Estimation of Crop Production and CO2 Fluxes Using Remote Sensing: Application to a Winter Wheat/Sunflower Rotation

To meet the incoming growth of the world’s food needs, and the demands of climate change, the agricultural sector will be forced to adapt its practices. To do so, the contribution of agricultural fields to greenhouse gas emissions, as well as the impact—on soil, climate and productions—of certain agricultural practices have to be known. In this study, the SAFY-CO2 crop model is driven by remote sensing products in order to estimate CO2 fluxes on the main crop rotation observed in the study area, i.e., winter wheat followed by sunflower. Different modeling scenarios are tested, particularly for intercropping periods, the approach being validated locally, thanks to eddy covariance flux measurements, and then applied regionally. Results showed that the model was able to reproduce crop production with high accuracy (rRMSE of 21% and 24% for winter wheat and sunflower yield, respectively) as well as daily net CO2 flux (RMSE of 1.29 and 0.97 gC.m−2.d−1 for winter wheat and sunflower respectively). Moreover, the tested modeling scenarios highlight the importance of taking the regrowth events into account for assessing accurate carbon budgets. In a perspective of large-scale application, the model was upscaled over more than 100 plots, allowing discussion of the effect of regrowth on carbon uptake.

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Integrated application of drones for soil management and precision agriculture in Nigeria

10.36265/colsssn.2020.4404 ◽

2020 ◽

pp. 21-30

Author(s):

Agbakoba Augustine Azubuike ◽

Ema Idongesit Asuquo ◽

Agbakoba Victor Chike

Keyword(s):

Precision Agriculture ◽

Crop Production ◽

Large Scale ◽

Agricultural Sector ◽

New Technologies ◽

Arable Land ◽

Agricultural Practices ◽

Soil Management ◽

Short Period ◽

Information And Communication

The recent push for precision agriculture has resulted in the deployment of highly sophisticated Information and Communication Technology (ICT) gadgets in various agricultural practices and methods. The introduction of ICT devices has been linked to significant improvements in agricultural activities. These devices have been shown to enhance the optimal management of critical resources such as water, soil, crop and arable land. Again, ICT devices are increasingly attractive due to their flexibility, ease of operation, compactness and superior computational capabilities. Especially when in comparison to the mundane methods previously used by most small- and large-scale farmers. For instance, ICT devices such as Unmanned Aerial Vehicles (UAVs) also referred to as drones, are increasingly being deployed for remote sensing missions where they capture high quality spatial resolution images. The data generated by these UAVs provide much needed information that aids in early spotting of soil degradation, crop conditions, severity of weed infestation and overall monitoring of crop yield variability. This enables farmers to acquire on-the-spot information that will enhance decision making within a short period of time, which will in turn contribute to reduction in running cost and potentially increase yield. It is safe to say that full potentials of drones are yet to be fully utilized in the Nigerian agricultural sector. This is due to several factors; most notably are the numerous challenges that accompany the introduction and adoption of much new technologies. Other factors; include high cost of technology, inadequate or total lack of skilled labour, poor awareness and low-farmer literacy. Therefore, this review work highlights the global progress recorded as a result of the recent application of drones for soil management and efficient crop production. Furthermore, key discussions surrounding the application of drones for precision agriculture and the possible drawbacks facing the deployment of such technology in Nigeria has been covered in this work.

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Establishing an operational system for assessment and forecasting the impact of extreme weather events on crop production

MAUSAM ◽

10.54302/mausam.v67i1.1230 ◽

2021 ◽

Vol 67 (1) ◽

pp. 289-296

Author(s):

SHIBENDU S. RAY ◽

SURESH K. SINGH ◽

NEETU . ◽

S. MAMATHA

Keyword(s):

Remote Sensing ◽

Case Studies ◽

Crop Production ◽

Large Scale ◽

Extreme Weather ◽

Extreme Weather Events ◽

Wheat Crop ◽

Production Forecasting ◽

Weather Events ◽

The Impact

Crop production forecasting is essential for various economic policy and decision making. There is a very successful operational programme in the country, called FASAL, which uses multiple approaches for pre-harvest production forecasting. With the increase in the frequency of extreme events and their large-scale impact on agriculture, there is a strong need to use remote sensing technology for assessing the impact. Various works have been done in this direction. This article provides three such case studies, where remote sensing along with other data have been used for assessment of flood inundation of rice crop post Phailin cyclone, period operational district/sub-district level drought assessment and understanding the impact of recent hailstorm/unseasonal rainfall on wheat crop. The case studies highlight the great scope of remote sensing data for assessment of the impact of extreme weather events on crop production.

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Pemulihan Perekonomian Jawa Timur di Masa Pandemi Covid-19 Melalui Sistem Pertanian Terpadu (SPT) Budikdamber (Budidaya Ikan dalam Ember)

Journal of Islamic Economics (JoIE) ◽

10.21154/joie.v1i1.3085 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Husna Ni’matul Ulya

Keyword(s):

Economic Development ◽

Large Scale ◽

Agricultural Sector ◽

Government Policies ◽

Agricultural System ◽

Work From Home ◽

Positive Growth ◽

Food Needs ◽

The Impact

Penurunan pertumbuhan ekonomi Indonesia selama masa pandemi covid-19 terjadi selama kuartal I dan kuartal II menyebabkan melemahnya laju perekonomian dan menurunnya pendapatan masyarakat, dikarenakan kebijakan pemerintah dalam pencegahan penyebaran covid-19 berupa Pembatasan Sosial Berskala Besar (PSBB), penerapan protokol kesehatan, dan Learn/Work From Home (LFH/WFH), sehingga masyarakat harus tetap berada di rumah dan mengurangi aktifitas di luar jika tidak dibutuhkan. Dampak yang terjadi mempengaruhi semua bidang perekonomian, salah satunya pada sektor/lapangan usaha pertanian. Perkembangan perekonomian propinsi Jawa Timur juga mengalami penurunan pada data terakhir sebesar 5,90 %. Di antara sektor yang mengalami penurunan, sektor pertanian adalah sektor yang masih mengalami pertumbuhan positif, karena produktifitas sektor pertanian tidak begitu dipengaruhi oleh situasi pendemi, namun yang menjadi masalah adalah jumlah permintaan yang lebih menurun daripada sebelumnya. Untuk memberikan solusi terhadap kebijakan pandemi, maka diperlukan Sistem Pertanian Terpadu (SPT) yang memanfaatkan lahan di sekitar rumah, sehingga masyarakat tidak perlu keluar rumah untuk bertani. Selain dapat mencukupi kebutuhan pangan keluarga, masyarakat dapat menjual hasil panennya, tidak hanya mencukupi kebutuhan nabati, tapi juga kebutuhan hewani. Sistem yang diadopsi dari program dompet dhuafa yang disebut Budidaya Ikan dalam Ember (budikdamber) menjadi salah satu pilihan untuk memenuhi sistem pertanian terpadu, karena selain memanfaatkan seumberdaya yang ada, juga dapat memaksimalkan penggunaan media yang sama untuk dua sistem budidaya. Namun, masih banyak yang harus dilaksanakan untuk menindaklanjuti pengembangan sistem ini yang disesuaikan dengan tuntutan perkembangan sosial, budaya, teknologi dan ekonomi masyarakat.The decline in Indonesia's economic growth during the Covid-19 pandemi occurred during the first quarter and second quarter which caused a weakening of the economy and decreased people's income, due to government policies to prevent the spread of Covid-19 in the form of Large-Scale Social Restrictions (PSBB), implementation of health protocols, and Learn / Work From Home (LFH / WFH), so that people must stay at home and reduce outdoor activities when not needed. The impact that occurs affects all sectors of the economy, one of which is the agricultural sector/business field. The economic development of East Java province also experienced a decline in the latest data of 5.90%. Among the sectors that have experienced a decline, the agricultural sector is still experiencing positive growth, because the productivity of the agricultural sector is not significantly affected by the pandemi situation, but the problem is that the amount of demand is decreasing more than before. To provide solutions to pandemi policies, an Integrated Farrming System (IFS) are needed which utilizes the land around the house, so that people don't have to leave the house to the farm. In addition to being able to meet family food needs, people can sell their crops, not only for vegetable needs but also for animal needs. The system adopted from the dompet dhuafa program called Aquaculture in a Bucket (budikdamber) is an option to fulfill an integrated agricultural system because, in addition to utilizing existing resources, it can also maximize the use of the same media for two cultivation systems. However, much remains to be done to follow up on the development of this system in line with the demands of social, cultural, technological, and economic development in society.

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A Macroeconomic Analysis of Agricultural Sector in Nigeria

Advanced Journal of Social Science ◽

10.21467/ajss.5.1.18-25 ◽

2019 ◽

Vol 5 (1) ◽

pp. 18-25

Author(s):

Isah Funtua Abubakar ◽

Umar Bambale Ibrahim

Keyword(s):

Value Chain ◽

Crop Production ◽

Agricultural Sector ◽

Time Series Data ◽

Tourism Industry ◽

Series Data ◽

Size Analysis ◽

The Real ◽

Real Gdp ◽

The Impact

This paper attempts to study the Nigerian agriculture industry as a panacea to growth as well as an anchor to the diversification agenda of the present government. To do this, the time series data of the four agriculture subsectors of crop production, livestock, forestry and fishery were analysed as stimulus to the Real GDP from 1981-2016 in order to explicate the individual contributions of the subsectors to the RGDP in order to guide the policy thrust on diversification. Using the Johansen approach to cointegration, all the variables were found to be cointegrated. With the exception of the forestry subsector, all the three subsectors were seen to have impacted on the real GDP at varying degrees during the time under review. The crop production subsector has the highest impact, however, taking size-by-size analysis, the livestock subsector could be of much importance due to its ability to retain its value chain and high investment returns particularly in poultry. Therefore, it is recommended that, the government should intensify efforts to retain the value chain in the crop production subsector, in order to harness its potentials optimally through the encouragement of the establishment of agriculture cottage industries. Secondly, the livestock subsector is found to be the most rapidly growing and commercialized subsector. Therefore, it should be the prime subsector to hinge the diversification agenda naturally. Lastly, the tourism industry which is a source through which the impact of the subsector is channeled to the GDP should be developed, in order to improve the impact of such channel to GDP with the sole objective to resuscitate the forestry subsector.

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Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽

10.3390/s21123982 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3982

Author(s):

Giacomo Lazzeri ◽

William Frodella ◽

Guglielmo Rossi ◽

Sandro Moretti

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Fire Severity ◽

Experimental Methodology ◽

Vegetation Recovery ◽

Burned Area ◽

Remotely Sensed Data ◽

The Impact ◽

Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

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Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽

10.3390/s18103232 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3232 ◽

Cited By ~ 17

Author(s):

Yan Liu ◽

Qirui Ren ◽

Jiahui Geng ◽

Meng Ding ◽

Jiangyun Li

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Large Scale ◽

Semantic Segmentation ◽

Remote Sensing Image ◽

Training Data ◽

Land Resources ◽

Remote Sensing Images ◽

Training Strategy ◽

The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

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Joint inverse estimation of fossil fuel and biogenic CO2 fluxes in an urban environment: An observing system simulation experiment to assess the impact of multiple uncertainties

Elem Sci Anth ◽

10.1525/elementa.138 ◽

2018 ◽

Vol 6 ◽

Cited By ~ 9

Author(s):

Kai Wu ◽

Thomas Lauvaux ◽

Kenneth J. Davis ◽

Aijun Deng ◽

Israel Lopez Coto ◽

...

Keyword(s):

High Resolution ◽

Urban Environment ◽

Co2 Emissions ◽

Fossil Fuel ◽

Atmospheric Transport ◽

System Simulation ◽

Co2 Flux ◽

Point Sources ◽

Co2 Fluxes ◽

The Impact

The Indianapolis Flux Experiment aims to utilize a variety of atmospheric measurements and a high-resolution inversion system to estimate the temporal and spatial variation of anthropogenic greenhouse gas emissions from an urban environment. We present a Bayesian inversion system solving for fossil fuel and biogenic CO2 fluxes over the city of Indianapolis, IN. Both components were described at 1 km resolution to represent point sources and fine-scale structures such as highways in the a priori fluxes. With a series of Observing System Simulation Experiments, we evaluate the sensitivity of inverse flux estimates to various measurement deployment strategies and errors. We also test the impacts of flux error structures, biogenic CO2 fluxes and atmospheric transport errors on estimating fossil fuel CO2 emissions and their uncertainties. The results indicate that high-accuracy and high-precision measurements produce significant improvement in fossil fuel CO2 flux estimates. Systematic measurement errors of 1 ppm produce significantly biased inverse solutions, degrading the accuracy of retrieved emissions by about 1 µmol m–2 s–1 compared to the spatially averaged anthropogenic CO2 emissions of 5 µmol m–2 s–1. The presence of biogenic CO2 fluxes (similar magnitude to the anthropogenic fluxes) limits our ability to correct for random and systematic emission errors. However, assimilating continuous fossil fuel CO2 measurements with 1 ppm random error in addition to total CO2 measurements can partially compensate for the interference from biogenic CO2 fluxes. Moreover, systematic and random flux errors can be further reduced by reducing model-data mismatch errors caused by atmospheric transport uncertainty. Finally, the precision of the inverse flux estimate is highly sensitive to the correlation length scale in the prior emission errors. This work suggests that improved fossil fuel CO2 measurement technology, and better understanding of both prior flux and atmospheric transport errors are essential to improve the accuracy and precision of high-resolution urban CO2 flux estimates.

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Investigation of the two-decade environmental impact of large-scale agricultural cultivation and its impact on climate change in the Lajta-Project

10.5194/egusphere-egu21-4863 ◽

2021 ◽

Author(s):

András Polgár ◽

Karolina Horváth ◽

Imre Mészáros ◽

Adrienn Horváth ◽

András Bidló ◽

...

Keyword(s):

Climate Change ◽

Life Cycle ◽

Environmental Impact ◽

Winter Wheat ◽

Environmental Impacts ◽

Crop Production ◽

Large Scale ◽

Winter Barley ◽

Cultivated Plants ◽

Agricultural Activity

Crop production is applied on about half of Hungary&#8217;s land area, which amounts to approximately 4.5 million hectares. The agricultural activity has significant environmental impacts.Our work aims the time series investigation of the impacts of large-scale agricultural cultivation on environment and primarily on climate change in the test area by applying environmental life cycle assessment (LCA) method.The investigated area of Lajta Project can be found in the triangle formed by the settlements Mosonszolnok, J&#225;nossomorja and V&#225;rbalog, in the north-western corner of Hungary, in Gy&#337;r-Moson-Sopron county. The area has intense agri-environment characteristics, almost entirely lacking of grasslands and meadows.We were looking for the answer to the question &#8220;To what extent does agricultural activity on this area impact the environment and how can it contribute to climate change during a given period?&#8221; The selection of the plants included in the analysis was justified by their significant growing area. We analysed the cultivation data of 5 crops: canola, winter barley, winter wheat, green maize and maize. Material flows of arable crop production technologies were defined in time series by the agricultural parcel register data. These covered the size of the area actually cultivated, the operational processes, records on seeds, fertilizer and pesticide use and harvest data by parcels. The examined environmental inventory database contained also the fuel consumption and lubricating oil usage of machine operations, and the water usage of chemical utilization.In the life cycle modelling of cultivation, we examined 13 years of maize, 20 years of green maize, 20 years of winter barley, 18 years of winter wheat and 15 years of canola data calculated on 1 ha unit using GaBi life cycle analysis software.In addition, we also calculated by an average cultivation model for all cultivated plants with reference data to 1 ha and 1 year period.We applied methods and models in our life cycle impact assessment. According to the values of the impact categories, we set up the following increasing environmental ranking of plant cultivation: (1) canola has minimum environmental impacts followed by (2) green maize and (3) maize with slightly higher values, (4) winter barley has 6 times higher values preceded by (5) winter wheat with a slight difference. The previous environmental ranking of the specific cultivated plants&#8217; contribution was also confirmed as regards the overall environmental impact: canola (1.0%) &#8211; green maize (4.9%) &#8211; maize (7.1%) &#8211; winter barley (43.1%) &#8211; winter wheat (44.0%).Environmental impact category indicator results cumulated to total cultivation periods and total crop growing areas (quantitative approach) display the specific environmental footprints by crops. Increasing environmental ranking of environmental impacts resulted from cultivating the sample area is the following: (1) canola &#8211; (2) maize &#8211; (3) green maize &#8211; (4) winter barley &#8211; (5) winter wheat. The slight difference resulted in the rankings in quantitative approach according to the rankings of territorial approach on the investigated area is due to the diversity of cultivation time factor and the crop-growing parameter of the specific crops.Acknowledgement: Our research was supported by the &#8222;Lajta-Project&#8221;.

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Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field

Remote Sensing ◽

10.3390/rs12050821 ◽

2020 ◽

Vol 12 (5) ◽

pp. 821 ◽

Cited By ~ 1

Author(s):

Shouyi Wang ◽

Zhigang Xu ◽

Chengming Zhang ◽

Jinghan Zhang ◽

Zhongshan Mu ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Distribution ◽

Winter Wheat ◽

Random Field ◽

Large Scale ◽

Conditional Random Field ◽

Pixel Classification ◽

Classification Result ◽

Edge Pixel ◽

Remote Sensing Imagery

Improving the accuracy of edge pixel classification is crucial for extracting the winter wheat spatial distribution from remote sensing imagery using convolutional neural networks (CNNs). In this study, we proposed an approach using a partly connected conditional random field model (PCCRF) to refine the classification results of RefineNet, named RefineNet-PCCRF. First, we used an improved RefineNet model to initially segment remote sensing images, followed by obtaining the category probability vectors for each pixel and initial pixel-by-pixel classification result. Second, using manual labels as references, we performed a statistical analysis on the results to select pixels that required optimization. Third, based on prior knowledge, we redefined the pairwise potential energy, used a linear model to connect different levels of potential energies, and used only pixel pairs associated with the selected pixels to build the PCCRF. The trained PCCRF was then used to refine the initial pixel-by-pixel classification result. We used 37 Gaofen-2 images obtained from 2018 to 2019 of a representative Chinese winter wheat region (Tai’an City, China) to create the dataset, employed SegNet and RefineNet as the standard CNNs, and a fully connected conditional random field as the refinement methods to conduct comparison experiments. The RefineNet-PCCRF’s accuracy (94.51%), precision (92.39%), recall (90.98%), and F1-Score (91.68%) were clearly superior than the methods used for comparison. The results also show that the RefineNet-PCCRF improved the accuracy of large-scale winter wheat extraction results using remote sensing imagery.

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Large Scale Demo Plot Trial Project of Potash Fertilization for Increased Yield and Profitability for Smallholder Soybean Fields in India

International Journal of Agricultural Extension ◽

10.33687/ijae.007.02.2844 ◽

2019 ◽

Vol 7 (2) ◽

pp. 159-170

Author(s):

Joachim B. Nachmansohn ◽

Patricia Imas ◽

Surinder K. Bansal

Keyword(s):

Soil Fertility ◽

Crop Production ◽

Large Scale ◽

Smallholder Farmers ◽

Agricultural Practices ◽

Crop Productivity ◽

Fine Tuning ◽

Soil Testing ◽

Yield Increase ◽

K Fertilization

Agriculture is the backbone of the Indian economy, in spite of concerned efforts towards industrialization in the last three decades. Therefore, the soil quality and fertility are the major factors in crop production. Declining soil fertility is one of the primary factors that directly affect crop productivity, and fertilizer-use is a key factor in order to keep soil fertility and productivity. A major factor in declining soil fertility is potassium (K) depletion, especially on smallholder farms where fertilization decisions are not based on regular soil testing. Most of the smallholder soybean producers do not have access and investment capacity to soil testing services. Therefore, there is a need to create K fertilizer recommendations based on empirically verified knowledge at India-specific scale. Such large-scale studies, in local filed conditions, are currently lacking. In order to bridge this gap, and generate proven set of directly applicable recommendations, a large-scale plot trial was launched; the Potash for Life (PFL) project. The study evaluated the K response in soybean when fertilizing with potash on K depleted soils in local variable field conditions. The aim was to (1) evaluate the effect and response consistency of K application on soybean yield, (2) to demonstrate to farmers the increased yield and profitability from K-inclusive fertilization regimes for this crop and give recommendations for transient yield increase, and (3) to raise the awareness among smallholder farmers about the importance of K fertilization. A comprehensive experiment was carried out in Madhya Pradesh (M.P.) and Maharashtra. The methodology was straight-forward; two identical plots side by side, with the only difference that one of them was fertilized with additional potash. The results showed a significant yield increase response from the potash application; the average yield increase was 244 kg ha-1 or 26 % in M.P., and 105 kg ha-1 or 36 % in Maharashtra. This entailed an average additional net profit of ₹ 6,681 INR ha-1 and ₹ 2,544 INR ha-1, in M.P. and Maharashtra respectively. It was concluded that the soil status of plant available K is significantly lower than the plant demand for soybean production in the two states, Consequently, K fertilization is necessary in order to improve agricultural practices and optimizing yields. Ultimately, following recommendations given in this study would allow farmers to generate additional profit, which could further allow them to invest in fine-tuning fertilizer practices through the means of soil testing.

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