Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data

Efficient monitoring of cultivated land quality (CLQ) plays a significant role in cultivated land protection. Soil spectral data can reflect the state of cultivated land. However, most studies have used crop spectral information to estimate CLQ, and there is little research on using soil spectral data for this purpose. In this study, soil hyperspectral data were utilized for the first time to evaluate CLQ. We obtained the optimal spectral variables from dry soil spectral data using a gradient boosting decision tree (GBDT) algorithm combined with the variance inflation factor (VIF). Two estimation algorithms (partial least-squares regression (PLSR) and back-propagation neural network (BPNN)) with 10-fold cross-validation were employed to develop the relationship model between the optimal spectral variables and CLQ. The optimal algorithms were determined by the degree of fit (determination coefficient, R2). In order to estimate CLQ at the regional scale, HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data were transformed into dry soil spectral data using the linkage model of original soil spectral reflectance to dry soil spectral reflectance. This study was conducted in the Guangdong Province, China and the Conghua district within the same province. The results showed the following: (1) the optimal spectral variables selected from the dry soil spectral variables were 478 nm, 502 nm, 614 nm, 872 nm, 966 nm, 1007 nm, and 1796 nm. (2) The BPNN was the optimal model, with an R2(C) of 0.71 and a normalized root mean square error (NRMSE) of 12.20%. (3) The results showed the R2 of the regional-scale CLQ estimation based on the proposed method was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional method. Additionally, the NRMSE of the regional-scale CLQ estimation base on dry soil spectral variables from HJ-1A HSI data was 2.00% lower than that of the model base on the original HJ-1A HSI data.

The Effect of Freeze-Thaw Cycling and the Initial Mass of Water on the Unfrozen Water Content of Calcium Bentonites Modified by Copper Ions

This research was conducted with the use of the DSC method; it involved the examination of the unfrozen water content in two model (source) calcium bentonites (≥75% smectite), after one to three freeze-thaw cycles in the natural state, as well as after the ion exchange for a potentially toxic element (Cu2+). The freeze-thaw cycles do not affect the unfrozen water content at a given negative temperature in a statistically significant manner. However, a statistically significant influence of temperature, the initial mass of the water, and the clay type on the change of the unfrozen water content was found. Moreover, the empirical models of predicting the unfrozen water in the bentonite after the exchange for Cu2+ ion were created, for which the parameter was the mass of the water and the mass of the dry soil, at the temperature of −2 °C.

Sustainability of Rice Production at Baixo Mondego, Portugal

This chapter aims to analyze the rice production system at the Baixo Mondego Valley to understand the main concerns. Field research and field trials were carried out to analyze rice production, marketing systems, and different irrigation alternatives. An analysis on the worries was made, and a correlational attempt was done. The results show a production system oriented by agri-environmental policies. The problems related with rice irrigation are water scarcity, environmental impacts on water quality, agroecosystems, and methane emissions. To reduce water demand, the alternate wetting and drying flooding method, and the improvement of the precise land levelling were studied on the scope of MEDWATERICE Project. About 12-14% of water saving was observed, with impact on production lower than 3.5%, allowing period of 11-19 days of dry soil, expecting positive implications for greenhouse gas emissions. Innovation in the irrigation system may help to reduce some of the farmers' concerns and help to better adapt this crop to the new needs of agriculture in terms of environmental competitiveness.

Diversity of arbuscular mycorrhizal fungi in the rhizosphere of Plantago coronopus in Northwestern Algerian coast

Mycorrhizal fungi play a major role in the functioning of ecosystems. However, their identification has remained a challenge for scientific research. This study presents the first identification report of species of arbuscular mycorrhizal fungi in the rhizosphere of the halophyte Plantago coronopus L. in Algeria. Samples of rhizospheric soil were collected in spring 2018 at three sites in the Bomo-plage dunes west of Oran, Algeria. The spores were isolated by wet sieving, morphologically identified, and quantified. The mean spore density was 107.94 spores 100 g-1 dry soil, which is high compared to other dune ecosystems. Endomycorrhizal spore morphotypes were involved in the following Genus: Glomus, Septoglomus, Rhizophagus, Diversispora, Funneliformis, Dentiscutata, Claroideoglomus, Scutellospora, and Entrophospora, to the following Family: Glomeraceae, Gigasporaceae, Diversisporaceae, Claroideoglomeraceae, and Acaulosporaceae. The Glomeraceae was the most dominant identified family. The identification of indigenous arbuscular mycorrhizal fungi has been shown to be essential for future programs to restore disturbed dune ecosystems.

Design and Build a Soil Nutrient Measurement Tool for Citrus Plants Using NPK Soil Sensors Based on the Internet of Things

A suitable planting medium is a medium of good quality soil that can support plant growth quickly. Fertile soil is the primary need for plants. The quality of the planting medium dramatically affects plant growth. The types of soil needed for plant growth vary, namely sandy soil, red soil, alluvial soil, and humus soil. Using an NPK sensor that functions to detect nutrients in the soil and can work if the tip of the sensor is plugged into the soil they want to detect, the results detected by the sensor will be sent in the form of analog signal data to nodemcu, which will be processed and displayed on the screen. Thingspeak. This tool is controlled by nodemcu with an NPK sensor to detect nutrients in the soil with output to thingspeak. The function of the NPK sensor tool will measure the nutrients in the soil for citrus seedlings, and the results read by the Npk sensor will be sent to the Thingspeak web, making it easier for farmers to seed citrus seeds. The data read by the sensor will be sent to thingspeak, making it easier to monitor nutrients in the soil. From the results of the tests carried out, it is found that the nutrient content in wet soil is higher than in dry soil; from the tests carried out, the NPK sensor accuracy rate is 90%.

Direct Seeded Rice and its Prospects in Nepal: A Review

Direct Seeded Rice (DSR) is an alternative cultivation technique to the conventional Transplanted Rice (TPR). In this method, seeds are directly sown to the field without the necessity of nursery raising and transplanting. DSR can generally be divided into dry-DSR (sowing dry seeds in dry soil), wet-DSR (sowing pre-germinated seeds in moist soil) and water-seeding (sowing dry or pre-germinated seeds in standing water). This helps in saving water alongside the labour and results in early harvest due to quick crop establishment. DSR is proved to be sustainable and eco-friendly since it emits less methane than in TPR. However, there are few constraints associated with DSR such as weed infestation, crop lodging and nutrient loss. If these major issues are fixed, DSR can result in greater economic returns as compared to TPR. In absence of puddling, the soil structure can be maintained which leads to greater yields of succeeding crops. This technique has been successfully practiced in many countries like Srilanka, Malaysia and USA. In developing nations like Nepal, irrigation and labour constraints can be tackled by adopting direct-seeding for rice cultivation.

Evaluation of crop growing periods at some stations in northest Brazil

Results of a study of crop .growing periods at some stations in northeast Brazil are presented in this paper. Daily soil moisture values for a minimum period of 25 years are evaluated by means of a simple soil moisture model using temperature and precipitation data. A first order Markov chain model is applied to the soil moisture data and initial and conditional probabilities of wet and dry soil days are obtained. Soil moisture averages and probabilities are used to evaluate crop growing periods at the stations. The effect of uncertainties in the model parameters on the estimated growing periods is investigated.

Modulation of summer monsoon sub-seasonal surface air temperature over India by soil moisture-temperature coupling

The influence of soil moisture on the sub-seasonal warmer surface air temperature anomalies during drier soil conditions associated with break spells in the Indian summer monsoon precipitation is explored using observations. The multi-model analysis of land surface states and fluxes available from the Second Global Soil Wetness Project (GSWP-2) are found useful in understanding the mechanism for this soil moisture-temperature coupling on sub-seasonal timescales. The analysis uses a soil moisture-temperature coupling diagnostic computed based on linear correlations of daily fields. It is shown that the summer surface air temperature variations are linked to intraseasonal variations of the Indian monsoon precipitation, which control the land-climate coupling by modulating the soil moisture variations. Strong coupling mainly occurs during dry soil states within the summer monsoon season over the transition zones between wet and dry climates of central to north-west India. In contrast, the coupling is weak for constantly wet and energy-limited evaporative regimes over eastern India during the entire summer monsoon season. This observational based analysis provided a better understanding of the linkages between the sub-seasonal dry soil states and warm conditions during the Indian summer monsoon season. A proper representation of these aspects of land-atmosphere interactions in weather and climate models used for sub-seasonal and seasonal monsoon forecasting could be critical for several applications, in particular agriculture. The soil moisture-temperature coupling diagnostic used in this study will be a useful metric for evaluating the performance of weather and climate models.

First Report of Entomopathogenic Fungi Occurrence in Forest Soils in Croatia

Entomopathogenic fungi (EPF) in Croatian forests are known only from observations of insect cadavers that show obvious signs of disease. To date, their presence in soils has not been investigated. The aim of this study was to investigate their occurrence, diversity, and distribution, and to assess their density in tested soils. Soil samples were collected during 2018, 2019, and 2020 at different localities throughout the country, and analyzed by using a method of isolation of fungi on selective culture media. To assess the density of EPF in tested soils, colonies of individual fungal species were counted and recorded; the results were expressed as the number of colony-forming units (CFU) per gram of dry soil. After morphological and molecular analysis, five entomopathogenic fungal genera were identified: Beauveria spp., Metarhizium spp., Purpureocillium spp., Lecanicillium spp., and Paecilomyces spp. Results also showed that the range of a total EPF colony density in the soil varies from 4 × 103 to 27.4 × 103 CFU g−1. The most common were EPF of the genus Beauveria, which were recorded at four of five locations, and at 16 of 25 sampling points, but the highest average number (density) of colonies belonged to the genus Metarhizium. Since this type of research was never conducted in Croatia previously, this is the first evidence that insect pathogenic fungi are present in soils of different natural forest habitats. Such research can be useful in selecting and utilizing entomopathogens that are suitable for biological pest control in certain target areas.

Quality Assessment of Dry Soil Mixing Columns in Soft Soil Areas of Eastern China

To investigate the quality of dry soil mixing (DSM) columns in different soft soil areas of east China, a large number of laboratory test results and field test results of DSM columns were collected and analyzed statistically. Furthermore, a quality assessment method for DSM columns is proposed in this paper. The hardness description (HD), standard penetration test (SPT), unconfined compressive strength (UCS), and soil-cement column quality designation (SCQD) are used as assessment indexes. The statistical analysis showed that the test results of SPT, UCS, and SCQD were scattered, particularly in shallow ground. The mean values of the SPT blow count, UCS, and SCQD of the DSM columns decreased with depth: the greater the depth, the worse the quality of DSM columns. The quality assessment results showed that the proportion of the great quality columns was 64.84%, and the proportion of the unqualified columns was 1.4%. The proportion of DSM columns with great quality in the lagoon soft soil area was greater than in other areas. The proportion of unqualified columns in the lacustrine soft soil area was the largest. For all soft soil areas, the proportions of great quality, good quality, general quality, and unqualified soil decreased in that order. The quality assessment of 8627 DSM columns showed the proportions of great quality and unqualified were 64.84% and 1.4%, respectively. It was found that the greater the depth of the DSM column, the more unqualified DSM columns, and the more difficult it was to control the quality of DSM columns.