Crop Yield Variability Research Articles

Foreign yield shocks and domestic price variability: the case of maize in developing countries

International trade helps to smooth food price swings caused from seasonal imbalances between domestic supply and demand. Trade also increases the possibility of importing price volatility from abroad. This concern looms large in the face of increased crop yield variability associated with climate change. We assess the extent to which maize yield shocks in exporting countries exacerbate the intra-seasonal variability of maize prices in a cross section of 75 markets in Africa, Asia, and Latin American countries during 2000/01–2017/18. We find that extreme below-trend reductions in maize yields in exporting countries are associated with increased intra-annual maize price variability in the focus countries. In contrast, above-trend maize yields in exporting countries are associated with reduced variability.

Testing the Effects of Water-Saving Technologies Adapted to Drought: Empirical Evidence from the Huang-Huai-Hai Region in China

The aggravation of extreme weather events has dramatically increased the risk of severe water shortages and seriously threatened agricultural production. The Huang-Huai-Hai region, an important agricultural production region in China, is subject to a severe water shortage and is often hit by drought. As a result, water-saving technologies (WSTs) have been implemented. It remains unclear how effectively these WSTs can reduce crop yield loss, crop yield variation, and the loss of net crop income caused by water scarcity. Therefore, this paper aimed to analyze the role of WSTs in response to drought by establishing a multi-objective expected utility function based on 988 farmers across the Huang-Huai-Hai region. Econometric analysis employing an endogenous switching regression model showed that using WSTs can significantly reduce crop yield loss and net income loss caused by drought. Adopting household-based WSTs or community-based water-saving technology generates even greater positive effects on crop yield and farmers’ net income. Therefore, the government should promote farmers’ adoption of more advanced WSTs by increasing subsidies and strengthening policy support.

Forecasting global crop yields based on El Nino Southern Oscillation early signals

CONTEXTThe El-Niño Southern Oscillation (ENSO), one of the most well-known climate modes, can lead to large-scale climate variability and subsequent crop loss, posing a severe risk to global food security. OBJECTIVEThe study's main goal was to examine the synchronous impacts of ENSO and the probability of simultaneous ENSO–related crop loss on the global yields of major crops and investigate the predictability of finer-scale variation in crop yields based on ENSO-related large-scale climate precursors. METHODSHere, using updated crop census data for ∼12,000 political units, the study first investigated the synchronous impact of ENSO on yield variability of major crops (i.e., maize, rice, wheat, and soybean) using Synthetic Analysis and bootstrap method, and then estimated the probability of simultaneous crop loss in the top five crop-producing countries by copula approach. Finally, multiple regression was developed to identify the best forecast model, the corresponding ENSO indices, and the lead time for each political unit based on pre-occurred ENSO indices. RESULTS AND CONCLUSIONSThe results show that 12.8% (2.1%), 13.4% (6.4%), 11.8% (10.2%), and 8.4% (18.3%) of wheat, rice, maize, and soybean harvest areas were significantly negatively (positively) associated with El Niño, respectively; and 7% (11.7%), 20.2% (3.4%), 5.8% (5.6%), and 14% (6.4%) with La Niña. El Niño reduced global-mean crop yield by 1.32%, 1.33%, and 0.37% for wheat, rice, and maize, respectively, but increased it for soybean by 1.9%. La Niña reduced the global mean yield for rice (2.1%), maize (1.5%), and soybean (1.3%) but increased it for wheat (1.0%). Rice (6.6%) had the highest probability of simultaneous loss during the El Niño phase, whereas La Niña is soybean (5.9%). Based on the early ENSO signals, crop yield could be reliably forecasted for ∼32.05%, ∼42.2%, ∼21%, and ∼ 26.37% of global harvest areas, with R2 being 0.24, 0.26, 0.24, and 0.23 and a lead time of 1–12 months, for wheat, rice, maize, and soybean, respectively. The results suggest that although the reliable yield prediction based on ENSO indexes alone can be developed in a limited proportion of harvest areas, it is skillful in the ENSO-sensitive regions. SIGNIFICANCEThe findings improved the understanding of ENSO-induced crop yield variability and developed novel approaches to forecast global crop yields based on early ENSO signals.

Exploring drivers of within‐field crop yield variation using a national precision yield network

Abstract While abiotic drivers of yields represent important limiting factors to crop productivity, the role of biotic drivers that could be directly managed by farmers (e.g. agri‐environment schemes supporting key ecosystem services) remains poorly understood. Precision yield mapping provides an opportunity to understand the factors that limit agricultural yield through the interpretation of high‐resolution cropping data. This has the potential to inform future precision agricultural management, such as the targeted application of agrochemicals, promoting increased sustainability in modern agricultural systems. We used precision yield measurements from a network of 1174 fields in England (2006–2020) to identify drivers of within‐field yield variation in winter wheat and oilseed rape. Potential drivers included climate, topography and landscape composition and configuration. We then explored relationships between in‐field yield patterns and local landscape context, including the presence of features associated with ecosystem benefits. Proximity to the field edge was associated with reduced yields in 85% of wheat and 87% of oilseed fields. This translating to an approximate reduction of 10% in wheat and 18% in oilseed yields lost due to field edge effects. We found evidence that reduced yields at the field edges were associated with biotic features of the surrounding landscape, including the occurrence of semi‐natural habitats. Specifically, agri‐environment scheme (AES) presence increased the rate at which yields at field edges approach those of the field centres. This suggests that AES occurrence within a landscape (rather than field adjacent) may increase edge effects. However, these trends are unclear and suggest interactions between drivers and the spatial and temporal scale of investigation. Synthesis and applications. While we found evidence of landscape context mitigating against field edge effects, these were counterintuitive. For example, AES at a landscape scale appeared to increase the severity of edge effects. This study highlights a lack of environmental data at sufficiently high spatiotemporal resolution to match that of precision agriculture data. This mismatch is hindering the effective integration of precision agriculture data in an environmental policy and/or management context and potentially leading to unnecessarily poorly informed decisions related to AES deployment. This may limit environmental and economic benefits.

Effects of plant density on the aboveground dry matter and radiation-use efficiency of field corn.

The amount of solar radiation intercepted by the plant canopy drives crop plant photosynthesis and the formation and development of plant organs. Radiation-use efficiency (RUE) is an index used to quantify the relationship between solar radiation and biomass, and crop yield can be increased by increasing RUE. The main goals of this study were to initially investigate the effects of plant densities on the aboveground dry matter of corn, and subsequently examine the effects of plant densities on RUE and leaf area index (LAI), and the effects of LAI on RUE. Finally, we provide a comparative assessment of the approaches used to determine RUE. Analyses were conducted using growth and meteorological data obtained for two field corn varieties (TNG1 and TNG7) grown under four different plant density conditions in central Taiwan in 2017. The RUE values obtained in this study were primarily estimated from the slope of the linear relationship between aboveground dry matter measured at periodic harvests and the corresponding cumulative intercepted photosynthetically active radiation up to the time of harvest. TNG1 and TNG7 with a row spacing of 37.5 cm × 20 cm had the largest amounts of aboveground dry matter and highest RUE values of 4.41 and 4.55 g MJ-1, respectively. We established that the higher the plant density, the higher were the values obtained for RUE and LAI. We also compared the different methods of estimating RUE and make recommendations in this regard. Our findings in this study will enable farmers to gain information on the dynamics of crop yield variation at an early stage of growth, and also provide reference values that can be incorporated in future crop yield models.

Using the SCOPE model for potato growth, productivity and yield monitoring under different levels of nitrogen fertilization

Most applications of remote sensing in agricultural crop monitoring use multispectral imaging techniques, but with upcoming hyperspectral missions, the opportunity arises to better estimate pigment absorption and crop structure by exploiting the full solar reflective spectrum. In this study, we demonstrate how hyperspectral time series can be used with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model to estimate crop yield variability among fields, crop varieties and nitrogen treatments generically, i.e. without a calibration with in situ , data. Field experiments were conducted in two potato fields in the Netherlands between May and September 2019. The fields were planted with five varieties of potato, under three nitrogen fertilization treatments. By fitting the model to the full VNIR-SWIR spectrum of measured hyperspectral reflectance, we retrieved the model input parameters of Leaf Area Index (LAI), leaf chlorophyll content (Cab) and leaf water content (Cw) and simulated the photosynthesis throughout the season using data of local Automatic Weather Stations (AWS). Statistical analysis of measured and retrieved traits of LAI, Cab and canopy water content showed that two fields responded differently to the treatments, exhibiting fewer classes than were expected based on the experimental design. Potato yield, which was estimated as the sum of photosynthesis flux multiplied by the harvest index of 0.64, correlated with the measured tuber dry weight with R 2 0.36 and RMSE 2.5 t ha −1 . This study demonstrates that even in the absence of crop or variety specific information, hyperspectral reflectance and local weather data ingested into SCOPE can explain a substantial part of the observed variability in yield among fields. • Time series of hyperspectral canopy reflectance were recorded in two potato fields. • The variation in traits was created by nitrogen fertilization treatments. • SCOPE model retrieved plant traits contained information about treatments. • SCOPE-simulated accumulated photosynthesis correlated with the final dry yield. • SCOPE model is more generic than dimensionality reduction methods.

Sensitivity Analysis and Determination of the Optimal Level of Water Use Efficiency for Winter Wheat and Barley under Different Irrigation Scenarios Using the AquaCrop Model in Arid and Semiarid Climatic Conditions (Case Study: Dehloran Plain, Iran)

The AquaCrop model is one of the most recent models that can simulate the growth rates and yields of various crops based on water consumption levels. To determine the optimal irrigation level, data measured in two crop years (2018–2019) in different irrigation scenarios (full irrigation or 100% water requirement and 90, 80, 70, 60, and 50% irrigation) were calibrated and validated for arid and semiarid climatic conditions using the AquaCrop model. The model was calibrated to simulate the grain yields of winter wheat and barley using R2, RMSE, d, EF, and NRMSE statistical indicators. The obtained values of these indices were, respectively, 0.97, 3, 0.98, 0.94, and 4 for winter wheat and 0.98, 4, 0.92, 0.89, and 7 for barley. The model efficiency was also validated using crop harvest data in the crop year 2019. For grain yield simulation, the calculated values of R2, RMSE, d, EF, and NRMSE statistical indicators were, respectively, 0.99, 4, 0.97, 0.93, and 4.4 for winter wheat and 0.97, 7, 0.94, 0.91, and 9 for barley. The data of field and modeled samples were analyzed by analysis of variance (ANOVA) using the F-test, and significant results were obtained for both crops in all applied scenarios at the 95% level.

Last Decade Assessment of the Impacts of Regional Climate Change on Crop Yield Variations in the Mediterranean Region

The influence of regional climate change (CC) on agricultural production variance in the Mediterranean region has been discussed based on the assessment of the last decade. Most of the Mediterranean region has experienced frequent natural disasters, expanding population, increase in temperature, and increase in the surface of the Mediterranean Sea. Furthermore, the temperature in the Mediterranean area is rising 25% faster than the rest of the globe, and in the summer, it is warming 40% faster than the global average. Climate change can alter the food supply, restrict access to food, and degrade food quality. Temperature rises, changes in precipitation patterns, changes in severe weather events, and decreased water availability, for example, might all result in lower agricultural production. The fact that most Mediterranean nations rely on imported basic foodstuffs adds to the severity of the situation. Instability and insecurity of agricultural supply in the region might lead to massive population movement, transforming most Mediterranean nations into a global source of instability. Based on the experience of similar geographical locations, the article has highlighted the essential elements affecting crop productivity and the five domains of water, ecosystems, food, health, and security. Despite the region’s complexity, the Mediterranean region has been offered an overall assessment that predicts the best strategy for the best solution. Such an attempt describes a methodical integration of scientific discoveries to understand better the combined hazards illustrated by the fact that CC has affected food production, resulting in widespread insecurity. Utilizing current technologies in agricultural production has been recommended to support regional nations in reaching higher yields. The significance of this study could be realized by mitigating climatic shocks through a sustainable food production system to accomplish development goals in vulnerable nations.

Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model

Abstract A major challenge for food security worldwide is the large interannual variability of crop yield, and climate change is expected to further exacerbate this volatility. Accurate prediction of the crop response to climate variability and change is critical for short-term management and long-term planning in multiple sectors. In this study, using maize in the U.S. Corn Belt as an example, we train and validate multiple machine learning (ML) models predicting crop yield based on meteorological variables and soil properties using the leaving-one-year-out approach, and compare their performance with that of a widely used process-based crop model (PBM). Our proposed long short-term memory model with attention (LSTMatt) outperforms other ML models (including other variations of LSTM developed in this study) and explains 73% of the spatiotemporal variance of the observed maize yield, in contrast to 16% explained by the regionally calibrated PBM; the magnitude of yield prediction errors in LSTMatt is about one-third of that in the PBM. When applied to the extreme drought year 2012 that has no counterpart in the training data, the LSTMatt performance drops but still shows advantage over the PBM. Findings from this study suggest a great potential for out-of-sample application of the LSTMatt model to predict crop yield under a changing climate. Significance Statement Changing climate is expected to exacerbate extreme weather events, thus affecting global food security. Accurate estimation and prediction of crop productivity under extremes are crucial for long-term agricultural decision-making and climate adaptation planning. Here we seek to improve crop yield prediction from meteorological features and soil properties using machine learning approaches. Our long short-term memory (LSTM) model with attention and shortcut connection explains 73% of the spatiotemporal variance of the observed maize yield in the U.S. Corn Belt and outperforms a widely used process-based crop model even in an extreme drought year when meteorological conditions are significantly different from the training data. Our findings suggest great potential for out-of-sample application of the LSTM model to predict crop yield under a changing climate.

DETERMINATION OF WEED DENSITY AND FREQUENCY AT DIFFERENT SITES OF AGRICULTURE RESEARCH FORM, PESHAWAR.

A survey was conducted in the month of January 2015, to record the weed density and frequency in different crops in Agriculture Research Farm. A total two farms randomly selected for the survey. The data were recorded on the density m-2and the frequency (%). Quadrate method was use for recording data. In the first field maximum value of density m-2 (93) were recorded for Cynodon dactylon while the minimum value of density m-2 (6) were recorded for the Convolvulus arvensis and Melilotus indica. Similarly the maximum value of frequency (80%) were recorded for the Cynodon dacrylon while the minimum value of frequency (20%) were recorded for the Convolvulus arvensis . In the second field the maximum value of density m-2(56) were recorded for the Euphorbia helioscopia while the minimum value of density m-2 (6) were recorded for the Silybum marianum. Similarly the maximum value of frequency (100%) were recorded for Euphorbia helioscopia while the minimum value of frequency (20%) were recorded for fumaria indica, Silybum marianum,Coronopus didymus and Sonchus olereaceus . It is concluded that the weed prevailing in an area should be identify and managed to harvest potential yields of various crops.

Use of Active Sensors in Coffee Cultivation for Monitoring Crop Yield

Monitoring the spatial variability of agricultural variables is a main step in implementing precision agriculture practices. Active optical sensors (AOS), with their instrumentation directly on agricultural machines, are suitable and make it possible to obtain high-frequency data. This study aimed to evaluate the potential of AOS to map the spatial and temporal variability of coffee crop yields, as well as to establish guidelines for the acquisition of AOS data for sensing the sides of a coffee plant, allowing the evaluation of large commercial fields. The study was conducted in a commercial coffee area of 10.24 ha, cultivated with the Catuaí 144 variety. Data collection was performed with six Crop Circle ACS 430 sensors (Holland Scientific, Lincoln, NE, USA) and two N-Sensor NG sensors (Yara International, Dülmen, Germany). Seven field expeditions were made to collect data using the optical sensors during 2019 and 2021, obtaining data during the flowering, fruit-filling and fruit maturation phases (pre-harvest), and post-harvest. The results showed that the different faces of the same plant present a different Pearson’s correlation coefficient (r) to its yield, obtained with a yield monitor on the harvester. The face with the highest exposure to solar radiation presented a slightly higher correlation to yield (−0.34 ≤ r ≤ −0.17) when compared with the face with less exposure (−0.27 ≤ r ≤ −0.15). In addition, it was observed that the vegetation indices measured at the beginning of the coffee cycle (before the rainy season that starts in October) present a positive correlation to the coffee yield of that same year (0.73 ≤ r ≤ 0.91). On the other hand, this relationship is changed after the beginning of the rain season, at which time the vegetation index increases abruptly, inverting the correlation with the yield after that (−0.93 ≤ r ≤ −0.77). Furthermore, it was observed that, due to the biennial nature of coffee production, the vegetation index acquired at a specific time has an inverted relationship when compared with the yield of that year and to the yield of the following (or previous) year.

Brassinosteroids enhance resistance to manganese toxicity in Malus robusta Rehd. via modulating polyamines profile

Manganese (Mn) toxicity in soil is a widely observed phenomenon, which seriously restricts growth, quality, and yield of various crops and fruits including apples. However, mechanisms underlying the regulation of polyamines (PAs) by brassinosteroids (BRs) to improve tolerance to Mn stress are still unclear. In this study, we investigated the effects of 2,4-epibrassinolide (EBL; a BR) on the expression of genes involved in BR signaling pathway, Mn accumulation, PAs-mediated responses (PA precursor levels, metabolic enzymes, and genes), and growth parameters in Mn-stressed Malus robusta Rehd. EBL application significantly modulated the expressions of genes related to BR signaling (MdBRI, MdBSK, etc.) and reduced Mn accumulation, along with improving the rate of increase in root length and plant height, relative water content, chlorophyll content, maximum photochemical efficiency of PSII (Fv/Fm), and actual photochemical efficiency (ΦPSII) and decreasing electrical conductivity. Furthermore, EBL application significantly reduced putrescine (Put) accumulation and increased spermine (Spm) content and (Spd + Spm)/Put ratio. EBL weakened ornithine (Orn) pathway, decreased ornithine decarboxylase (ODC) activity, and increased biosynthesis of Spm from Put via elevating the PA oxidase (PAO) activity and expression of MdSPDS, MdSPMS, and MdPAO. The trends for free, PS-conjugated, and PIS-bound PAs were similar to that of total PAs, except that no significant change was observed in free Spm, PS-conjugated Spd, and Spm, as well as PIS-bound Spd. This study revealed that BR-regulated PAs help in mitigating Mn toxicity and clarified the mechanisms of regulation of PAs by BRs in apple trees.

Crop Yield Loss Risk Is Modulated by Anthropogenic Factors.

High crop yield variation between years-caused by extreme shocks on the food production system such as extreme weather-can have substantial effects on food production. This in turn introduces vulnerabilities into the global food system. To mitigate the effects of these shocks, there is a clear need to understand how different adaptive capacity measures link to crop yield variability. While existing literature provides many local-scale studies on this linkage, no comprehensive global assessment yet exists. We assessed reported crop yield variation for wheat, maize, soybean, and rice for the time period 1981-2009 by measuring both yield loss risk (variation in negative yield anomalies considering all years) and changes in yields during "dry" shock and "hot" shock years. We used the machine learning algorithm XGBoost to assess the explanatory power of selected gridded indicators of anthropogenicfactors globally (i.e., adaptive capacity measures such as the human development index, irrigation infrastructure, and fertilizer use) on yield variation at a 0.5° resolution within climatically similar regions (to rule out the role of average climate conditions). We found that the anthropogenic factors explained 40%-60% of yield loss risk variation across the whole time period, whereas the factors provided noticeably lower (5%-20%) explanatory power during shock years. On a continental scale, especially in Europe and Africa, the factors explained a high proportion of the yield loss risk variation (up to around 80%). Assessing crop production vulnerabilities on global scale provides supporting knowledge to target specific adaptation measures, thus contributing to global food security.

Influence of climate changes on the geographical rapeseed growing conditions (case of Kharkiv region)

The purpose of the article is to determine the impact of changes in climatic conditions on the cultivation of rapeseed (on the example of Kharkiv region). Main material. Dependence on weather conditions affects the stability of the country’s agricultural production. Recently, there has been an increase in the annual and seasonal average air temperature, which already has a noticeable effect on the gross yields of various crops. The relevance of the study of rapeseed productivity depending on changes in climatic conditions lies in the fact that climate change has become one of the most important problems today. The negative consequences of this change, such as an increase in droughts, flood levels, and a decrease in the yield of various crops, significantly affect economic activity and threaten the food security of the world population, creating potentially unforeseen problems for the modern world. The research can reveal what climatic conditions have the greatest influence on the productivity of rapeseed in Kharkiv region, and what changes should be made to crop rotation and agricultural techniques in order to maintain high yields. Dissemination of knowledge from this research can contribute to reducing the negative effects of climate change on rapeseed and increasing plant productivity. Conclusions. The topic of the research on rapeseed yield indicators is quite relevant and important, aimed at maintaining food security and increasing the production of this important oil crop in the face of climate change. The results of the study can be useful for farmers, agronomists, scientists and government agencies dealing with agriculture and food issues. The study of the subject will allow us to determine what agricultural technologies are the most effective for the cultivation of rapeseed in the conditions of climate change and what measures can be taken to increase the yield and economic efficiency of the rapeseed cultivation.

Higher landscape diversity associated with improved crop production resilience in Kansas-USA

Cropland expansion and agriculture intensification have been the primary methods to increase food production since the 19th century. The resulting landscape simplification, however, can impede long-term agricultural crop productivity. This paper examines the role of landscape diversification on resilience of corn, soy, and winter wheat production, in terms of crop yield and yield variability, in the state of Kansas. The study uses panel data analysis with county fixed effects and time trends to estimate the relationship between landscape diversity and crop production resilience. Results show that diversity has a significant positive effect on yields after controlling for weather, irrigation, and chemical inputs. We find that the yields of winter wheat increase, on average, by 28% at high levels of landscape diversity, while corn and soy yields increase 7% and 5%, respectively. In addition, we find that increases in landscape diversity are significantly associated with reduced yield variability for winter wheat and corn, and that landscape diversity mitigates the effect of extreme weather conditions on yield. Study findings suggest that within a single, relatively low diversity state, increasing landscape diversity is positively associated with crop production resilience. Future extreme climate conditions may reduce crop yields and yield stability, requiring appropriate policies to ensure food security. Our findings suggest that landscape diversification may be an important tool within a portfolio of approaches to increase crop resilience under highly variable weather conditions.

Animal pollination increases stability of crop yield across spatial scales.

The benefits of animal pollination to crop yield are well known. In contrast, the effects of animal pollination on the spatial or temporal stability (the opposite of variability) of crop yield remain poorly understood. We use meta‐analysis to combine variability information from 215 experimental comparisons between animal‐pollinated and wind‐ or self‐pollinated control plants in apple, oilseed rape and faba bean. Animal pollination increased yield stability (by an average of 32% per unit of yield) at between‐flower, ‐plant, ‐plot and ‐field scales. Evidence suggests this occurs because yield benefits of animal pollination become progressively constrained closer to the maximum potential yield in a given context, causing clustering. The increase in yield stability with animal pollination is greatest when yield benefits of animal pollination are greatest, indicating that managing crop pollination to increase yield also increases yield stability. These additional pollination benefits have not yet been included in economic assessments but provide further justification for policies to protect pollinators.

Estimation of Rice Yield Considering Heading Stage Using Satellite Imagery and Ground-Based Data in Indonesia

Understanding the temporal and spatial variability in crop yield is considered as one of the key steps in agricultural risk assessment. Therefore, a study of an irrigated area in Cihea, West Java, Indonesia, was conducted to assess rice yield per field using SENTINEL-2 imagery and yield observation data in 2018 and 2019. The study area is located in the Citarum River basin. SENTINEL-2 images were used to derive paddy rice’s growth curve and estimate rice growth stages based on the normalized difference vegetation index. Using these results, the regression model formula using Band 4 (665 nm) and the normalized difference water index in the ripening stage was created (R2 = 0.40, RMSE = 1.21 t/ha). The results from this model were used to generate yield maps, which illustrated a distinct spatial variation in rice yield, such as the average rice productivity in the study area was relatively high, however, the difference between years tended to be small in the upper stream area. The results of this study show that this method is effective in this area to monitor rice yield condition and distribution.Understanding the temporal and spatial variability in crop yield is considered as one of the key steps in agricultural risk assessment. Therefore, a study of an irrigated area in Cihea, West Java, Indonesia, was conducted to assess rice yield per field using SENTINEL-2 imagery and yield observation data in 2018 and 2019. The study area is located in the Citarum River basin. SENTINEL-2 images were used to derive paddy rice’s growth curve and estimate rice growth stages based on the normalized difference vegetation index. Using these results, the regression model formula using Band 4 (665 nm) and the normalized difference water index in the ripening stage was created (R2 = 0.40, RMSE = 1.21 t/ha). The results from this model were used to generate yield maps, which illustrated a distinct spatial variation in rice yield, such as the average rice productivity in the study area was relatively high, however, the difference between years tended to be small in the upper stream area. The results of this study show that this method is effective in this area to monitor rice yield condition and distribution.

Exploring crop yield variability under different land management practices with spectral vegetation indices in the Ethiopian Blue Nile basin

An accurate crop yield forecast at a reasonable cost is required for increasing production and securing food. Therefore, spectral reflectance was measured with a spectroradiometer (λ 350–2500 nm) to predict grain yield (GY) and aboveground biomass yield (ABY) of teff and finger millet in Ethiopia. We calculated six spectral vegetation indices (SVIs): enhanced vegetation index (EVI), normalized difference VI (NDVI), soil-adjusted VI (SAVI), green (GNDVI), green chlorophyll VI (GCVI), and simple ratio (SR). Linear regression models fitted validation data with coefficient of determination (R2) of 0.73, and root mean square error (RMSE) of 0.11 for teff GY, R2 of 0.45 and RMSE of 0.52 for teff ABY, R2 of 0.92 and RMSE of 0.22 for finger millet GY, and R2 of 0.90 and RMSE of 0.24 for finger millet ABY. The study demonstrates the potential of spectroradiometer-derived SVIs to predict finger millet and teff production in the catchment.

APPLICATION OF THE STANDARDIZED PRECIPITATION INDEX FOR THE ASSESSMENT AND FORECAST OF ATMOSPHERIC DROUGHTS IN ALTAI KRAI

The research objective is to identify and analyze the possibility of using the Standardized Precipitation Index (SPI) in Altai Krai for agroclimatic and meteorological research, including forecasting climate change and predicting the yield of various crops. An agroclimatic assessment of atmospheric droughts within the conditions of Altai Krai was performed using a method relatively new for national agrometeorology. At the moment, in the Russian Federation and the CIS countries, the G. T. Selyaninov Hydrothermic Factor (HF) is mainly used for assessing droughts, which makes it possible to assess the territory water availability by the ratio of the period characteristics with the temperature above 10°С. The authors have compiled the SPI calculator in the Microsoft Excel software environment. The paper presents the graphs of the moisturization course for the period of the active growing season for 31 meteorological stations of Altai Krai, and the frequency of arid events is calculated for compiling a precipitation model for the period of 1964–2020. The paper does not provide formulas described in detail in literature sources. The research results can be used in planning activities in the sphere of agriculture, developing an economic strategy for the region, and strategy in relation to adaptation to climate change. Areas are highlighted that are priority-oriented for taking measures to prevent the adverse consequences of climate change. General tendencies of changes in agrometeorological indicators have been found in those areas where they are the greatest ones. A comparison is made with the work results regarding other entities of the Russian Federation and the near abroad, wherein the possibility of using the SPI method for assessing droughts is analyzed.

Wheat Yield Modelling using Infocrop and DSSAT Crop Simulation Models

Background: Wheat is an important crop of India That is grown in the winter season (October to April). A field study was conducted for nine plots having a dimension of 3 × 2 meter and different nutrient was applied to test the biomass yield. The objectives are to describe (a): To conduct field experiments under controlled conditions and vary the dosage of fertilizers for different treatments. (b): To observe the variation in crop yield of wheat with different dosages of fertilizers. (c): To model the crop yield of different treatments using InfoCrop and DSSAT crop models. Methods: An experiment was conducted for Wheat crop having a dimension of 3 × 2 meter at the Indian Institute of Technology, UK-India. Three plots (C1, C2, C3) was given control fertilizer supply while plot (F1, F2, F3) given -25%, +25%, +50% and plot (N1, N2, N3) given -25%, +25%, +50% w.r.t to control. The simulation model Infocrop and DSSAT were simulated to check test the model. Result: In this study, two crop simulation models namely Infocrop and DSSAT were used to predict the yield of wheat crops. Field experiments were carried out at a farm site at Roorkee, India and different treatments of fertilizers are applied to the field plots. In total, seven fertilizer treatments are used: C (controlled with a normal dosage of fertilizers), F1, F2, F3 (varying amount of applied farmyard manure), N1, N2 and N3 (varying amount of applied urea). The objective of the study was to evaluate yields of the seven treatments using Infocrop and DSSAT crop models and compare them to the observed values of yields. The grain yield of the controlled treatment (C1, C2, C3) was predicted accurately by both models. There was a significant variation in the grain yields of the F1, F2, F3 treatments with the application of farm yard manure (FYM). DSSAT under-predicted the grain yields for the plot F1, F2, F3 treatments and over-predicted these for N1, N2, N3 treatments. Overall, Infocrop, which was developed and validated for Indian conditions, simulated the grain yield more accurately as compared to the DSSAT model under varying applications of FYM and urea dosages.