Crop Conditions Research Articles

Organic Carbon, Nitrogen Accumulation and Nitrogen Leaching as Affected by Legume Crop Residues on Sandy Loam in the Eastern Baltic Region.

Legumes have a wide range of positive effects on soil properties, including nitrogen and carbon storage, soil structure and the phytosanitary condition of crops. From an agronomic point of view, legumes are most valued for their ability to take up atmospheric nitrogen in symbiosis with nitrogen-fixing bacteria. The aim of this research was to determine the effect of legume residues (peas, fodder beans, narrow-leaved lupins) on the N (Ntotal) and organic carbon (Corg) accumulation in soil and N leaching under temperate climate conditions. The experiment was carried out in lysimetric equipment in 2016-2023. The effect of legumes on Corg and Ntotal accumulation in soil and N leaching were studied in a Fabaceae-Cereals sequence. Three species of legumes-peas, fodder beans and narrow-leaved lupines-were tested; spring barley (Hordeum vulgare L.) was grown as a control treatment. The lysimeter surface area was 1.75 m2 and the experimental soil layer was 0.60 m (sand loam Haplic Luvisol). It was found that after harvesting, more residues were incorporated into the soil with lupines (p < 0.05), which, compared to pea and bean residues, increased Ntotal and Corg concentrations in the soil. There was a strong correlation (r = 0.95) between the Ntotal concentration in the soil and the N amount incorporated with residues. Mineral N released during residue decomposition was leached from the humic horizon under conditions of excess moisture in the autumn-winter period and increased the nitrate concentration in the lysimeter water. The increase in concentration was recorded within 5 to 6 months after the application of the residues. As a result, the N leaching losses increased on average by 24.7-33.2% (p < 0.05) during the year of legume cultivation. In the following year, after legume residue incorporation, the effect of residues on nitrate concentration and N leaching decreased and did not differ significantly from that of barley residues.

Research on rice leaf area index estimation based on fusion of texture and spectral information

Leaf Area Index (LAI) is one of the indicators used to measure the growth status of rice fields. Rapid, accurate, and large-scale monitoring of LAI plays an important role in ensuring stable grain yield increase. In recent years, the spectral saturation problem and the parameter adjustment problem of machine learning algorithms have become the main limitations to improve the accuracy of LAI estimation. High-resolution Unmanned Aerial Vehicles (UAVs) images contain not only rich spectral information, but also texture information reflecting the crop canopy structure. Therefore, in order to fully understand the role of spectral information and texture information fusion in rice LAI estimation, this study used the hyperspectral sensor carried by the UAVs to obtain the spectral images of rice canopy of different varieties and different growth stages. Rice canopy reflectance and 8 basic texture features based on Gray-level Co-occurrence Matrix (GLCM) were extracted from hyperspectral images to calculate vegetation indexs (VIs) and combined texture features. Normalized difference texture index (NDTI), Non-linear texture index (NLTI), Enhanced vegetation texture index (EVTI), and Modified triangular texture index (MTTI) were calculated using two and three GLMC-based texture features to explore the effect of combinations of different basic texture features on LAI sensitivity. Two rice LAI estimation models were developed for single spectral indicators and combined with texture indicators, respectively. The results show that: (1) After preprocessing and feature band screening, the optimal spectral band, vegetation index, and trilateral parameters were obtained. When the combined spectral parameters (SP) of the three were used as the only input to the model, R2 showed an increasing trend throughout the growth period. The best results were achieved using the support vector regression (SVR) combined with the pelican optimization algorithm (POA) in the pre jointing stage: R2= 0.839, RMSE = 0.107, and MAPE = 7.02%. (2) When texture information based on hyperspectral images was incorporated into the model input, the results showed that the models based on spectral indicators combined with texture measurements were all superior to those using spectral indicators alone, with the best model having a coefficient of determination: R2 = 0.917, RMSE = 0.078, and MAPE = 4.19%, which has promising applications in crop growth index detection. This technology can quickly and effectively monitor the growth status of crops in the field, providing a theoretical basis for estimating crop yield in the later stage.

Analisis Kesesuaian Lahan untuk Tanaman Jagung Menggunakan Citra Landsat 8 OLI/TIRS dan Sistem Informasi Geografis di Kabupaten Lampung Selatan

Analysis of land suitability for corn plants is needed to increase production in the development of the agricultural sector. South Lampung Regency is the largest corn producer in Lampung Province with production reaching 598,032 tonnes in 2016. The aim of the study was to determine the potential for land development for corn plants using Landsat 8 OLI/TIRS image interpretation and Geographic Information Systems in South Lampung Regency by assessing the quality of the land and to determine the level of land suitability conditions for corn crops based on planting seasons 1, 2 and 3 in South Lampung Regency. The analysis is carried out by the matching method or by giving a class assessment to each of the required parameters. The results show that the 1st planting season has the greatest level of land suitability compared to the other growing seasons, 73% very suitable to suitable. Meanwhile, Planting Season 2 had the lowest suitability level of 60% due to less stable temperatures due to the transition from rainy to dry seasons. And Planting Season 3 has a moderate suitability level of 66%, because this season the temperature has stabilized for cultivating corn. Keywords: Corn, Geographic Information System, Class Assessment, Land Suitability, Landsat 8 OLI/TIRS Imagery, Matching.

Multi-Parametric Approach to Management Zone Delineation in a Hazelnut Grove in Italy

The increase in high-density hazelnut (Corylus avellana) areas drives the interest in practices of precision management. This work addressed soil apparent electrical conductivity (ECa), RGB aerial (UAV) images, proximal sensing, and field scouting in delineating and validating management zones (MZs) in a 2.96 ha hazelnut grove in Italy. ECa data were fitted to a semi-variogram, interpolated (simple kriging), and clustered, resulting in two MZs that were subjected to soil analysis. RGB imagery was used to extract tree canopies from the soil background and determine two vegetation indices (VIs) of general crop status: the Visible Atmospherically Resistant Index (VARI) and the Normalized Green-Red Difference Index (NGRDI). Then, plant growth parameters were manually assessed (tree height, crown size, etc.) and a proximal VI, the Canopy Index (CI), was determined with the MECS-VINE® vertical multisensor. MZ1 was characterized by lower ECa values than MZ2. This was associated with a lower clay content (9% vs. 21% in MZ1 vs. MZ2) and organic matter content (1.03% vs. 1.51% in MZ1 vs. MZ2), indicating lower soil fertility in MZ1 vs. MZ2. Additionally, hazelnut trees had significantly smaller canopies (1.42 vs. 1.94 m2 tree−1) and slightly lower values of VARI, NGRDI, and CI in MZ1 vs. MZ2. In conclusion, our approach used ECa to identify homogeneous field areas, which showed differences in soil properties influencing tree growth. This is the premise for differential hazelnut management in view of better efficiency and sustainability in the use of crop inputs.

Irrigation Management Strategies to Increase Crop-Water Productivity and Grain Quality of Direct Seeded Rice in North West IGP: A Review

Due to its great productivity and profitability, rice (Oryza sativa L.) is farmed in alluvial irrigated tracts of northwest India. For half of the world's population as well as in our country, rice serves as the main source of calories. However, excessive groundwater use in rice farming has resulted in an alarming decline in water table, indicating overuse of groundwater. Therefore, it is necessary to investigate alternative, resource-conserving methods that can sustain rice production. Direct seeded rice presents a compelling option when the future of rice production is in jeopardy due to worldwide water constraint and rising labour costs. The oldest method of crop establishment in this regard, direct seeded rice (DSR), is becoming more and more well-liked constraints minimal input requirements. It has certain benefits, including labour savings, a reduction in water and manpower requirements, early crop maturity, cheap production costs, improved soil physical conditions for crops, a reduction in methane emissions, and greater options for being the best match in various cropping systems. For dry-seeded rice, management strategies that cut irrigation water use and boost crop-water production are needed. Some of the interventions in this respect include cultivars with short growing seasons, tillage, and irrigation scheduling. High crop-water productivity is ensured with optimal yields thanks to irrigation scheduling that aims to eliminate over- or under-irrigation. Tillage changes the edaphic environment of the soil, which impacts crop development. Because of their shallow root systems, rice plants are unable to use the water in the deeper layers of the soil. In order to increase the deep root growth of rice cultivars, deep tillage has become the preferable method. The present research evaluates irrigation management options to raise crop-water productivity and grain quality of DSR in northwest IGP based on the available evidence.

Paper on Smart Irrigation

Agriculture holds immense significance in India as it is the main source of livelihood for a majority of the population. Over the past few decades, farming has undergone significant technological advancements, becoming more industrialized and technology-driven. Smart agriculture gadgets have empowered farmers to have better control over livestock rearing and crop cultivation, enhancing predictability and efficiency. Technology plays a crucial role in the development of this agricultural nation, and water is a vital resource for farming. While water irrigation is a common method to supply water to farms, it can sometimes lead to wastage. The conventional water irrigation system faces challenges due to water scarcity caused by population growth and climate changes. The Internet of Things (IoT) has emerged as a new approach to modernize agriculture. By leveraging automation, IoT can optimize various farming processes and reduce the need for human intervention, making agriculture more efficient. Therefore, a novel method called "automation of water irrigation system using IoT" is proposed to conserve water and save time. This approach involves the use of sensors such as temperature, humidity, and soil moisture sensors to measure different soil parameters. Based on the soil moisture value, the motor controlling the water flow will automatically switch on or off. The status of the motor and the sensed parameters will be displayed on a mobile device. By monitoring and collecting data on soil moisture, temperature, humidity, and sunlight intensity across multiple fields, water usage efficiency and crop yield can be improved for both large and small farms. Additionally, when the soil in agricultural land becomes dry, this method triggers the automatic flow of water for irrigation. Sensors are deployed on farms to monitor crop status, taking into account climate changes and factors like light, humidity, temperature, shape, and size. If any deviations are detected by the sensors, the crop status will be displayed on the farmer's smartphone.

Response of soybean cultivars with different maturation times to pre-emergence herbicides

Herbicide selectivity is the basis for chemical control of weeds; however, it depends on the interactions between herbicide, crop, and edaphoclimatic conditions. The objective of this work was to evaluate the selectivity of herbicides applied at the pre-emergence stage of soybean cultivars with different maturation times grown in different locations. The experiment was conducted under field conditions, in two crop seasons (2019/2020 and 2020/2021), in Lages and Curitibanos, state of Santa Catarina, Brazil. A randomized block experimental design was used, with treatments organized in 4×3 (2019/2020) and 4×4 (2020/2021) factorial arrangements, with four replications. The treatments consisted of interactions between four soybean cultivars: BMX Raio IPRO® (Raio), BMX Zeus IPRO® (Zeus), MONSOY 5947 IPRO® (Monsoy), and BMX Fibra IPRO® (Fibra) and three or four herbicide treatments: control with no herbicides, sulfentrazone + diuron, and diclosulam in 2019/2020; and control with no herbicide, sulfentrazone + diuron, diclosulam, and pyroxasulfone + flumioxazin in 2020/2021. The plots were evaluated for phytotoxicity of herbicides, canopy closure, plant height, stand of plants, number of pods per plant, one-thousand grain weight, and grain yield. The phytotoxicity of herbicides on the soybean plants was mild, under 11%, regardless of the cultivar and growth location. The soybean plants fully recovered from the injuries at 14 days after herbicide application. In Curitibanos, the cultivar Zeus presented higher grain yield in both crop seasons. The herbicide factor showed that sulfentrazone + diuron and diclosulam decreased the grain yield of the cultivars Raio and Zeus in 2019/2020. In the following crop season, only the cultivar Raio presented decreases in grain yield due to the application of diclosulam and pyroxasulfone + flumioxazin. In Lages, the soybean cultivar factor affected the grain yield; the cultivars Monsoy and Fibra were the most productive.

Spatio-temporal variation of water requirement and meteorological impact factors of maize Shaanxi, China

To quantitatively reveal the characteristics of the spatial and temporal distribution and the influencing factors of the maize water requirement in Shaanxi Province, the Penman-Monteith model and path analysis were used to systematically analyze the interannual variation and spatial distribution of the maize water requirement at different growth stages in three climatic zones in Shaanxi Province. And the relationship between the water requirement and meteorological factors based on daily meteorological data in Shaanxi Province for the past 60 years. The results showed that the maize water requirement during the whole growth period decreased from 1960 to 1989, with a rate of change of −2.08 mm/a, increased from 1989 to 2019, with a rate of change of 0.38 mm/a, and decreased from 1960 to 2019, with a rate of change of −0.46 mm/a. The water requirement of maize decreases from north to south during the whole reproductive period. That of seedling and male stage are more in the Guanzhong, followed by the north and the least in the south. The water requirement of maize at jointing stage increased from north to south. And the water requirement distribution is more in the north, followed by the south and the least in the Guanzhong area during the maturity period. The average temperature and the duration of the sunshine are the key factors that affect maize water requirement in Shaanxi Province, and have an increasing effect on water requirement, wind speed has an increasing effect on water requirement but is not obvious, water vapor pressure and relative humidity have an inhibiting effect on water requirement. For spring maize, the promoting effect of the duration of the sunshine on maize water requirement was gradually strengthened from north to south. The promoting effect of mean temperature and wind speed decreased gradually. The inhibitory effect of water vapor pressure and relative humidity decreases continuously. This study can provide a scientific basis for the management of irrigation quota in maize growing areas under similar cropping conditions.

APPLICATION OF MACHINE LEARNING IN ESTIMATING ONTREE YIELD OF CITRUS FRUIT

Citrus is manually counted to estimate the yield. By using some innovative agricultural techniques yield and production can be increased. Numerous agricultural innovations have been introduced in recent years. Higher agricultural production, prediction, and reliable crop status information are more important than ever due to the expected growth of the human population. Agriculture has always been the foundation of human society. Current study was aimed to develop a reliable and meaningful information-gathering agricultural field based on image processing during 2020. Citrus yield can be increased in the initial stages by counting it with RGB and HSV-based images taken from an Android phone from various angles using machine learning techniques. Fertilizers such as potash, phosphorus, and nitrogen can then be utilized to boost yield. According to the findings, farmers can control and monitor citrus health production more efficiently and effectively by integrating machine learning with agriculture. The citrus calculation using the given technique compared with manually counted citrus, having difference of up to 5 to 10 citruses for a single plant per plot in a field. The proposed method produced excellent results under varying lighting conditions, leaf occlusion, and fruit overlap on photos taken at various distances from the orange trees.

A Ground Point Fitting Method for Winter Wheat Height Estimation Using UAV-Based SfM Point Cloud Data

Height is a key factor in monitoring the growth status and rate of crops. Compared with large-scale satellite remote sensing images and high-cost LiDAR point cloud, the point cloud generated by the Structure from Motion (SfM) algorithm based on UAV images can quickly estimate crop height in the target area at a lower cost. However, crop leaves gradually start to cover the ground from the beginning of the stem elongation stage, making more and more ground points below the canopy disappear in the data. The terrain undulations and outliers will seriously affect the height estimation accuracy. This paper proposed a ground point fitting method to estimate the height of winter wheat based on the UAV SfM point cloud. A canopy slice filter was designed to reduce the interference of middle canopy points and outliers. Random Sample Consensus (RANSAC) was applied to obtain the ground points from the valid filtered point cloud. Then, the missing ground points were fitted according to the known ground points. Furthermore, we achieved crop height monitoring at the stem elongation stage with an R2 of 0.90. The relative root mean squared error (RRMSE) of height estimation was 5.9%, and the relative mean absolute error (RMAE) was 4.6% at the stem elongation stage. This paper proposed the canopy slice filter and fitting missing ground points. It was concluded that the canopy slice filter successfully optimized the extraction of ground points and removed outliers. Fitting the missing ground points simulated the terrain undulations effectively and improved the accuracy.

Estimating Drought-Induced Crop Yield Losses at the Cadastral Area Level in the Czech Republic

In the Czech Republic, soil moisture content during the growing season has been decreasing over the past six decades, and drought events have become significantly more frequent. In 2003, 2015, 2018 and 2019, drought affected almost the entire country, with droughts in 2000, 2004, 2007, 2012, 2014 and 2017 having smaller extents but still severe intensities in some regions. The current methods of visiting cadastral areas (approximately 13,000) to allocate compensation funds for the crop yield losses caused by drought or aggregating the losses to district areas (approximately 1000 km2) based on proxy data are both inappropriate. The former due to the required time and resources, the later due to low resolution, which leads to many falsely negative and falsely positive results. Therefore, the study presents a new method to combine ground survey, remotely sensed and model data for determining crop yield losses. The study shows that it is possible to estimate them at the cadastral area level in the Czech Republic and attribute those losses to drought. This can be done with remotely sensed vegetation, water stress and soil moisture conditions with modeled soil moisture anomalies coupled with near-real-time feedback from reporters and with crop status surveys. The newly developed approach allowed the achievement of a proportion of falsely positive errors of less than 10% (e.g., oat 2%, 8%; spring barley 4%, 3%; sugar beets 2%, 21%; and winter wheat 2%, 6% in years 2017, resp. 2018) and allowed for cutting the loss assessment time from eight months in 2017 to eight weeks in 2018.

Potential Applications of Silicate Solubilizing Bacteria and Potassium Silicate on Sugarcane Crop under Drought Condition

The decline of soil nutrient status and productivity of crops, and restoration of soil health is a key global concern. Crop yields are reduced by soil degradation, which endangers the world's food supply. Crop yields and soil health are further decreased by improper fertilizers use in conjunction with intense cropping. A wide variety of plants, particularly monocotyledonous crops can collect significant levels of silicon in their organs showing the benefits of silicon in terms of growth, biomass, and yield, whereas most dicots are unable to accumulate enough Si under their epidermal system. In the present investigation, treatment of applying silica solubilizer at a rate of 12.5 kg with 50 kg FYM/ha in soil with sett treatment of 0.5% K2SiO3,2.5% urea and potash foliar spray in a specified time showed a significant result in all the growth and yield parameters tested when plant and its ratoon were in drought condition.

Iot-Based Privacy-Preserving Anomaly Detection Model for Smart Agriculture

Internet of Things (IoT) technology has been incorporated into the majority of people’s everyday lives and places of employment due to the quick development in information technology. Modern agricultural techniques increasingly use the well-known and superior approach of managing a farm known as “smart farming”. Utilizing a variety of information and agricultural technologies, crops are observed for their general health and productivity. This requires monitoring the condition of field crops and looking at many other indicators. The goal of smart agriculture is to reduce the amount of money spent on agricultural inputs while keeping the quality of the final product constant. The Internet of Things (IoT) has made smart agriculture possible through data collection and storage techniques. For example, modern irrigation systems use effective sensor networks to collect field data for the best plant irrigation. Smart agriculture will become more susceptible to cyber-attacks as its reliance on the IoT ecosystem grows, because IoT networks have a large number of nodes but limited resources, which makes security a difficult issue. Hence, it is crucial to have an intrusion detection system (IDS) that can address such challenges. In this manuscript, an IoT-based privacy-preserving anomaly detection model for smart agriculture has been proposed. The motivation behind this work is twofold. Firstly, ensuring data privacy in IoT-based agriculture is of the utmost importance due to the large volumes of sensitive information collected by IoT devices, including on environmental conditions, crop health, and resource utilization data. Secondly, the timely detection of anomalies in smart agriculture systems is critical to enable proactive interventions, such as preventing crop damage, optimizing resource allocation, and ensuring sustainable farming practices. In this paper, we propose a privacy-encoding-based enhanced deep learning framework for the difficulty of data encryption and intrusion detection. In terms of data encoding, a novel method of a sparse capsule-auto encoder (SCAE) is proposed along with feature selection, feature mapping, and feature normalization. An SCAE is used to convert information into a new encrypted format in order to prevent deduction attacks. An attention-based gated recurrent unit neural network model is proposed to detect the intrusion. An AGRU is an advanced version of a GRU which is enhanced by an attention mechanism. In the results section, the proposed model is compared with existing deep learning models using two public datasets. Parameters such as recall, precision, accuracy, and F1-score are considered. The proposed model has accuracy, recall, precision, and F1-score of 99.9%, 99.7%, 99.9%, and 99.8%, respectively. The proposed method is compared using a variety of machine learning techniques such as the deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM).

Agricultural Development in Comprehensive Village Development Program (CVDP) Area and Non-Comprehensive Village Development Area of Comilla

Purpose: This study compares the effectiveness of the Comprehensive Village Development Program (CVDP) and Non-CVDP models in the context of agricultural development in rural Bangladesh. The roles played by cooperative institutions in CVDP and non-CVDP villages in agricultural development initiatives based on agricultural extension in terms of the status of crops, vegetables, livestock, poultry, and fish farming so that the Bangladesh government can concentrate on the value of the CVDP model and implement it in the underdeveloped rural area.&#x0D; Data and Method: The secondary data used in this research were gathered between 1990 and 2017 and provided in the form of tables and charts. The study's results are interpreted using the percentage approach. The purposive sampling technique is used to emphasize qualitative and quantitative analysis. Information is gathered from BARD, books, journals, and government reports.&#x0D; Findings: The results of the study demonstrate that improved agricultural techniques, including the use of fertilizers and insecticides, were used in CVDP communities. However, there were no appreciable differences in the rice yield between CVDP and non-CVDP villages. Utilization of irrigation and realization of water charges have both increased in the CVDP zones. In all the communities, sophisticated farming machinery was utilized. The villages participating in the CVDP have established sales networks and expertise in raising livestock and poultry. Multiple ownership, theft, fish poisoning, etc. affected pond fish firming more in non-CVDP communities than in CVDP villages.&#x0D; Practical Implications: The government may be able to restructure agricultural extension developments in Bangladesh according to the CVDP model with the help of the data gathered from the research once again.&#x0D; Research Limitations: It is difficult to use recent data in the analysis due to a lack of past research on the topic in Bangladesh, limited access to relevant areas, and the absence of new studies in this field.&#x0D; Value: The findings of this study complement those of earlier research that could not examine variances in agricultural growth (in terms of the output of agro products including rice, vegetables, cereals and vegetables, livestock, poultry, etc.

Agricultural Development in Comprehensive Village Development Program (CVDP) Area and Non-Comprehensive Village Development Area of Comilla

Purpose: This study compares the effectiveness of the Comprehensive Village Development Program (CVDP) and Non-CVDP models in the context of agricultural development in rural Bangladesh. The roles played by cooperative institutions in CVDP and non-CVDP villages in agricultural development initiatives based on agricultural extension in terms of the status of crops, vegetables, livestock, poultry, and fish farming so that the Bangladesh government can concentrate on the value of the CVDP model and implement it in the underdeveloped rural area.&#x0D; Data and Method: The secondary data used in this research were gathered between 1990 and 2017 and provided in the form of tables and charts. The study's results are interpreted using the percentage approach. The purposive sampling technique is used to emphasize qualitative and quantitative analysis. Information is gathered from BARD, books, journals, and government reports.&#x0D; Findings: The results of the study demonstrate that improved agricultural techniques, including the use of fertilizers and insecticides, were used in CVDP communities. However, there were no appreciable differences in the rice yield between CVDP and non-CVDP villages. Utilization of irrigation and realization of water charges have both increased in the CVDP zones. In all the communities, sophisticated farming machinery was utilized. The villages participating in the CVDP have established sales networks and expertise in raising livestock and poultry. Multiple ownership, theft, fish poisoning, etc. affected pond fish firming more in non-CVDP communities than in CVDP villages.&#x0D; Practical Implications: The government may be able to restructure agricultural extension developments in Bangladesh according to the CVDP model with the help of the data gathered from the research once again.&#x0D; Research Limitations: It is difficult to use recent data in the analysis due to a lack of past research on the topic in Bangladesh, limited access to relevant areas, and the absence of new studies in this field.&#x0D; Value: The findings of this study complement those of earlier research that could not examine variances in agricultural growth (in terms of the output of agro products including rice, vegetables, cereals and vegetables, livestock, poultry, etc.

Conservation agriculture based crop management practices impact diversity and population dynamics of the insect-pests and their natural enemies in agroecosystems

Human efforts to grow abundant food through the persistent use of resource-intensive farming practices have resulted in declining soil health, and deterioration of ecosystem functions and services. Conservation agriculture (CA) has emerged as a practice to minimize the impacts of conventional resource-exhaustive and energy-intensive agriculture. Minimum soil disturbance, permanent soil cover, and diversification are the key components of CA. Tillage through conventional practices on the other hand has detrimental effects on the soil and environment as it requires deep inversion of soil with instruments such as mouldboard plow, disc plow etc. leaving very less organic matter in soil after establishment of crop. Even though, CA advocates many benefits over conventional agriculture in terms of soil and water conservation, the consequent changes in moisture and temperature regimes due to reduced tillage and surface cover would likely going to influence the biological activity, including insect pests and their natural enemies which dwell within these agroecosystems. The changed crop conditions under CA may favor particular insect communities and their ecological niches. The adoption of such practices may lead to decrease in insect pests with major activity on the crop canopy. However, the activity of the insect pests that spend their maximum life span at the soil surface or beneath the soil surface may increase. Recent insect-pest outbreaks in North-Western India and imbalances reported in Indo-Gangetic Plains point to the need for a better understanding of the inter-relationships between tillage intensity, residue retention, and insect pest population dynamics. The current review analyzes the existing state of knowledge of these dynamics and presents the scenarios that may emerge as CA get more acceptance. This review will help to develop countermeasures to improve performance and ecosystem services of Conservation agriculture (CA) based cropping systems.

Community structure of soil mites under different crops and its response to environmental factors in the buffer zone of Shibing Karst World Natural Heritage

To reveal the differences in the community structure of soil mites under different crop environments in the buffer zone of the Shibing Karst World Natural Heritage Site, soil mites from corn fields (CF), pear tree lands (PL), tobacco fields (TF), grape fields (GF), and plum gardens (PG) were collected in the buffer zone in January 2022. Through the identification of mite specimens and data analysis, differences in the number of families, genera, individuals, individual density, diversity, similarity of communities, ecological groups of oribatid mites, and responses to environmental factors of soil mites under different crop environments were determined. The results showed that: (1) A total of 6941 soil mites were captured in the study area, belonging to 130 genera, 75 families, and 3 orders. The number of soil mite genera (CF and TF > PL > PG > GF) and number of soil mite individuals (PG > PL > GF > TF > CF) showed characteristics of differential distribution and surface soil aggregation in the five habitats. (2) Scheloribates, Protorobotritia, and Tectocepheus were the dominant genera in the study area. Lohmannia, Scheloribates, and Tectocepheus, representative species that indicate a good soil environment, were distributed in the sample plots of CF, PL, and PG to varying degrees, indicating that the ecological environment of these three crops was healthy. (3) The diversity index of PG was the highest, and similarity analysis of the five habitats revealed that they were at a medium level of similarity. (4) The ecological groups of oribatid mites in the different crop environments were dominated by the overall (O) type, which is a combination of Macropylina, Gymnonota, and Poronota. (5) The community structure of soil mites was sensitive to bulk density (BD), natural water content (NWC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available potassium (AK), and soil organic matter (SOM). Based on the above results, there were differences in the community structures of soil mites under different crop environments in the study area. The identification of the dominant mite genera can preliminarily indicate the environmental conditions of different crops, and a special correlation exists between the community structure of mites and environmental factors. Studying the community structure of mites in different crop environments in the Shibing Karst World Natural Heritage Site buffer zone is important in ensuring the ecological protection and sustainable development of the buffer zone. Furthermore, the results of this study support the role of the buffer zone as an ecological protection barrier for the heritage site.

Implication of blanket NPK application on nutrient balance of maize based on soil and tissue diagnosis approaches in the savannas of northern Nigeria

Improper nutrient management reduces the yield and affects the nutrient status of crops. This study aimed to diagnose the nutrients limitation in maize. A three-year multi-location (348 sites) nutrient experiments were conducted in randomized block design to analyse nutrients limitation for maize production under conventional fertilizer recommendation system in Nigeria using DRIS, and to identify soil factors that influence DRIS indices using random forest model. DRIS indices for nutrients were calculated from the results of ear leaf samples collected from the experimental plots. The DRIS indices were summed, and used to cluster plots using k-means cluster algorithm. The results show large differences in average yield between the clusters. The clusters also differed based on frequency with which nutrients are most limiting. B was the most limiting in cluster one and three, Mn in cluster two and K in cluster four. Random forest results show that soil pH, B and Mg had the largest influence on DRIS indices in cluster one. DRIS indices were most influenced by soil N and B in cluster two. To a lesser extent, the soil Fe, K, Mg and S contents also influenced DRIS indices in cluster two. Soil K, B and Zn were the most significant factors influencing the DRIS indices in cluster four. Bulk Density, Fe, Na, ECEC, and organic carbon had a moderate influence on the indices in this cluster. Nutrient limitation in plants can be diagnose using the DRIS. Soil properties have a definite influence on maize nutrient status.

Integration of Remote Sensing and Field Observations in Evaluating DSSAT Model for Estimating Maize and Soybean Growth and Yield in Maryland, USA

Crop models are useful for evaluating crop growth and yield at the field and regional scales, but their applications and accuracies are restricted by input data availability and quality. To overcome difficulties inherent to crop modeling, input data can be enhanced by the incorporation of remotely sensed and field observations into crop growth models. This approach has been recognized to be an important way to monitor crop growth conditions and to predict yield at the field and regional scale. In recent years, satellite remote sensing has provided high-temporal and high-spatial-resolution data that allow for generating continuous time series of biophysical parameters such as vegetation indices, leaf area index, and phenology. The objectives of this study were to use remote sensing along with field observations as inputs to the Decision Support System for Agro-Technology (DSSAT) model to estimate soybean and maize growth and yield. The study used phenology and leaf area index (LAI) data derived from Planet Fusion (daily, 3 m) satellite imagery along with field observation data on crop growth stage, LAI and yield collected at the United State Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center (BARC), Beltsville, Maryland. For maize, a total of 17 treatments (site years) were used (ten treatments for model calibration and seven treatments for validation), while for soybean (maturity groups three and four), a total of 18 treatments were used (nine for calibration and nine for validation). The calibrated model was tested against an independent, multi-location and multi-year set of phenology and yield data (2017–2020) from BARC fields. The model accurately simulated maize and soybean days to flowering and maturity and produced reasonable yield estimates for most fields and years. Model run for independent locations and years produced good results for phenology and yields for both maize and soybean, as indicated by index of agreement (d) values ranging from 0.65 to 0.93 and normalized root-mean-squared error values ranging from 1 to 20%, except for soybean maturity group four. Overall, model performances with respect to phenology and grain yield for maize and soybean were good and consistent with other DSSAT evaluation studies. The inclusion of remote sensing along with field observations in crop-growth model inputs can provide an effective approach for assessing crop conditions, even in regions lacking ground data.

Multiobjective optimization of regional irrigation and nitrogen schedules by using the CERES-Maize model with crop parameters determined from the remotely sensed leaf area index

Using crop models to develop reasonable irrigation and nitrogen schedules (INSs) is critical for achieving high yields and maximizing water use efficiency (WUE) and nitrogen use efficiency. However, the INSs developed based on crop parameters determined through field experiments have low robustness at the regional scale. Remote sensing data indicate the growth conditions of regional crops, and representative regional crop parameters can be determined using remote sensing data. The objective of the present study was to develop a regional INS by adopting a crop model with crop parameters calibrated using the remotely sensed leaf area index (LAI). First, the crop parameters of a field-scale crop model were calibrated on the basis of field-scale trial data for four summer maize growing seasons. Sensitive crop parameters at the regional scale were then identified using an assimilation algorithm based on the remotely sensed LAI. The rationality of using the remotely sensed LAI for determining regional crop parameters was demonstrated by simulating regional maize growth. Finally, a regional-scale crop model was developed and coupled with a multiobjective genetic algorithm to optimize the regional INS for different typical-rainfall years. The results indicated that the regional-scale crop model was more accurate than was the field-scale crop model in simulating the regional LAI, plant height, soil water content, and yield and the corresponding RMSE decreased by 0.50 m2/m2, 0.15 m, 0.02 cm3/cm3, 344.11 kg/ha. Compared with the INSs currently used by farmers, the optimized INS was associated with a marginally higher irrigation quantity (average increase of 2.66%) but a significantly lower nitrogen application rate (average decrease of 31.6%). The crop yield, WUE, and partial factor productivity for nitrogen were 6.35–12.51%, 2.64–10.41%, and 61.8–66.2% higher, respectively, in different typical-rainfall years when using the optimized INS than when using an unoptimized INS. The results of this study indicate that a regional INS established on the basis of regional crop model parameters estimated using remote sensing data can aid irrigation and nitrogen application scheduling.