Crop Conditions Research Articles

Correlation between Ground Measurements and UAV Sensed Vegetation Indices for Yield Prediction of Common Bean Grown under Different Irrigation Treatments and Sowing Periods

The objective of this study is to assess the possibility of using unmanned aerial vehicle (UAV) multispectral imagery for rapid monitoring, water stress detection and yield prediction under different sowing periods and irrigation treatments of common bean (Phaseolus vulgaris, L). The study used a two-factorial split-plot design, divided into subplots. There were three sowing periods (plots; I—mid April, II—end of May/beginning of June, III—third decade of June/beginning of July) and three levels of irrigation (subplots; full irrigation (F)—providing 100% of crop evapotranspiration (ETc), deficit irrigation (R)—providing 80% of ETc, and deficit irrigation (S) providing—60% of ETc). Canopy cover (CC), leaf area index (LAI), transpiration (T) and soil moisture (Sm) were monitored in all treatments during the growth period. A multispectral camera was mounted on a drone on seven occasions during two years of research which provided raw multispectral images. The NDVI (Normalized Difference Vegetation Index), MCARI1 (Modified Chlorophyll Absorption in Reflectance Index), NDRE (Normalized Difference Red Edge), GNDVI (Green Normalized Difference Vegetation Index) and Optimized Soil Adjusted Vegetation Index (OSAVI) were computed from the images. The results indicated that NDVI, MCARI1 and GNDVI derived from the UAV are sensitive to water stress in S treatments, while mild water stress among the R treatments could not be detected. The NDVI and MCARI1 of the II-S treatment predicted yields better (r2 = 0.65, y = 4.01 tha−1; r2 = 0.70, y = 4.28 tha−1) than of III-S (r2 = 0.012, y = 3.54 tha−1; r2 = 0.020, y = 3.7 tha−1). The use of NDVI and MCARI will be able to predict common bean yields under deficit irrigation conditions. However, remote sensing methods did not reveal pest invasion, so good yield predictions require observations in the field. Generally, a low-flying UAV proved to be useful for monitoring crop status and predicting yield and water stress in different irrigation regimes and sowing period.

Investigating the potential of Andean lupin as a lignocellulosic feedstock for Europe: First genome‐wide association study on Lupinus mutabilis biomass quality

AbstractThe development of multipurpose crops will drive the transformation of agricultural “waste” into added‐value products, helping to meet biomass demands without competing with food production or increasing environmental pressure. Lupinus mutabilis, has been proposed not only as a valid source of protein and oil for Europe but also as a possible source of lignocellulosic feedstock for the biorefinery industry. In this study, the quality of L. mutabilis lignocellulosic biomass and its genetic architecture are investigated for the first time, using a panel of 223 accessions planted across three locations in two different European cropping conditions. Biomass quality was evaluated based on the estimation of neutral detergent fiber, cellulose, hemicellulose and acid detergent lignin fractions, and on the basis of the monosaccharide composition of cell wall polysaccharides. The broad variation in yield and composition of biomass encountered in the panel confirms the potential of L. mutabilis as lignocellulosic feedstock and points out the value of this panel as a breeding tool for the improvement of biomass quality. A genome‐wide association study was conducted to identify single‐nucleotide polymorphisms (SNPs) associated with biomass quality, both across locations and per specific location. Scanning of 16,781 SNPs across the whole genome identified 46 unique quantitative trait loci for biomass quality, 4 of which were detected as common either among traits or GWAS models. For each of the traits analyzed, between 3 and 10 SNPs were detected, explaining 2.7%–15.9% of the phenotypic variation. Underlying these loci, 28 genes were proposed as candidate genes for biomass quality. Important genes involved in cellulose and sucrose synthesis (CESA4, SPP1,WRKY33, GONST2), monolignol biosynthesis (SKIP31, WAT1, CCR‐SNL6) and pectin degradation (RAV1, PE) were identified and will require validation to confirm their value for application in L. mutabilis breeding.

Predictive Modeling of Above-Ground Biomass in Brachiaria Pastures from Satellite and UAV Imagery Using Machine Learning Approaches

Grassland pastures are crucial for the global food supply through their milk and meat production; hence, forage species monitoring is essential for cattle feed. Therefore, knowledge of pasture above-ground canopy features help understand the crop status. This paper finds how to construct machine learning models to predict above-ground canopy features in Brachiaria pasture from ground truth data (GTD) and remote sensing at larger (satellite data on the cloud) and smaller (unmanned aerial vehicles (UAV)) scales. First, we used above-ground biomass (AGB) data obtained from Brachiaria to evaluate the relationship between vegetation indices (VIs) with the dry matter (DM). Next, the performance of machine learning algorithms was used for predicting AGB based on VIs obtained from ground truth and satellite and UAV imagery. When comparing more than twenty-five machine learning models using an Auto Machine Learning Python API, the results show that the best algorithms were the Huber with R2 = 0.60, Linear with R2 = 0.54, and Extra Trees with R2 = 0.45 to large scales using satellite. On the other hand, short-scale best regressions are K Neighbors with an R2 of 0.76, Extra Trees with an R2 of 0.75, and Bayesian Ridge with an R2 of 0.70, demonstrating a high potential to predict AGB and DM. This study is the first prediction model approach that assesses the rotational grazing system and pasture above-ground canopy features to predict the quality and quantity of cattle feed to support pasture management in Colombia.

On the development of descriptors in small bitter gourd (Momordica charantia L. var. muricata (Willd.) Chakrav)

Small bitter gourd (Momordica charantia L. var. muricata (Willd.) Chakrav.) is an under-utilized vegetable with ethnobotanical and therapeutic values, under domestication interphase. This taxon deserves a distinct crop status, owing to the existence of well-marked distinguishing morphological traits, unique flavour and taste apart from M. charantia var. charantia, wide variability within the taxon, preferential cultivation by the traditional farmers in Indian subcontinent and occurrence of distinct, named landraces. However, presently this crop is facing a threat of genetic erosion, restricting mostly at the homestead level throughout the region. In the present study, seventy-four small bitter gourd collections from across India were assessed for understanding morphological variability and their classification, and further devising key descriptors helpful for effective characterization and utilization. In order to describe it under a distinct taxon level, botanical description is also worked out. Morphological traits such as shape and lobing of leaf, shape and size of fruit, nature of tubercles and ridges, and size, shape and colour of seeds, besides robustness and extended growth period, showed remarkable character differentiation from cultivated bitter gourd. A set of 45 characterization and 35 evaluation descriptors have been developed, out of which 27 characterization and 26 evaluation descriptors are categorized under the minimum descriptor list.

Host Status of Cover Crops for the Management of Pratylenchus Jaehni on Coffee.

The lesion nematode Pratylenchus jaehni occurs at low frequency in Brazilian coffee orchards but could provoke extensive root damage. Intercropping cover crops is a traditional practice in Brazilian coffee orchards, and the use of non-hosts of P. jaehni as cover crops may be a useful management method. In this work, 10 cover crops were tested concerning reproduction of P. jaehni. Cajanus cajan, Canavalia ensiformis, and Mucuna deeringiana are cover crops commonly used as intercropping in coffee orchards, but they must not be used in orchards infested with P. jaehni, because they are good hosts of this nematode. Brachiaria ruziziensis, Crotalaria juncea, Dolichos lablab, and Pennisetum glaucum were considered poor hosts. Helianthus annuus cv. Catissol and cv. Uruguai and Crotalaria spectabilis proved to be non-hosts to P. jaehni, and therefore, they are the cover crops recommended in coffee orchards infested with this nematode.

Mathematical modelling of oscillations of a machine for cutting tops of root crops

High-quality cutting of root crop tops at the root ensures both a high yield and determines the condition of the root crops before they are extracted from the soil. Therefore, the search for conditions that will ensure this is an important and urgent problem for the production of these crops. The purpose of this research was to determine, through an analytical study, the optimal parameters of a new design of a machine for harvesting root crop tops, which is mounted in front of an aggregating tractor and performs oscillatory movements in the longitudinal-vertical plane. Methods related to the modelling of complex dynamic systems consisting of an aggregating tractor and a front-mounted machine, the development and solution of the final form of differential equations of oscillatory motion and computer modelling of the oscillatory process were used. According to the developed equivalent scheme, a new differential equation of angular vibrations of the machine was solved in the final form. In addition, numerical modelling was performed on a PC, which allowed constructing graphical dependencies of the machine’s turning angle φ at different speeds V of its forward movement and values of the c stiffness coefficient and μ damping coefficient of the pneumatic tyres of the copying wheels, and at different values of the unevenness of the soil surface and the main design parameters of the machine. It was established that with an increase V from 1.5 m·s-1 to 2.5 m·s-1, the amplitude of oscillations of the machine’s turning angle φ increased from 0.88° to 1.18°. However, at V 1.5 m·s-1, the duration of the transient process is 0.22 s, and at a speed of 2.5 m·s-1, this figure is already 0.14 s, i.e., a decrease of 36%. The positive amplitude of oscillations φ of the machine’s steering angle reaches 1.2°, and the negative amplitude does not exceed 0.3°, i.e., the oscillation range is insignificant. In the range of values considered V, preference should be assigned to its higher value. The structural and kinematic parameters of the system examined were determined using computer simulation. The presented method of mathematical modelling of the oscillatory process can be used in the research of any machines that are hitched in front of the aggregating tractors

Host status of cover crops for root-lesion nematodes (Pratylenchus spp.) associated with apple orchards in South Africa

Host status of cover crops for root-lesion nematodes (Pratylenchus spp.) associated with apple orchards in South Africa

Pantoea agglomerans-Induced Dieback in Pistachio in Chile

Pistachio crops have a great economic potential, as their global production has increased dramatically over the past few decades. Therefore, it is important to maintain the healthy phytosanitary status of pistachio crops. In a Chilean pistachio orchard, a dieback of the trees was observed, with blighting of twigs and severe necrosis in the trunk and twigs. Bacterial isolation, pathogenicity tests and molecular characterization were conducted to determine the causal agent of the observed disease. The bacterial isolation and analysis of 16S rRNA gene led to the identification of Pantoea genus bacteria. Pathogenicity tests carried out on fruits inoculated with Pantoea isolates induced large necrosis seven days post-inoculation. Further inoculations were carried out on pruning cuttings and on the trunk of 18-month-old pistachio plants. Thirty-one weeks after inoculation, necrotic lesions were observed in the wood of pistachio plants. Sequence analysis of housekeeping genes enabled the isolated bacterium to be identified as Pantoa agglomerans, and to verify its role as the causal agent of the pistachio dieback with necrotic lesions. This is the first report of an occurrence of P. agglomerans inducing dieback in pistachio.

Local Factors Impact Accuracy of Garlic Tissue Test Diagnosis

The low productivity of garlic in Brazil requires more efficient nutritional management. For this, environmental and fertilization-related factors must be adjusted to a set of local conditions. Our objective was to provide an accurate diagnosis of the nutrient status of garlic crops in southern Brazil. The dataset comprised 1024 observations, 962 as field tests conducted during the 2015–2017 period to train the model, and 61 field observations collected during the 2018–2019 period to validate the model. Machine learning models (MLM) related garlic yield to managerial, edaphic, plant, and climatic features. Compositional data analysis (CoDa) methods allowed classification of nutrients in the order of limitation to yield where MLM detected nutrient imbalance. Tissue analysis alone returned an accuracy of 0.750 in regression and 0.891 in classification about the yield cutoff of 11 ton ha−1. Adding all features documented in the dataset, accuracy reached 0.855 in regression and 0.912 in classification. Local diagnosis based on MLM and CoDa and accounting for local features differed from regional diagnosis across features. Local nutrient diagnosis may differ from regional diagnosis because several yield-impacting factors are taken into account and benchmark compositions are representative of local conditions.

Soil Classification & Prediction of Crop Status with Supervised Learning Algorithm: Random Forest

Crop Management System (CMS) as developed in an Ionic framework with a Real-Time Firebase database for loop backing and decision support. The main two features were; Soil classification where the soil classified based on temperature, humidity, and soil properties such as soil moisture, soil nutrients, and soil PH level using Random Forest Algorithm. By Bootstrap method using Random Forest, samples from the dataset were selected & then classification trees was generated. The other feature was crop precision where the condition of the crop was and examined using temperature, humidity, soil moisture, soil PH levels, and soil nutrients (N, P, K). IoT device was used to fetch data from the field and then compare with already stored ideal values, suitable for optimal yield, in CMS database then process using the application to suggest the crop for cultivation and to optimize the usage of water and fertilizers. Currently, we classify the soil using Random Forest Algorithm & suggest the suitable crop for the classified type of soil & also measure the soil moisture and soil nutrients of agricultural field Acre based on the reading results we are suggesting the crop to is cultivated and pre-requisite which would be needed in future. The proposed method gives an accuracy of 96.5% as compared to existing methods of Artificial Neural Networks and Support Vector Machines

Lipid accumulation and oxidative stress in the crop tissues of male and female pigeons during incubation and chick-rearing periods

The current study aimed to evaluate the changes in lipid accumulation and oxidative status in pigeon crops during different breeding stages. Forty-two pairs of adult pigeons were randomly assigned to 7 groups. Lipid droplet accumulation in pigeon crops was visualized by using oil red O staining from d 17 of incubation (I17) to d 7 of chick rearing (R7). Transmission electron microscopy analysis showed swollen mitochondria with disintegration of cristae and typical characteristics of endoplasmic reticulum stress in crop tissues at R1 compared with those at I4. During the peak of pigeon milk formation, the concentrations of reactive oxygen species, and oxidative damage markers (advanced oxidation protein products, 8-hydroxy-2 deoxyguanosine, and malondialdehyde) and the enzyme activities of total superoxide dismutase and glutathione peroxidase were all elevated significantly (P < 0.05). The protein concentration of B-cell lymphoma-2 associated X in crop tissues was significantly higher at R1, while the level of B-cell lymphoma-2 protein in males was the highest at I4 (P < 0.05). The ratio of B-cell lymphoma-2 associated X protein (Bax)/B-cell lymphoma-2 (Bcl-2) in both male and female crops peaked at R1 (P < 0.05). Gene expression of the key enzymes involved in mitochondrial and peroxisomal fatty acid β-oxidation was investigated in crops. In males, the gene expression of carnitine palmitoyltransferase 1a peaked at R15, and that of carnitine palmitoyltransferase 2 increased significantly from R1 to R15 (P < 0.05). The mRNA abundance of long chain 3-hydroxyacyl-CoA dehydrogenase increased to the maximum value at R1 and I17 in males and females, respectively. From I17 to R7, the mRNA levels of acyl-CoA oxidase 1 and acyl-CoA oxidase 2 were decreased in pigeon crops (P < 0.05). Conclusively, lipid droplet accumulation was found in male and female pigeon crops from the end of incubation to the early stage of chick rearing. Although antioxidant defence and mitochondrial fatty acid β-oxidation were both mobilized, oxidative stress in crop tissues still occurred during the peak of milk formation.

Development of Electronic Wetting Front Detector for irrigation scheduling

Water is an important for plants growth, development and generating good crop yield. The rate of consumption of water by the crops depends on the type of crop, stage, soil characteristics and local climatic conditions. Irrigation scheduling takes care of when and how much water is to be applied to a crop during the entire the growing period. This calls for optimal water management, equipment, and labor in developing countries. An Electronic Wetting Front Detector was developed for irrigation scheduling by estimating the optimum soil moisture content present at the root-zone depth of the crops. The device measures the volumetric water content using electrical resistance of the soil and alarms the growers for the need of irrigation through its LED indicator installed in the device. The device was further calibrated and validated in the laboratory as well as in the farmer's field near Central University of Jharkhand, Ranchi, India. The device was tested and calibrated in the laboratory with the soil present in a container along with the Tensiometer fixed in the same container of soil and the data of the gravimetric soil moisture content was compared for accuracy by using regression analysis. Similarly, validation was also performed in the farmer's field and the result showed that it has a higher accuracy for measuring the soil water content. The developed device interprets readings from the sensors and alarms the farmer of overirrigation or underirrigation based on the sensors data. The EWFD developed is new intervention since the device could be easily used by farmer to understand the soil water content and schedule irrigation with respect to crop needs by alarming the farming. Therefore, the device may prove a valuable contribution to the list of other scientific instruments used for measuring the soil water content. The device can be used perfectly for scheduling irrigation.

Spatial distribution and risk assessment of fluorine and cadmium in rice, corn, and wheat grains in most karst regions of Guizhou province, China

The pollution status of crops planted in Guizhou province of Southwestern China with high background values of Fluorine (F) and Cadmium (Cd) has attracted people's concern. The present study aimed to investigate the spatial distributions of F and Cd in rice, corn and wheat grains, and further evaluate their health risks to residents in Guizhou province. The contents of F and Cd were measured by fluoride ion-selective electrode and inductively coupled plasma mass spectrometry (ICP-MS) methods, respectively. Additionally, the inverse distance weighted (IDW) technique was conducted to analyze spatial distribution, and the health risk was estimated by target hazard quotient (THQ) and hazardous index (HI). The results indicate that Cd contents in samples varied from 0.000 to 0.463 for rice, 0.000 to 0.307 for corn, and 0.012 to 0.537 (mg/kg) for wheat, while F contents ranged from 0.825 to 5.193 (rice), 0.946 to 8.485 (corn), and 0.271 to 9.143 (wheat) mg/kg. The Cd exceeding ratios were 11.600% for rice, 13.500% for corn, and 45.100% for wheat grains, respectively. In terms of spatial distribution, high levels of F and Cd in rice were found in the northern and central in Guizhou, while Cd in corn was distributed in the eastern and F in corn were distributed in the west area of Guizhou. Moreover, the high levels of F and Cd in wheat were distributed in the western and eastern areas. The mean carcinogenic risks (R) of Cd in rice, corn, and wheat in children were 4.150 × 10-4, 1.670 × 10-4 and 3.470 × 10-4, respectively, and that in adults were 3.430 × 10-4, 0.471 × 10-4, and 2.190 × 10-4, respectively. The HI for adults in rice, corn and wheat grains were 0.756, 0.154, and 0.514, respectively, and that for children were 0.913, 0.549, and 0.814, respectively. Collectively, the potential risks produced by F and Cd to the local residents should not be ignored.

Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices.

Plant nitrogen content (PNC) is an important indicator to characterize the nitrogen nutrition status of crops, and quickly and efficiently obtaining the PNC information aids in fertilization management and decision-making in modern precision agriculture. This study aimed to explore the potential to improve the accuracy of estimating PNC during critical growth periods of potato by combining the visible light vegetation indices (VIs) and morphological parameters (MPs) obtained from an inexpensive UAV digital camera. First, the visible light VIs and three types of MPs, including the plant height (H), canopy coverage (CC) and canopy volume (CV), were extracted from digital images of the potato tuber formation stage (S1), tuber growth stage (S2), and starch accumulation stage (S3). Then, the correlations of VIs and MPs with the PNC were analyzed for each growth stage, and the performance of VIs and MPs in estimating PNC was explored. Finally, three methods, multiple linear regression (MLR), k-nearest neighbors, and random forest, were used to explore the effect of MPs on the estimation of potato PNC using VIs. The results showed that (i) the values of potato H and CC extracted based on UAV digital images were accurate, and the accuracy of the pre-growth stages was higher than that of the late growth stage. (ii) The estimation of potato PNC by visible light VIs was feasible, but the accuracy required further improvement. (iii) As the growing season progressed, the correlation between MPs and PNC gradually decreased, and it became more difficult to estimate the PNC. (iv) Compared with individual MP, multi-MPs can more accurately reflect the morphological structure of the crop and can further improve the accuracy of estimating PNC. (v) Visible light VIs combined with MPs improved the accuracy of estimating PNC, with the highest accuracy of the models constructed using the MLR method (S1: R 2 = 0.79, RMSE=0.27, NRMSE=8.19%; S2:R 2 = 0.80, RMSE=0.27, NRMSE=8.11%; S3: R 2 = 0.76, RMSE=0.26, NRMSE=8.63%). The results showed that the combination of visible light VIs and morphological information obtained by a UAV digital camera could provide a feasible method for monitoring crop growth and plant nitrogen status.

Comparison of plant proximal sensing approaches for nitrogen supply detection in crops

AbstractNondestructive proximal sensors can be an efficient source of information of N status in crops for localized and rapid adjustment of fertilization applications. The aim of this study was to compare two transmittance/reflectance‐based sensors (SPAD, ASD) and a florescence‐based sensor (Multiplex) in their ability to measure N content in corn (Zea mays L.), spring and winter barley (Hordeum vulgare L.), and rye (Secale cereale L.), both at the leaf and canopy level. Measurements of leaves and canopies from six fertilization field trials in 2019 and 2020 were analyzed to establish relationships between sensor information and laboratory‐determined N content in crops. Analyses included linear regression for single sensor variables and machine learning for multivariate approaches, to assess the relative accuracy of the proximal sensors to measure N. The ASD is time‐intensive and requires post hoc analyses of the spectra. However, the spectral outputs of this device were clearly correlated with the N status of leaves and canopies. At the leaf level, SPAD showed higher accuracy than any of the single Multiplex variables to predict plant N. Multiplex performance could be improved by combining three of its variables. At the canopy level, interpolated SPAD values and the best‐performing Multiplex variables showed similar accuracy. It could be concluded that the relationship sensor‐N status is species specific. Despite the high standard deviation recorded in some raw Multiplex variable, the derived indices showed a comparable low standard deviation. At both, leaf and canopy levels an integrated sensor solution would combine the multidimensionality of Multiplex and ASD, and the accuracy and practicality of SPAD.

Bama Pig Manure Organic Fertilizer Regulates the Watermelon Rhizosphere Bacterial Community to Inhibit the Occurrence of Fusarium Wilt Under Continuous Cropping Conditions.

Fusarium wilt caused by Fusarium oxysporum f. sp. niveum is an important manifestation of continuous cropping barrier, which causes the quality and yield of watermelon to decline. In early stage of this study, the organic fertilizer fermented by Bama pig manure applied in soil was proved to significantly inhibit the occurrence of disease by improving the structure of soil microbial community. However, the mechanism was not clear. The high-throughput sequencing technology, combined with network and PICRUSt2 function analysis was used to investigate it. MiSeq sequencing showed that the bacterial community of organic fertilizer treated soil was composed of 34 phyla and 768 genera, the number of genera was higher than that of sterile water treated soil. Fertilization significantly increased the diversity and changed the composition of bacterial community based on alpha, beta diversity, and ANOSIM/Adonis analysis. LEfSe species difference and network analysis showed that fertilization improved the relative abundance of bacteria with biological control or plant growth promotion characteristics in soil, such as Sphingomonas, Halobacillus, Nocardioides, and enhanced the interaction between rhizosphere bacteria, made the network structure more complex. PICRUSt2 also revealed fertilization promoted the bacterial function, such as metabolism and genetic information processing. These results showed that the pig manure organic fertilizer might reduce the occurrence of Fusarium wilt by regulating bacterial community, interaction, and functional metabolism in watermelon rhizosphere soil.

Soybean response to organic fertilizer based on bird manure

Organic fertilizers based on chicken manure have a diverse composition of basic nutrients and microelements, which is their advantage over mineral fertilizers. The introduction of organic fertilizers obtained based on chicken manure improves the biological activity of the soil. The study aims to determine the effectiveness of using bird droppings as an organic fertilizer to conserve soil bioresources and increase soybean productivity. In the course of the work, the authors tested new organic fertilizers based on chicken manure obtained using a cavitationvortex heat generator by processing semi-dry manure. Their influence on soil microbiological indicators, growth, development, and biometric characteristics of soybean plants, phytosanitary condition of crops, productivity, and product quality was revealed. The authors also found that fertilizers based on chicken manure help to improve the microbiological characteristics of the soil, which increases the availability of nutrients for plants. The authors note a change in the number of mycomycetes at the beginning of the growing season: a decrease of 68% with the use of Preparation 1 and an increase of 35.6% with the use of nitrogen fertilizers. Pre-sowing introduction of preparations into the soil and processing during the growing season activate the vegetative development of plants and contribute to an increase in the yield of green mass and grain. The greatest effect was obtained when preparation 2 was applied under soybean. The increase in soybean grain yield was 0.2–0.3 t/ha or 8–14% of the control. The authors also found that treatment with drugs improves the structural parameters of soybean plants: the number of beans per plant increases by 6–8%; by 18–23% - the mass of grains from one plant; the safety of plants for harvesting increases by 2–4%.

UAV Remote Sensing Prediction Method of Winter Wheat Yield Based on the Fused Features of Crop and Soil

The early and accurate acquisition of crop yields is of great significance for maintaining food market stability and ensuring global food security. Unmanned aerial vehicle (UAV) remote sensing offers the possibility of predicting crop yields with its advantages of flexibility and high resolution. However, most of the existing remote sensing yield estimation studies focused solely on crops but did not fully consider the influence of soil on yield formation. As an integrated system, the status of crop and soil together determines the final yield. Compared to crop-only yield prediction, the approach that additionally considers soil background information will effectively improve the accuracy and reduce bias in the results. In this study, a novel method for segmenting crop and soil spectral images based on different vegetation coverage is first proposed, in which pixels of crop and soil can be accurately identified by determining the discriminant value Q. On the basis of extracting crop and soil waveband’s information by individual pixel, an innovative approach, projected non-negative matrix factorization based on good point set and matrix cross fusion (PNMF-MCF), was developed to effectively extract and fuse the yield-related features of crop and soil. The experimental results on winter wheat show that the proposed segmentation method can accurately distinguish crop and soil pixels under complex soil background of four different growth periods. Compared with the single reflectance of crop or soil and the simple combination of crop and soil reflectance, the fused yield features spectral matrix FP obtained with PNMF−MCF achieved the best performance in yield prediction at the flowering, flag leaf and pustulation stages, with R2 higher than 0.7 in these three stages. Especially at the flowering stage, the yield prediction model based on PNMF-MCF had the highest R2 with 0.8516 and the lowest RMSE with 0.0744 kg/m2. Correlation analysis with key biochemical parameters (nitrogen and carbon, pigments and biomass) of yield formation showed that the flowering stage was the most vigorous season for photosynthesis and the most critical stage for yield prediction. This study provides a new perspective and complete framework for high-precision crop yield forecasting using UAV remote sensing technology.

Participatory agroecological diagnosis in small and medium-sized producers in Michoacán and Morelos, Mexico

production units by identifying: (i) crop management practices, (ii) types of inputs and technologies used, and (iii) health status of soils and crops. Design/Methodology/Approach: This implemented research was of the type known as "In-depth case study", where the minimum sample size is 6 to 10 cases. Descriptive, correlational, and explanatory aspects corresponding to the selected production units were considered; based on qualitative and quantitative information. Results: For indicators of crop management and soil health, the predominant type of agriculture was transition to agroecology. Crop health indicators were the most agroecological. Study limitations/Implications: The results obtained allowed to establish the current state of the production units; with which further comparisons of the condition of those production units in the future can be made. Findings/Conclusions: The methodology used allowed to evaluate participatively, from an agroecological approach, the sustainability of soils and crops in production units of the municipalities Ario de Rosales, Michoacán and Tetela del Volcán, Morelos. Within the six productive units studied, it was found a productive unit with the appropriate characteristics to be considered as an "agroecological beacon".

Understanding the perceived indicators of food sovereignty and food security for rice growers and rural organizations in Mazandaran Province, Iran

BackgroundFood sovereignty and food security are inseparable from agricultural development policies, particularly regarding how to increase food production and productivity to meet future demand. This study investigates the status and perceptions of food sovereignty and food security of small-scale rice growers’ households in the Mazandaran Province of northern Iran. The study region is one of the most important places for domestic rice production, with nearly 230,000 hectares of rice lands in the country and 45% of total domestic rice production. The role of the Rice Research Institute of Iran (RRII) as an innovative rural institution was highlighted by the rice producers for contributing to food sovereignty and food security. A survey was distributed among 127 rice farmers’ households to obtain indicators of food sovereignty (localization of food systems, values for food providers, concentration of local control of the food system, building knowledge and skills, right to food, working with nature) and food security (availability, accessibility, utility, quality). Principal Component Analysis and Partial Correlation tests were used for finding the relationship between variables and focused indicators.ResultsResults show that food sovereignty in Northern Iran focused on localizing the food system by gaining access to financial assets and local markets, investing in human capital and local training and improving access to the water resources; food security focused on environmental and climate extension, increasing household’s revenues by improving food policies and food quality from rice farmers’ perspectives. In addition, the results of this study demonstrate the desire in Northern Iran for the role of innovative rural organizations as vital linkages between rice farmers and the public sector (i.e., Ministry of Agriculture Jihad).ConclusionsThis research shows that from the perspective of the surveyed rice growers in northern Iran, four main indicators that relate to investment and resources were the most significant: capital, markets, credit, and knowledge. Further studies are required for remote sensing monitoring of rice crop condition and yields, condition of irrigation systems, and geographic relationship of the agricultural infrastructure to food sovereignty and food security in northern Iran.