Large Agricultural Areas Research Articles

In-field apple size estimation using photogrammetry-derived 3D point clouds: Comparison of 4 different methods considering fruit occlusions

In-field fruit monitoring at different growth stages provides important information for farmers. Recent advances have focused on the detection and location of fruits, although the development of accurate fruit size estimation systems is still a challenge that requires further attention. This work proposes a novel methodology for automatic in-field apple size estimation which is based on four main steps: 1) fruit detection; 2) point cloud generation using structure-from-motion (SfM) and multi-view stereo (MVS); 3) fruit size estimation; and 4) fruit visibility estimation. Four techniques were evaluated in the fruit size estimation step. The first consisted of obtaining the fruit diameter by measuring the two most distant points of an apple detection (largest segment technique). The second and third techniques were based on fitting a sphere to apple points using least squares (LS) and M−estimator sample consensus (MSAC) algorithms, respectively. Finally, template matching (TM) was applied for fitting an apple 3D model to apple points. The best results were obtained with the LS, MSAC and TM techniques, which showed mean absolute errors of 4.5 mm, 3.7 mm and 4.2 mm, and coefficients of determination (R2) of 0.88, 0.91 and 0.88, respectively. Besides fruit size, the proposed method also estimated the visibility percentage of apples detected. This step showed an R2 of 0.92 with respect to the ground truth visibility. This allowed automatic identification and discrimination of the measurements of highly occluded apples. The main disadvantage of the method is the high processing time required (in this work 2760 s for 3D modelling of 6 trees), which limits its direct application in large agricultural areas. The code and the dataset have been made publicly available and a 3D visualization of results is accessible at http://www.grap.udl.cat/en/publications/apple_size_estimation_SfM.

Targeting the survey efforts: Gaps and biases in epiphyte sampling at a biodiversity hotspot

Species distribution data are essential to understanding and protecting biodiversity against current human threats. However, biodiversity knowledge is unevenly distributed geographically and needs to be evaluated to target deficient knowledge locations and support conservation planning. In this study, we intend to assess the inventory completeness of the vascular epiphytes from the Atlantic Forest, in addition to: comparing the sampling effort between (i) phytophysiognomies, (ii) epiphyte taxa, and quantifying (iii) spatial and (iv) environmental biases. We used the average of the last 10% of Species Accumulation Curves (SAC) as the inventory completeness and quantified their spatial biases using Spatial Autoregressive (SAR) models. We evaluated the environmental biases using the Mann-Whitney U test. We found that most epiphyte inventories in the Atlantic Forest are undersampled, with well-sampled inventories concentrated on the southeastern coast; thus, Ombrophilous forests are better sampled than other phytophysiognomies. These well-sampled inventories are spatially biased mainly in large forest cover, protected and low agricultural coverage areas. In addition, epiphytes were more collected in regions environmentally associated with high diversity, such as areas with low temperature, high rainfall and altitudes. More conspicuous and richer groups had the highest sampling coverage, but the lowest effort when compared to poor groups. In order to reduce the gaps, we suggest that future inventories focus on the continental portion of the Atlantic Forest, since these areas are already highly threatened by land-use and have large Wallacean shortfalls for all epiphyte taxa. Therefore, enabling future researchers to propose efficient conservation actions in these priority areas.

Production of biodiesel from oleaginous fungal lipid using highly catalytic bimetallic gold-silver core-shell nanoparticle.

This study aims to synthesize, characterize and apply gold-silver core-shell nanoparticles (Au@Ag NPs), a nanocatalyst, to maximize biodiesel production from fungal isolate Fusarium solani (FS12) via a transesterification one-step reaction. The Au@Ag NPs structure was examined by UV-vis spectrophotometer, transmission electron microscopy, X-ray diffraction and Fourier transform infrared (FTIR). All devices were used to characterize Au@Ag NPs and confirmed successful synthesis of nanoparticles. Fungal lipid was quantitatively determined by sulfo-phospho-vanillin colorimetric method. Among 15 F. solani isolates obtained from rhizospheric soils of the date palm, F. solani (AF12) was chosen as the highly significant producer that accumulates above 20% lipid. The maximum biodiesel yield was 91.28±0.19%, obtained under the optimum reaction conditions of 3% Au@Ag NPs as nanocatalyst concentration, and 1:20 oil to methanol molar ratio at 70℃ for 30min. HPLC method was applied for monitoring in situ transesterification reaction. FTIR spectroscopy was used in qualitative analysis of biodiesel by verifying the presence of unique characteristic peaks of diagnostic significance. The quality of the biodiesel produced was confirmed by the high purity of fatty acid methyl esters analysis content up to >99%. These findings propose the applicability of F. solani (FS12) as a promising isolate to accumulate lipids and biodiesel production as a feedstock. The link between nanotechnology and fungi. Au@Ag NPs were synthesized at room temperature, which displayed high catalytic activity for in situ transesterification reaction. Catalytic activity appeared at low temperature, mole ratio and short reaction time. Oleaginous fungi are described as easily grown, have short life cycle, are cost-effective, and they utilized various sources of carbon up to waste and a simplified process to develop scale-up production as well, economic value, opposite the usage of vegetable oils which need for large agricultural areas.

Integration of Sentinel-derived NDVI to reduce uncertainties in the operational field monitoring of maize

Canopy cover (CC) is a key parameter in calibration and validation of crop growth models, especially those used in operational field monitoring. However, CC direct measurements require intense field campaigns, increasing the cost in time-series data acquisition for large agricultural areas. Normalized Difference Vegetation Index (NDVI) is a commonly used remote-sensing vegetation index, expressing crop water-status, being indirectly related to CC. In this paper, we explore the relationship between on-site CC and the high-resolution NDVI data acquired via Sentinel 2 products. This relationship was utilized to produce CC time series over the cultivation period in four maize fields in northern and central Greece. Subsequently, the expression linking CC and NDVI was used to operationally validate CC change in a crop model capable to simulate the maize growth cycle (AquaCrop). The proposed method involves the dynamic in-season re-adjustment to a number of key model input parameters, based on the remotely acquired CC time series, namely maximum CC, canopy growth and decline coefficient, growing degree days needed to the beginning of senescence stage. These re-adjusted parameters were imported to model’s crop file to improve simulations in CC, soil water content, final biomass and yield. Results showed that the remotely acquired CC time series could be successfully used as an alternative mean to validate CC simulations. Moreover, the ingestion of re-estimated parameters to crop file, improved model’s capability to simulate CC (R2 >0.98; RMSE<5.12%), biomass (Pe<12%) and yield (Pe<12%). No significant differences were observed in model’s performance regarding soil water content simulation.

Ca and Sr isotope constraints on chemical weathering processes: A view through the Ebro river basin, Spain

The Ebro River (928 km long, 85,530 km2 drainage basin) in north-eastern Spain flows into the western Mediterranean Sea through several large cities, and agricultural, mining and industrial areas. The river is one of the largest contributors of freshwater to the Mediterranean and ends in the Ebro delta, one of the most important wetlands in Europe. Bedrock of the Ebro River basin mainly consists of carbonate rocks and evaporites of Paleozoic and Mesozoic age. The Ebro river mainstream was sampled monthly for a year (2006) near the outlet at Amposta, and a field campaign in April of the same year sampled the Ebro along its main course as well as its main tributaries. The behaviour of Ca and its isotopes—combined with that of Sr-isotopes—during water/rock interactions was investigated at the basin scale, with the objective of elucidating the processes controlling the isotope signatures of a large river draining mostly sedimentary bedrock. The δ44Casw values (normalized to seawater) ranged from −0.87 to −1.09‰ along the Ebro mainstream, decreasing toward the delta as the Ca content increased. At Amposta, the δ44Casw ratio ranged between −0.66 and − 1.04‰, with a tendency to decrease with increasing discharge. These variations were similar to those given by the 87Sr/86Sr values and Sr contents. For the main tributaries, the δ44Casw ratio in river waters ranged between −0.43 and − 1.04‰, whereas anhydrite-gypsum bedrock values ranged from −0.94 to −1.22‰ and carbonate bedrock from −1.04 to −1.39‰. Comparing Sr-isotope values and Ca/Na ratios showed the role of anhydrite/halide bedrock weathering for some tributaries (Guadalope, Matarrana, Aragon, Ega), that of carbonate/halide bedrock weathering dominating the others (Gallego, Cinca, Segre), the Ebro mainstream being a mix of both. Bedrock weathering masks the sea-salt signal, if any. As halides contain no Ca, a comparison of δ44Casw and 87Sr/86Sr values further demonstrated the role of anhydrite and limestone for the Ebro and its tributaries, highlighting geochemical processes like carbonate supersaturation in the Guadalope and Matarrana tributaries.

Establishing almond orchards in karstic and water-limited central Taurus Mountain region of southern Turkey

Irrigable farmland is very limited in the Central Taurus Mountain region of Turkey. Most of the rain-fed land has been used for cultivation of wheat and chickpeas. Despite the increase in input costs in recent years, wheat and chickpea prices have not increased in parallel and therefore, large agricultural areas have been left empty. Almond performs well in such karstic areas of water scarcity. The introduction of new high-yield American, Spanish, and French varieties offers an opportunity to the local farmers. However, the terrain is highly fragmented and most of the plots are used for subsistence farming. Therefore, establishing an orchard is a very expensive prospect for low-income villagers. The stages of establishing an almond orchard in the karstic area in the Taurus Mountain region of southern Turkey are explained in this study and the approximate costs are summarized. Establishing a 1-ha almond orchard was estimated to cost about 45,000 Turkish liras, based on 2017–2018 figures. Even though expenses for establishing an almond orchard may vary among sites depending on the terrain, factors such as seedling variety, fencing, and site preparation (involving soil excavation) make up about 45% of the total cost.

Vegetable Cultivation with Hydroponic System Nutrient Film Technique In Karang Taruna Gendowang Village

The result of initial survey in Gendowang Village, Moga District, Pemalang regency is an area with a large potential agricultural area with a total land of 82 ha more and the average population is farming, so it is very potential in developing agricultural systems with hydroponic systems. Enthusiastic citizens are seen at the time of socialization activities. The initial method of applying this technology is an approach to the community, then socialization and workshop. The method used in developing this agricultural system is the application of hydroponic methods with NFT systems. The result is by increasing knowledge and skills in the insertion of this method by 43.95%. &#x0D;

Новая технология обработки вегетирующих растений

A new technology for processing vegetative plants on large agricultural areas at night with a UAV is described. The use of an unmanned aerial vehicle as a mobile platform with a device attached to it for stimulating non-thermal intensity of vegetative plants with optical laser radiation at various stages of ontogenesis contributes to the development of plants at the initial stages of vegetation and, as a consequence, at later stages of growth, increasing their resistance to certain diseases and productivity., which allows at the stage of storage to significantly increase the safety of the crop without additional investment in the vegetable store.

Time-weighted dynamic time warping analysis for mapping interannual cropping practices changes in large-scale agro-industrial farms in Brazilian Cerrado

Methods for crop phenology detection using time series analysis have provided accurate information for large agricultural areas in shorter processing times, which can be useful for agronomic management and supply chain monitoring. Given the crop dynamics in the Brazilian Cerrado, with alternating crop type plantings, crop successions, and crop rotations, as well as climate and crop practices variation between harvest periods, these methods can be useful for detecting subtle land use and land cover changes at farm and crop field scales, improving thematic classifications and the near real-time crop monitoring. In this study, the Time-Weighted Dynamic Time Warping method was applied to recognize patterns in Moderate Resolution Imaging Spectroradiometer (MODIS) time series for land use and cover classification, identifying crop successions and rotations at crop field level in a large-scale agro-industrial agglomerate of farms located at Brazilian Cerrado. We detected and analyzed temporal cropping patterns in training samples to classify the MODIS time series and images, using a robust ground truth data set for validation. The method distinguished Cotton-fallow, Soybean-cotton, Soybean-maize, and Soybean-millet cropping patterns with an overall accuracy above 85% for all evaluated harvest periods. Seasonal variations in the crop fields, caused by interannual succession and rotation, were detected. The method demonstrated the benefit of creating a spatial vector data set for supporting decision-making in several crop management contexts, improving crop and supply chain monitoring.

Malaria in migrant agricultural workers in western Ethiopia: entomological assessment of malaria transmission risk

BackgroundEthiopia has made great strides in malaria control over the last two decades. However, this progress has not been uniform and one concern has been reported high rates of malaria transmission in large agricultural development areas in western Ethiopia. Improved vector control is one way this transmission might be addressed, but little is known about malaria vectors in this part of the country.MethodsTo better understand the vector species involved in malaria transmission and their behaviour, human landing collections were conducted in Dangur woreda, Benishangul-Gumuz, between July and December 2017. This period encompasses the months with the highest rain and the peak mosquito population. Mosquitoes were identified to species and tested for the presence of Plasmodium sporozoites.ResultsThe predominant species of the Anopheles collected was Anopheles arabiensis (1,733; i.e. 61.3 % of the entire Anopheles), which was also the only species identified with sporozoites (Plasmodium falciparum and Plasmodium vivax). Anopheles arabiensis was collected as early in the evening as 18:00 h-19:00 h, and host-seeking continued until 5:00 h-6:00 h. Nearly equal numbers were collected indoors and outdoors. The calculated entomological inoculation rate for An. arabiensis for the study period was 1.41 infectious bites per month. More An. arabiensis were collected inside and outside worker’s shelters than in fields where workers were working at night.ConclusionsAnopheles arabiensis is likely to be the primary vector of malaria in the agricultural development areas studied. High rates of human biting took place inside and outdoor near workers’ residential housing. Improved and targeted vector control in this area might considerably reduce malaria transmission.

Improving the monitoring of corn phenology in large agricultural areas using remote sensing data series

Aim of study: Mexico's large irrigation areas demand non-structural actions to improve the irrigation service, such as monitoring crop phenology; however, its application has been limited by the large volumes of field information generated, diversity of crop management and climatic variability. The objective of this study was to generate and validate a methodology to monitor corn (Zea mays L.) phenology from the historical relationship of the vegetation indexes (VIs), EVI and NDVI, with the phenological development (PD) of corn grown in large irrigation zones.Area of study: Irrigation District (ID) 075 “Valle del Fuerte”, northern Sinaloa, Mexico.Material and methods: We used a database of 20 years of climate, field crop growth and crop phenology data, and Landsat satellite images. A methodology was proposed on a large scale supported with GIS and remote sensing data series.Main results: The methodology was validated in 19 plots with an acceptable correlation between observed PD and estimated PD for the two VIs, with slightly better values for EVI than for NDVI. NDVI and EVI models agreed with experimental PD observations in 92.1% of the farms used to validate the methodology, in 2.5% only the NDVI model coincided with the real, in 3.1% only the EVI model coincided, and in 2.3% both models disagreed with observation, generated a stage out of phase with respect to the real phenological stage.Research highlights: is possible to generalize the methodology applied to large irrigation zones with remote sensing data and GIS.

AgroTutor: A Mobile Phone Application Supporting Sustainable Agricultural Intensification

Traditional agricultural extension services rely on extension workers, especially in countries with large agricultural areas. In order to increase adoption of sustainable agriculture, the recommendations given by such services must be adapted to local conditions and be provided in a timely manner. The AgroTutor mobile application was built to provide highly specific and timely agricultural recommendations to farmers across Mexico and complement the work of extension agents. At the same time, AgroTutor provides direct contributions to the United Nations Sustainable Development Goals, either by advancing their implementation or providing local data systems to measure and monitor specific indicators such as the proportion of agricultural area under productive and sustainable agriculture. The application is freely available and allows farmers to geo-locate and register plots and the crops grown there, using the phone’s built-in GPS, or alternatively, on top of very high-resolution imagery. Once a crop and some basic data such as planting date and cultivar type have been registered, the application provides targeted information such as weather, potential and historical yield, financial benchmarking information, data-driven recommendations, and commodity price forecasts. Farmers are also encouraged to contribute in-situ information, e.g., soils, management, and yield data. The information can then be used by crop models, which, in turn, send tailored results back to the farmers. Initial feedback from farmers and extension agents has already improved some of the application’s characteristics. More enhancements are planned for inclusion in the future to increase the application’s function as a decision support tool.

Integrating watershed and ecosystem service models to assess best management practice efficiency: guidelines for Lake Erie managers and watershed modellers

Lake Erie is the shallowest and most biologically productive system of the Great Lakes, surrounded by large urban, industrial, and agricultural areas. This combination prompted extensive efforts to promote best management practices (BMPs) to mitigate non-point source pollution in Lake Erie’s watershed. Recent technical and conceptual advancements caution that significant variability exists in the BMP efficiency to reduce the severity of runoff and nutrient concentrations due to differences in implementation, the dependence of operational performance on local soil and climatic conditions, storm events and seasonality, and declining performance over time owing to imperfect maintenance. Given the uncertainty surrounding the design and efficiency of BMPs in abating non-point source pollution, our primary objective is to review the critical strengths and potential weaknesses of nine agricultural BMPs promoted for use in the Lake Erie watershed. We examine the capacity of the current generation of watershed process-based models to recreate possible BMP-mediated changes in the water and nutrient cycles. After reviewing modelling strategies (dynamic, external forcing, and empirical) to recreate non-linear watershed responses and feedback loops to BMP efficiency, our study recommends adopting dynamic representations of the interplay among key mechanisms, like soil moisture, water table, nutrient availability, plant uptake, and subsequent growth. Notwithstanding the increased sophistication of complex mathematical models, their learning capacity is usually compromised by the coarse resolution of environmental data and limited empirical knowledge to accurately parameterize environmental properties and partially understood biogeochemical processes. Moreover, BMPs may differentially affect the provisions of an ecosystem (e.g., a BMP may amplify one ecosystem service while dampening another). In this context, we highlight the expression of the value of ecosystem services in monetary and non-monetary terms as a critical information piece when considering trade-offs among costly and diverse policy decisions. Our study also examines the degree to which different types of valuation methods, socioeconomic models, and data have been operationalized in Lake Erie. Consistent with the Integrated Watershed Management framework, we advocate the adoption of a rigorous mass-balance approach to assess the impact of BMPs on nutrient cycles, as well as the integration of the projected environmental improvements with terrestrial ecosystem services, beneficial use impairments, and aquatic ecosystem services. The proposed strategy has the potential to improve the decision-making process by identifying cost-effective management actions and balancing different goods and services provided by the agroecosystems at different time scales.

UAV Path Optimization for Precision Agriculture Wireless Sensor Networks.

The use of monitoring sensors is increasingly present in the context of precision agriculture. Usually, these sensor nodes (SNs) alternate their states between periods of activation and hibernation to reduce battery usage. When employing unmanned aerial vehicles (UAVs) to collect data from SNs distributed over a large agricultural area, we must synchronize the UAV route with the activation period of each SN. In this article, we address the problem of optimizing the UAV path through all the SNs to reduce its flight time, while also maximizing the SNs’ lifetime. Using the concept of timeslots for time base management combined with the idea of flight prohibition list, we propose an efficient algorithm for discovering and reconfiguring the activation time of the SNs. Experimental results were obtained through the development of our own simulator—UAV Simulator. These results demonstrate a considerable reduction in the distance traveled by the UAV and also in its flight time. In addition, the model provides a reduction in transmission time by SNs after reconfiguration, thus ensuring a longer lifetime for the SNs in the monitoring environment, as well as improving the freshness and continuity of the gathered data, which support the decision-making process.

Field Comparison of Electrical Resistance, Electromagnetic Induction, and Frequency Domain Reflectometry for Soil Salinity Appraisal

By using different physical foundations and technologies, many probes have been developed for on-site soil salinity appraisal in the last forty years. In order to better understand their respective technical and practical advantages and constraints, comparisons among probes are needed. In this study, three different probes, based on electrical resistance (ER), electromagnetic induction (EMI), and frequency domain reflectometry (FDR), were compared during a field survey carried out in a large salt-threatened agricultural area. Information about the soil bulk electrical conductivity (σb) at different depths was obtained with each of the probes and, additionally, other soil properties were also measured depending on the specifications of each instrument and, moreover, determined in samples. On average, the EMI and FDR techniques could be regarded as equivalent for σb measurement, whereas ER gave higher σb values. Whatever the case, EMI, and also ER, had to be supplemented with information about soil clay, organic matter, and water mass fractions to attain, despite this effort, poor soil salinity estimations by means of multiple linear regression models (R2 &lt; 0.5). On the contrary, FDR needed only probe data to achieve R2 of 0.7, though root mean standard error (RMSE) was still 1.5 dS m−1. The extra measurements and calculations that modern electrical conductivity contact probes integrate, specifically, those based on FDR, remarkably increase their ability for soil salinity appraisal, although there is still room for improvement.

ESTIMATING IRRIGATION WATER USE FOR DATE PALM USING REMOTE SENSING OVER AN OASIS IN ARID REGION

Date palm needs sufficient water of acceptable quality to reach its potential yield. The present study conducted in Al-Hassa Oasis located in the Eastern Region of the Kingdom of Saudi Arabia aiming to estimate the daily, monthly and annual actual evapotranspiration (ETa) for date palm using Landsat-8 satellite data during 2017/2018. Also, an attempted was made to compare between the computed ETa and the actual water applied in the field. The Surface Energy Balance Algorithm for Land (SEBAL) supported by climate data was used to calculate the ETa. The SEBAL model outputs were validated using the FAO Penman-Monteith method coupled with field observation and measurements. The results showed that the highest daily ETa value observed during the summer season was 9 mm.day−1, and the lowest value was 2 mm.day−1 in winter. The mean monthly water applied in the farms was 15% higher than that suggested by SEBAL during the peak summertime. The annual ETa varied between 800 and 1,400 mm.year−1, while the annual irrigation requirement for date palm was in the range of 11000 – 13000 m3.ha−1. The validation measure showed a significant agreement level between the SEBAL model and the FAO Penman-Monteith method with RMSE of 0.84 mm.day−1. The study concludes that the ETa calculated from the satellite data and the SEBAL model is useful for guiding the daily operation of date palm water management at the farm scale. Also, this information is essential for water planners and policymakers to formulate strategies and make decisions for managing water resources over large agricultural areas.

Humic substances reduce the erodibility of soils in mining areas

The management of mineral processing wastes is a fundamental issue in the cleaner production and sustainable development of mining industry. This work demonstrates that the humic substances obtained from the recycling of mineral coal tailings can minimize the environmental impact associated with the exploitation of ornamental rocks and could be used in the recovery process for large agricultural areas that are currently degraded. Results of disaggregation tests with specimens of different ages reveal that the incorporation of the humic substrate results in greater stability of the solid particles of the material due to insoluble two- or three-dimensional structures that act as cementing agents by increasing the tensile strength of the solid matrix. It was also shown an increase in the tensile strength in specimens that were subjected to a long period of storage (i.e., the strength in test specimens with 5% of humic substances as compared to no incorporation of humic substances doubled in a period of 1 year, from 3.23 KPa to 6.74 KPa). Moreover, the stability of long-term humic substrate seems to be an advantage over products of easy degradation that only confer an intense effect on the initial stability of aggregates after incorporation, which is a short-term effect. Results from optic and electron microscopy corroborate the above statement. Moreover, results from microscopy clearly show that the effect of the incorporation of humic substance is more apparent over a longer period of time. The results of this study may be useful for other countries where mining industry causes erodibility of soils.

Predicting Soybean Yield at the Regional Scale Using Remote Sensing and Climatic Data

Crop yield modeling at the regional level is one of the most important methods to ensure the profitability of the agro-industrial economy and the solving of the food security problem. Due to a lack of information about crop distribution over large agricultural areas, as well as the crop separation problem (based on remote sensing data) caused by the similarity of phenological cycles, a question arises regarding the relevance of using data obtained from the arable land mask of the region to predict the yield of individual crops. This study aimed to develop a regression model for soybean crop yield monitoring in municipalities and was conducted in the Khabarovsk Territory, located in the Russian Far East. Moderate Resolution Imaging Spectroradiometer (MODIS) data, an arable land mask, the meteorological characteristics obtained using the VEGA-Science web service, and crop yield data for 2010–2019 were used. The structure of crop distribution in the Khabarovsk District was reproduced in experimental fields, and Normalized Difference Vegetation Index (NDVI) seasonal variation approximating functions were constructed (both for total district sown area and different crops). It was found that the approximating function graph for the experimental fields corresponds to a similar graph for arable land. The maximum NDVI forecast error on the 30th week in 2019 using the approximation parameters according to 2014–2018 did not exceed 0.5%. The root-mean-square error (RMSE) was 0.054. The maximum value of the NDVI, as well as the indicators characterizing the temperature regime, soil moisture, and photosynthetically active radiation in the region during the period from the 1st to the 30th calendar weeks of the year, were previously considered as parameters of the regression model for predicting soybean yield. As a result of the experiments, the NDVI and the duration of the growing season were included in the regression model as independent variables. According to 2010–2018, the mean absolute percentage error (MAPE) of the regression model was 6.2%, and the soybean yield prediction absolute percentage error (APE) for 2019 was 6.3%, while RMSE was 0.13 t/ha. This approach was evaluated with a leave-one-year-out cross-validation procedure. When the calculated maximum NDVI value was used in the regression equation for early forecasting, MAPE in the 28th–30th weeks was less than 10%.

Maximizing detection probability for effective large‐scale nocturnal bird monitoring

AbstractAimOur specific objectives were to (a) estimate detection probabilities of nocturnal bird species, after taking into account survey‐specific covariates, and (b) investigate the influence of site‐specific covariates on owl and nightjar abundance, integrating effects of imperfect detection.InnovationWe conceived a survey protocol to estimate probabilities of detection and estimates of abundance of owls and nightjars in a large area, the Basque Country, northern Spain.Main ConclusionsOur results show that detection probability was strongly influenced by playback broadcast and by observer experience. Date irregularly affected species according to their reproductive periods, and we also found that vocal activity gradually diminished proportionally to the hour after sunset. Tawny owl (Strix aluco) was the most abundant and widely distributed species. Its abundance was positively related to forest areas (mainly pine timber forests) and decreased in large urban and agricultural areas. Open space species were less common. Barn owls (Tyto alba), little owls (Athene noctua), Eurasian scops owls (Otus scops) and long‐eared owls (Asio otus) avoided forest areas, but showed different responses to agriculture, grass‐fields, scrub and urban areas. Finally, European nightjar (Caprimulgus europaeus) was moderately frequent, and its abundance was favoured by scrub areas and, weakly, by eucalyptus patches, whereas it was negatively affected by large forest areas. We have shown that it is fundamental to consider the effects of survey‐specific covariates in the methodology design and analytical development. Our results also indicate some ecological adaptations and population changes in the nocturnal bird community following an increase in urbanization and in the extent of timber plantations, and also the simplification of natural habitats.

Benefits of literation information against improvement of mushroom agriculture in Rambipuji, Jember District

Agriculture is the most important sector in economic life in an area. Jember Regency is the area with the largest agricultural area. One of the agricultural products in the Jember region is Mushroom. The mushroom cultivation system is very dependent on the season so that it results in instability of farmers’ income. This study aims to determine: 1) The condition of mushroom farming communities in the Jember area; 2) how the benefits of information literacy for mushroom farmers. This research uses descriptive research method with a qualitative approach. Determination of informants using the Snowball Sampling technique. In this study data collection using the method of observation and interviews. The object of research chosen was farmers who had used agricultural technology and modernization through a literacy approach. The results showed the pattern of mushroom cultivation using modernization techniques and the use of appropriate technology to maximize agricultural yields and sales of mushroom production. Through information literacy, mushroom farmers can gain new knowledge. The results obtained through information literacy in the form of knowledge about agricultural modernization, agricultural technology and implementation of information in its application in agriculture, especially mushroom agriculture. To increase revenue and development in the field of mushroom cultivation