and the low quantity of quality ground-based data in these regions. First, we computed seasonal median composites of Sentinel-1 radar backscatter and Sentinel-2 optical reflectance measures for each pixel in the region, and used them to build both crop/non-crop and maize/non-maize Random Forest (RF) classifiers. Several thousand crop/non-crop labels were collected through an in-house GEE labeler, and thousands of crop type labels from the 2015–2017 growing seasons were obtained from various sources. Results show that the crop/non-crop classifier successfully identified cropland with over 85% out-of-sample accuracy in both countries, with Sentinel-1 being particularly useful for prediction. Among the cropped pixels, the maize/non-maize classier had an accuracy of 79% in Tanzania and 63% in Kenya. To map maize yields, we build on past work using a scalable crop yield mapper (SCYM) that utilizes simulations from a crop model to train a regression that predicts yields from observations. Here we advance past approaches by (i) grouping simulations by Global Agro-Environmental Stratification (GAES) zones across the two countries, in order to account for landscape heterogeneity, (ii) utilizing gridded datasets on soil and sowing and harvest dates to setup model simulations in a scalable way; and (iii) utilizing all available satellite observations during the growing season in a parsimonious way by using harmonic regression fits implemented in GEE. SCYM estimates were able to capture about 50% of the variation in the yields at the district level in Western Kenya as measured by objective ground-based crop cuts. Finally, we illustrated the utility of our yield maps with two case studies. First, we document the magnitude and interannual variability of spatial heterogeneity of yields in each district, and how it varies for different parts of the region. Second, we combine our estimates with recently released soil databases in the region to investigate the most important soil constraints in the region. Soil factors explain a high fraction (72%) of variation in predicted yields, with the predominant factor being soil nitrogen levels. Overall, this study illustrates the power of combining Sentinel-1 and Sentinel-2 imagery, the GEE platform, and advanced classification and yield mapping algorithms to advance understanding of smallholder agricultural systems.

Profitability analyses of site-specific nitrogen (N) management strategies have often failed to provide satisfying reasons for adoption of precision farming technologies. However, effects of precision farming on product quality and price premiums, as well as on downside risk mitigation, are generally not taken into account. This study aimed to evaluate the comparative advantages of site-specific N management over uniform N management considering N supply on grain quality, and accordingly price premiums for wheat from a downside risk point of view. A virtual field was modelled with two subfields representing two distinctive yield zones to investigate how consideration of grain quality affects the economic potential of site-specific N management under temporally varying N mineralization and changing price patterns of wheat. Moreover, the extent was investigated to which site-specific N management can have a risk-reducing effect on economic shortfalls compared to uniform N management. Two site-specific N management options were assessed: variable N rate application using yield mapping and N sensor for real-time proximal sensing. Results indicated that even though crop yields were only slightly higher, higher expected protein contents of grains could be achieved with site-specific N management options compared to uniform N management. Baking wheat quality was secured to a greater extent with site-specific N management options. Higher average grain quality improved the economic benefits due to price premiums. A risk-reducing effect was observed with the site-specific N management by maintaining the baking wheat quality with a higher probability. Higher economic returns mostly compensated the additional costs for the precision farming technologies in the lower tail of the probability distribution and, thus, site-specific N management did not show any substantial disadvantage on downside risk as compared to uniform N management.

Yield mapping is one of the most widely used precision farming technologies. However, the value of the maps can be compromised by the presence of systematic and random errors in raw within field data. In this paper, an automated method to clean yield maps is proposed so as to ensure the quality of further data processing and management decisions. First, data were screened by filtering null and edge yield values as well global outliers. Second, spatial outliers or local defective observations were deleted. The local Moran’s index of spatial autocorrelation and Moran’s plot were used as tool to identify the spatial outliers. The protocol to filter out global and local outliers was evaluated on 595 real yield datasets from different grain crops. Significant improvements in the distribution and spatial structure of yield datasets was found. Approximately 30% of the dataset size was removed from each monitor dataset, with one third of the removal occurring during filtering of spatial outliers. The automation of null, edge yield values and the removal of global outliers improved yield distributions, whereas the cleaning of local outliers impacted the yield spatial structure for all yield maps and crops. The algorithm proposed to clean yield maps is easy to apply for preprocessing the growing number of available yield maps.

Even though precision farming (PF) technologies offer apparent on-farm and off-farm benefits, their adoption remains low. A lot of efforts are put towards increasing interest among farmers and other actors and motivating them to use PF techniques. Among others, gamification is a popular method that is being increasingly used not only in agriculture but in many other disciplines. This study describes the implementation of PF principles as an added application (mod) in a farming simulator game. The developed mod includes several new features: RTK base stations, soil types, soil sampling, pH value, site-specific variable rate lime and nitrogen application, yield mapping and economic analysis.

At the CREA research facility of Treviglio (Bergamo, Italy), to provide farmers with valuable hints for the transition from conventional to precision agriculture, information on crop production dynamics (Maize and Triticale) has been obtained using real-time soil mapping (resistivity technique) and production quality and quantity monitoring with a commercial yield mapping apparatus. The geostatistical processing of data resulted in the same zoning for Triticale, meaning that the characteristics of soil influenced crop behavior more than the variability resulting from other factors, which suggests that improvements in product yields can be planned and achieved acting, for instance, on variable rate distribution of fertilizers. The importance of the acquired data can help farmers to manage factors that are external to their plots of land.

This study compares simplified and advanced precision nitrogen (N) fertilization approaches for winter wheat relying on Sentinel-2 NDVI, grain yield maps, and protein content. Five N fertilization treatments were compared: (1) a standard rate, calculated by a typical N balance (Flat-N), (2) a variable rate calculated using a simplified linear model, adopting a proportional strategy (NDVI directly related) (Var-N-dir), (3) a variable rate calculated using a simplified linear model, adopting a compensative strategy (NDVI inversely related) (Var-N-inv), (4) a variable rate calculated using the AgroSat model (Var-N-Agrosat), and (5) a variable rate calculated applying the Agricolus model (Var-N-Agricolus). The study was carried out in four fields over two cropping seasons with a randomized blocks design. Results indicate that the weather remains the main factor influencing yield, as it typically happens in a rainfed crop. No substantial differences in crop yield were observed among the N fertilization models within each year and experimental locations. However, in the more favorable season, the low-input direct model (Var-N-dir) resulted as the best choice, providing the higher NUE (nitrogen use efficiency) value. In the less favorable season, results showed a better performance of the advanced models (Var-N-Agricolus and Var-N-Agrosat), which limited yield losses and reduced intra-field variability, with relevant importance given to the increasing frequency of abnormal climate phenomena. In general, all these VRT approaches allowed reduction of the excess of fertilizers, preservation of the environment, and saving money.

Excessive information, and practical experience, in crop and soil management, and new agriculture technology, have been accumulated over time. To meet the increasing need of humanity for food, a new farming concept known as Precision Farming (PF) or Precision Agriculture (PA) or Site-Specific Land Management (SSLM), is developed in recent years. This concept is a new approach leads to develop the agricultural processes for increasing soil and crop productivity, while saving efforts and costs. Precision farming includes many techniques such as Global Positioning Systems (GPS), Geographic Information Systems (GIS), Remote Sensing (RS), Yield Monitoring, Variable Rate Application (VRA), Yield Mapping, Site-Specific Management Zones (SSMZ) and Crop Modeling. SSMZ is considered as an important factor of PF application. SSMZ delineation can be improved based on the accuracy of determined of soil and crop characteristics which are used for managing and sustaining soil functions. The improvement of land management, at the field scale, will be based on better characterization of soil variability and crop properties within-field. This can be carried out through mapping soil and crop properties with high resolutions, compared with the traditional way. The goal of delineating site-specific management zones (SSMZ) is to get a better explanation of the actual variation within the field. Soil sampling by traditional methods and laboratory analysis are not cost efficient or timely enough. The Grid soil sampling and management zone are found the most important methods for precision farming to collect soil samples. The grid soil sampling is elaborated by dividing a field into a square of cells. However, recent research estimated single soil chemical and physical attribute by using various sensors order to reduce costs and improve management zone delineation. SSMZ map can be produced from a single layer of data, or combinations of different data layers including, topographic attributes map, yield map, soil maps, and soil nutrients maps. The farmer can use SSMZ map to select which production and management strategies plans are required and where they should be placed. Therefore, Implementation of this technique under the status of developing countries is necessary to increase soil and crop production, reduce costs, increase farm profitability and reduce environmental risks and desertification processes (Mohamed in Arab J Geosci 6:4647–4659, 2013 [1]).

Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.

Yield mapping technologies can help to increase the quantity and quality of agricultural production. Current systems only focus on the quantifcation of the harvest, but the quality has equal or greater importance in some perennial crops and impacts directly on the financial proftability. Therefore, a system was developed to quantify and relate the quality obtained in the classifcation line with the plants of the orchard and for decision-making. The system is comprised of hardware, which obtains the location of the harvester bag during harvesting and unloading at the unloading site, and software that processes the collected data. The cloud of real-time data contributed from the diferent collectors (bins) allows the construction of yield maps, considering the multi-stage harvesting system. Further, the system enables the creation of a detailed map of the plants and fruits harvested. As the harvest focuses on quality, it takes place in stages, depending on the ripening of the fruits. In addition to the yield maps, the system allows identifcation of the efciency of each worker undertaking the harvest by the number of performed discharges and by the time spent. The system was developed in partnership with the Federal Technological University of Paraná and Embrapa Uva & Vinho and was tested in apple orchards in southern Brazil. Although the system was evaluated with only data from apple cultivation, monitoring the quality and quantifying other orchard fruits can positively impact the fruit sector. Made available in DSpace on 2021-10-05T17:00:25Z (GMT). No. of bitstreams: 1 Bazzi2021.pdf: 2193189 bytes, checksum: e671ca1af3f51f66dd2fce2c9221eefd (MD5) Previous issue date: 2021