Yield Variability Research Articles

Weather indices during reproductive phase explain wheat yield variability

AbstractWhen water and nutrients are not limiting, and pests and disease are effectively controlled, crop growth and yield is determined by weather conditions such as temperature and solar radiation. To determine the relationship between weather indices and crop yield, multiple wheat varieties were sown at two sowing times, for five sowing seasons and at two locations. The following weather indices around the 50% anthesis stage were recorded and analysed: mean temperature (Tmean), maximum temperature (Tmax), number of days with temperature >30°C (T30), vapour pressure deficit (VPD), photosynthetically active radiation, photothermal quotient (PQ) and photothermal quotient corrected for vapour pressure deficit (PQvpd). Overall, for every 1°C rise in temperature, crop yield decreased by 370 kg/ha. For every 1°C rise in temperature, normal sowing window yield decreased by 360 kg/ha while late-sown wheat yield decreased by 640 kg/ha. Correlation analysis was conducted between the weather indices and grain number, grain yield and grain protein. There was a significant positive correlation between PQ and PQvpd and grain number and grain yield. There was a significant negative correlation between Tmean, Tmax, T30 and VPD and grain number and grain yield. Grain protein content showed a positive correlation with maximum air temperature and a negative correlation with the weather indices PQ and PQvpd. PQ and PQvpd can be used to predict grain number and grain yield potential. This study showed that grain number and grain yield predicted using PQ and PQvpd are more reliable than using temperature and radiation individually.

Unraveling the genomic background of resilience based on variability in milk yield and milk production levels in North American Holstein cattle through genome-wide association study and Mendelian randomization analyses

Breeding more resilient animals will benefit the dairy cattle industry in the long term, especially as global climate changes become more severe. Previous studies have reported genetic parameters for various milk yield-based resilience indicators, but the underlying genomic background of these traits remain unknown. In this study, we conducted genome-wide association studies of 62,029 single nucleotide polymorphisms (SNPs) with 4 milk yield-based resilience indicators, including the weighted occurrence frequency (wfPert) and accumulated milk losses (dPert) of milk yield perturbations, and log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily yield residuals. These variables were previously derived from 5.6 million daily milk yield records from 21,350 lactations (parities 1 to 3) of 11,787 North American Holstein cows. The average daily milk yield (ADMY) throughout lactation was also included to compare the shared genetic background of resilience indicators with milk yield. The differential genetic background of these indicators was first revealed by the significant genomic regions identified and significantly-enriched biological pathways of positional candidate genes, which confirmed the genetic difference among resilience indicators. Interestingly, the functional analyses of candidate genes suggested that the regulation of intestinal homeostasis is most likely affecting resilience derived based on variability in milk yield. Based on Mendelian randomization analyses of multiple instrumental SNPs, we further found an unfavorable causal association of ADMY with LnVar. In conclusion, the resilience indicators evaluated are genetically different traits and there are causal associations of milk yield with some of the resilience indicators evaluated. In addition to providing biological insights into the molecular regulation mechanisms of resilience derived based on variability in milk yield, this study also indicates the need for developing selection indexes combining multiple indicator traits and taking into account their genetic relationship for breeding more resilient dairy cattle.

Epi Source-Drain Damage Mitigation During Channel Release of Stacked Nanosheet Gate-All-Around Transistors

Nanosheet gate-all-around devices have demonstrated several advantages in device performance and area scaling over finFET devices with higher device density and improved electrostatic control. Robust inner spacer (IS) and channel formation is critical for high performance, reduced variability and good yield. An isotropic dry etch of the sacrificial SiGe layer with extremely high selectivity to gate spacer, IS and Si channels is necessary for high-quality channel formation over a wide range of sheet widths. Furthermore, the nFET Si:P and pFET SiGe:B source-drain (S/D) epitaxy must be isolated using inner spacers or buffers to prevent damage during Channel Release (CR). The damage can be further mitigated with optimized CR etch chemistry, enabling IS scaling. We highlight S/D damage mechanisms during CR, then demonstrate reduced S/D damage by co-optimization of the IS, CR chemistry and S/D epitaxy.

Statistical-dynamical modeling of the maize yield response to future climate change in West, East and Central Africa using the regional climate model REMO

Africa is vulnerable to the impacts of climate change, particularly in terms of its agriculture and crop production. The majority of climate models project a negative impact of future climate change on crop production, with maize being particularly vulnerable. However, the magnitude of this change remains uncertain. Therefore, it is important to reduce the uncertainties related to the anticipated changes to guide adaptation options. This study uses a combination of local and large-scale empirical orthogonal function (EOF) predictors as a novel approach to model the impacts of future climate change on crop yields in West, East and Central Africa. Here a cross-validated Bayesian model was developed using predictors derived from the regional climate model REMO for the period 1982–2100. On average, the combined local and large-scale EOF predictors explained around 28 % of maize yield variability from 1982 to 2016 of the entire study regions. Notably, climate predictors played a significant role in West Africa, explaining up to 51 % of the maize yield variability. Large-scale climate EOF predictors contributed most to the explained variance, reflecting the role of regional climate in future maize yield variability. Under a high-emissions scenario (RCP8.5), maize yield is projected to decrease over the entire study region by 20 % by the end of the century. However, a minor increase is projected in eastern Africa. This study highlights the importance of incorporating climate predictors at various scales into crop yield modeling. Furthermore, the findings will offer valuable guidance to decision-makers in shaping adaptation options.

Distinct soil health indicators are associated with variation in maize yield and tile drain nitrate losses

AbstractAssociations between nitrogen (N) management and losses with soil health indicators (SHI) are widely presumed but relatively untested. An on‐farm experiment conducted in central Illinois was conducted to test potential relationships of SHI with agroecosystem outcomes of maize (Zea mays L.) yield and N losses under bounding N‐fertilization rates of 168 and 252 kg/ha. Chemical (n = 19), physical (n = 11), and biological (n = 14) SHI were measured at 24 locations within a 30 ha field at five timepoints (V3, V10, RT, R6, and post‐harvest). Yields did not necessarily reflect N‐fertilization rates, with lowest yields (14.5 Mg/ha) under 224 kg/ha. Flow‐weighted nitrate‐N concentrations were significantly higher under 168 kg N/ha (10.6 mg/L) relative to higher application rates, though cumulative tile nitrate‐N loads were similar. SHI varied more by sampling location and time than by N fertilization rate. Depending on the time of sampling, distinct SHI were related to yield and tile N losses. Total soil carbon and permanganate oxidizable carbon (POXC) best explained yield variation, whereas POXC and sand content best explained variation in nitrate‐N loss. Nematode indices helped explain variability in yield (Simpson and Shannon indices) and nitrate‐N losses (maturity index), supporting recent propositions to integrate nematode measures into soil health assessments. This study provides a basis for expanding to multiyear assessments of SHI linkages with nutrient losses and crop productivity in the North Central United States.

Influence of small-scale spatial variability of soil properties on yield formation of winter wheat

BackgroundWith the increasing development of sophisticated precision farming techniques, high-resolution application maps are frequently discussed as a key factor in increasing yield potential. However, yield potential maps based on multiple soil properties measurements are rarely part of current farming practices. Furthermore, small-scale differences in soil properties have not been taken into account.MethodsTo investigate the impact of soil property changes at high resolution on yield, a field trial has been divided into a sampling grid of 42 plots. The soil properties in each plot were determined at three soil depths. Grain yield and yield formation of winter wheat were analyzed at two sites.ResultsMultiple regression analyses of soil properties with yield measures showed that the soil contents of organic carbon, silt, and clay in the top and subsoil explained 45–46% of the variability in grain yield. However, an increasing clay content in the topsoil correlated positively with grain yield and tiller density. In contrast, a higher clay content in the subsoil led to a decrease in grain yield. A cluster analysis of soil texture was deployed to evaluate whether the soil´s small-scale differences caused crucial differences in yield formation. Significant differences in soil organic carbon, yield, and yield formation were observed among clusters in each soil depth.ConclusionThese results show that small-scale lateral and vertical differences in soil properties can strongly impact crop yields and should be considered to improve site-specific cropping techniques further.

Genetic variability and correlation studies of yield and yield related attributes in BC2F2 population of sweet corn

The present study was carried out to study the genetic variability, heritability, genetic advance as percent of mean, correlation, skewness, and kurtosis of the backcross population BC2F2 derived from the cross SCM-Se-Sh-2 × DBT 17. High heritability coupled with high genetic advance was observed for cob girth, number of kernels per row, cob weight, 100 grain weight and single plant yield, suggesting additive gene action and selection of progenies based on this trait will result in higher genetic gain. Association studies showed that single plant yield was positively correlated with all yield attributes, viz., cob weight, number of kernels per row, 100 grain weight and plant height. These results depicted that cob weight, numbers of kernels per row and 100 grain weight may be used as reliable criteria for improving grain yield. Single plant yield, 100 grain weight, cob weight and plant height showed positive skewness and platykurtic distribution. This indicates that traits are governed by a large number of genes, and most of the genes exhibited complementary gene action, suggesting rigorous selection to achieve genetic gain. Overall, it is seen that this population has potential for yield improvement with enriched beta-carotene content.

Estimating yield-contributing physiological parameters of cotton using UAV-based imagery.

Lint yield in cotton is governed by light intercepted by the canopy (IPAR), radiation use efficiency (RUE), and harvest index (HI). However, the conventional methods of measuring these yield-governing physiological parameters are labor-intensive, time-consuming and requires destructive sampling. This study aimed to explore the use of low-cost and high-resolution UAV-based RGB and multispectral imagery 1) to estimate fraction of IPAR (IPARf), RUE, and biomass throughout the season, 2) to estimate lint yield using the cotton fiber index (CFI), and 3) to determine the potential use of biomass and lint yield models for estimating cotton HI. An experiment was conducted during the 2021 and 2022 growing seasons in Tifton, Georgia, USA in randomized complete block design with five different nitrogen treatments. Different nitrogen treatments were applied to generate substantial variability in canopy development and yield. UAV imagery was collected bi-weekly along with light interception and biomass measurements throughout the season, and 20 different vegetation indices (VIs) were computed from the imagery. Generalized linear regression was performed to develop models using VIs and growing degree days (GDDs). The IPARf models had R2 values ranging from 0.66 to 0.90, and models based on RVI and RECI explained the highest variation (93%) in IPARf during cross-validation. Similarly, cotton above-ground biomass was best estimated by models from MSAVI and OSAVI. Estimation of RUE using actual biomass measurement and RVI-based IPARf model was able to explain 84% of variation in RUE. CFI from UAV-based RGB imagery had strong relationship (R2 = 0.69) with machine harvested lint yield. The estimated HI from CFI-based lint yield and MSAVI-based biomass models was able to explain 40 to 49% of variation in measured HI for the 2022 growing season. The models developed to estimate the yield-contributing physiological parameters in cotton showed low to strong performance, with IPARf and above-ground biomass having greater prediction accuracy. Future studies on accurate estimation of lint yield is suggested for precise cotton HI prediction. This study is the first attempt of its kind and the results can be used to expand and improve research on predicting functional yield drivers of cotton.

Intra-seasonal rainfall variability and crop yield in the Upper East Region of Ghana

Intra-seasonal rainfall variability and crop yield in the Upper East Region of Ghana

Estimation of variability, heritability, and genetic advance among Ethiopian coriander (Coriandrum sativum L.) accessions

The objective of this study was to assess the extent of variability of yield and yield-related traits, heritability, and genetic advances among Ethiopian coriander accessions. The experiment was conducted at Kulumsa Agricultural Research Center, Southeast Ethiopia, during 2019- 2020 cropping seasons. Twenty-five Ethiopian coriander accessions were tested in a 5 x 5 simple lattice design with two replications; and data were collected on eight yield and yield-related traits. The combined analysis of variance revealed highly significant differences (P<0.01) between coriander accessions for each trait. The results showed a wide range of variability in all studied traits, indicating the potential for simultaneous improvement of yield and yield-related traits. The heritability (H2b) estimates ranged from low for seeds per umbellet and thousand seed weight to very high, for seed yield per hectare and number of umbels per plant in the first and second cropping season, respectively. The highest genetic advances as a percentage of the mean (GAM (%)) were observed for seed yield per plant, seed yield per hectare, and number of umbellets per umbel. These findings suggest that cultivar selection based on these traits with high H2b coupled with GAM (%) could enhance progeny performance and increase the productivity and profitability of coriander. Therefore, the results had practical implications for coriander breeders and farmers in Ethiopia and could contribute to the development and sustainable coriander farming practices. Further investigations are necessary to identify superior accessions with desirable processing qualities, assess the phenotypic and genotypic diversity among Ethiopian coriander accessions.

Genotype and agronomic management interaction to enhance wheat yield and water use efficiency in the Mediterranean rainfed environment of Morocco: I. Field data analysis

Durum wheat (Triticum turgidum subsp. durum), considered better drought tolerant, is the most cultivated wheat in Morocco and in the Middle East and North Africa (MENA) region. The region, including Morocco, predominantly has rainfed production systems, declining water supply, and increasing trends and effects of rainfall variability and climate extremes leading to poor crop yield and yield stability. The objectives of this study were to understand major factors determining wheat yield and water use efficiency (WUE); assess the interaction of genotype × environment × management on crop yield and field- and crop-water use efficiencies; and determine the water-limited yield gaps of genotypes under different agronomic management practices in the Mediterranean climate of Morocco. Four years (2015/16–2018/19) of on-station experiments investigating genotype (ten genotypes including seven advanced line and three commercial varieties), seeding time (17 November vs. 29 December), and water management (rainfed vs. supplementary irrigation), was conducted at Merchouch research station, Morocco. The results showed that durum wheat yield mostly varied due to year (rainfall) by 43%, followed by water management × year (23%), year × seeding time (15%), and genotype (7%), with the highest yield (7.15 t ha−1) observed in 2018 (wet year) and the lowest in 2019 (dry year). Across years, not only the rainfall amount but also its distribution during the crop growing season caused yield variability. In low-rainfall years, supplementary irrigation (28–166 mm) increased yield by 2.12–3.27 t ha−1 compared to rainfed conditions. The significant (p < 0.01) year × seeding time × genotype effect on grain yield and WUEs indicates that the response of genotype and seeding time varied with rainfall amount and distribution as the water-limited yield gap in rainfed conditions appeared to be more than 2 t ha−1. In both supplementary irrigated and rainfed systems, the machine learning model showed the effect of different climatic (rainfall and temperature) and management (seeding time, genotype, and irrigation) factors determining yield, yield attributes, and WUEs. The total rainfall remained the most important factor explaining variation in rainfed wheat yield followed by the temperature at flowering, mid- and early-season rainfall, and genotype in order of importance. Similarly, seasonal rainfall was the most important factor explaining variation in grain yield followed by early-season rainfall, days to flowering, temperature at flowering, irrigation amount, seeding time, and genotype in supplementary irrigated durum wheat. Our study clearly shows that yield is highly related to rainfall in rainfed drylands, and the selection of high WUE variety and crop management practices are the key to improving the resilience and sustainability of durum wheat in Morocco and similar production environments in the MENA region.

Урожайность и адаптивность сортов гороха в условиях подтайги Северного Зауралья

Abstract. The purpose of the study is a comparative characteristic of the yield of peas in the production and state variety testing of the Tyumen region, as well as a comprehensive assessment of pea varieties by yield and adaptability parameters in the conditions of a subtaiga of the region. Methods. The studies were carried out on the basis of statistical data of the pea yield in the production and state variety testing of the Tyumen region for 2017–2021, as well as data on the results of state variety testing of the varieties admitted to use for 2019–2021 under the conditions of a subtaiga (Nizhne-Tavdinskiy and Aromashevskiy STP). Coefficient of index of environmental conditions (Ij), stress tolerance (Y2 – Y1), yield variability (v, %), plasticity (bi), stability (Si2) and general adaptive ability (GAA) were calculated. Results. The variety Bagu (30.5 and 33.0 c/ha, respectively) was the best in terms of average yield and average yield in contrast conditions, and in terms of realizing the yield potential the variety Kumir was the best (74.7 %). Stress tolerance is low in all varieties, from –19.7 (Yamal) to –27.3 (Tomas), and yield variability is significant, from 30.5 % (Bagu) to 42.7 % (Agrointel). Strong responsiveness to changes in conditions was noted in the variety Salamanca (bi = 1.13), which makes it possible to attribute it to intensive. The varieties Yamal (Si2 = 3.30) and Salamanka (Si2 = 4.36) were the best stability. The varieties Bagu (GAA = 3.3) and Salamanka (GAA = 2.5) were the largest general adaptive ability. The varieties Bagu (sum of ranks 19), Salamanca (sum of ranks 24), Yamal (sum of ranks 31) and Thomas (sum of ranks 33) were recognized as the best by the sum of the ranks of the parameters of yield and adaptability. Scientific novelty. The yield and adaptive potential of admitted to use of pea varieties was revealed based on the results of their testing in 6 environments using a number of methodological approaches. Practical significance. The ranking of varieties according to the parameters of yield and adaptability made it possible to identify the best varieties according to the complex of signs and properties in the conditions of the subtaiga of the Tyumen region.

Investigating the relationship between fluctuations in daily milk yield as resilience indicators and health traits in Holstein cattle

Disease-related milk losses directly impact dairy herds' profitability and the production efficiency of the dairy industry. Therefore, this study aimed to quantify phenotypic variability in milk fluctuation periods related to diseases and to explore milk fluctuation traits as indicators of disease resilience. By combining high-frequency daily milk yield data with disease records of cows that were treated and recovered from the disease, we estimated milk variability trends within a fixed period around the treatment day of each record for 5 diseases: udder health, reproductive disorders, metabolic disorders, digestive disorders, and hoof health. The average milk yield decreased rapidly from 6 to 8 d before the treatment day for all diseases, with the largest milk reduction observed on the treatment day. Additionally, we assessed the significance of milk fluctuation periods highly related to diseases by defining milk fluctuations as a period of at least 10 consecutive days in which milk yield fell below 90% of the expected milk production values at least once. We defined the development and recovery phases of milk fluctuations using 3,847 milk fluctuation periods related to disease incidences, and estimated genetic parameters of milk fluctuation traits, including milk losses, duration of the fluctuation, variation rate in daily milk yield, and standard deviation of milk deviations for each phase and their genetic correlation with several important traits. In general, the disease-related milk fluctuation periods lasted 21.19 ± 10.36 d with a milk loss of 115.54 ± 92.49 kg per lactation. Compared with the development phase, the recovery phase lasted an average of 3.3 d longer, in which cows produced 11.04 kg less milk and exhibited a slower variation rate in daily milk yield of 0.35 kg/d. There were significant differences in milk fluctuation traits depending on the disease, and greater milk losses were observed when multiple diseases occurred simultaneously. All milk fluctuation traits evaluated were heritable with heritability estimates ranging from 0.01 to 0.10, and moderate to high genetic correlations with milk yield (0.34 to 0.64), milk loss throughout the lactation (0.22 to 0.97), and resilience indicator (0.39 to 0.95). These results indicate that cows with lower milk losses and higher resilience tend to have more stable milk fluctuations, which supports the potential for breeding for more disease-resilient cows based on milk fluctuation traits. Overall, this study confirms the high impact of diseases on milk yield variability and provides insightful information about their relationship with relevant traits in Holstein cattle. Furthermore, this study shows the potential of using high frequency automatic monitoring of milk yield to assist on breeding practices and health management in dairy cows.

Wheat Yield Estimation at High Spatial Resolution through the Assimilation of Sentinel-2 Data into a Crop Growth Model

Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m × 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R2) and a root mean square error (RMSE) of 0.80 and 0.65 m2/m2, respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production.

Heterogeneous climate effect on crop yield and associated risks to water security in India

ABSTRACT This study uses the Just–Pope approach to investigate the effects of seasonal weather variables and extremes on the mean yield and yield variability of rice, bajra, chickpea, groundnut and sugarcane in India during the period 1990–2015. Results reveal that changes in rainfall and evapotranspiration across seasons largely affect mean yields for most crops, including bajra, chickpea and groundnut. However, high summer rainfall and low monsoon evapotranspiration extremes reduce groundnut and chickpea yield variability. Considering the importance of water availability to crop yields, this study suggests improving irrigation and water reallocation and management to reduce the severity of seasonal climate effects.

Path coefficient analysis of seed yield in Indian mustard [Brassica juncea (L.) Czern. &amp; Coss.] under drought

The present investigation was carried out to assessthe components ofseed yield under droughtstress using pathcoefficient analysis. Twenty two genotypes ofIndian mustard were grown in a Randomized Complete Block Design with three replications during rabi, 2007-08. Analysis of variance indicated presence of variability in theexperimental genotypesforseed yield and other morpho-physiological characters except main shoot length, harvest index, protein content, SLW at 35 days aftersowing (DAS) and seed: husk ratio. Seed yield/plant was positively and significantly correlated at both phenotypic and genotypic levels with days to maturity, plant height, primary branches/plant, secondary branches/plant, siliquae on main shoot, LAI, CGR and biological yield/plant. Seed yield/plant had negative genotypic correlations of moderate strength with transpiration quotient at 35 and 75 DAS indicating its positive relationship with water use efficiency. TheRGRduring 35-75 DAS followed byRGRbetween 75 and 95 DAS exerted the highest direct positive effects on seed yield. Biological yield/plant, SLW at 75 DAS, TDM at 75 and 95 DAS, SCMR at 35 DAS, siliquae on main shoot, days to maturity and siliqua length also had substantial positive direct effects. The characters under study explained about 96.5% and 100% variability in the seed yield at phenotypic and genotypic level, respectively. Dry matter production, early growth and development of the crop are important under drought due to continuous decline in available soil moisture. Since LAI, LGR, CGR, RGR and NAR are the functions of dry matter production and/or leaf area, therefore, selection on the basis of high biological yield and large LAI at 50% flowering should be quite effective in improving seed yield under drought. An increase in LAI might increase radiation load resulting in to high transpiration. Therefore, genotypes having high LAI with erect leaves and low TQ should be accorded priority in the selection programme.

Impact of El-Nino and La-Nina Episodes on Rainfall Variability and Crop Yield

El Nino and La Nina events have an impact on the Indian monsoon in terms of less rainfall than average and more rainfall in La Nina years. El-Nino events are more likely to see rainfall variability during the monsoon and depressions over the Bay of Bengal (July-August). ENSO is a disruption in ocean surface temperatures and atmospheric circulation over the Pacific Ocean leading to wide spread changes in rainfall/precipitation regimes around the world. El Nino years' effects on crop production in India as a result of lower rainfall during the south-west monsoon. In the kharif season (June to September), crops suffer from moisture and have lower yields in El-Nino years, but the opposite in La-Nina years. The El Nino is associate with the possibility of drought like situation at many occasions and La Nina is the opposite of El-Nino events.

Cassava production in africa: A panel analysis of the drivers and trends

Cassava is Africa's most important tuberous crop. It is an all-year-round cheap and reliable staple food for millions of Africans, making it vital for food security on the continent. However, cassava production in Africa is hindered by a persistent problem of low yield per hectare. This study addresses the dearth of research on the specific influences of area harvested and yield per hectare on cassava production in Africa. This work uses panel data from 37 African countries from 1961 to 2020 and sheds light on three key aspects. Firstly, it investigates the extent and nature of the low yield per hectare problem, offering insights into its underlying causes and implications. Secondly, it examines the interplay between area harvested and yield per hectare, revealing the factors driving the observed trends in cassava yields on the continent. Lastly, this study contributes to the achievement of Sustainable Development Goals, particularly Goal 15: Life on Land and Goal 2: Zero Hunger, by providing valuable information to enhance cassava production sustainability. The findings indicate that approximately 95.6% of the variability in production can be explained by changes in the area harvested, around 1.1% by yield variability, about 27.6% by consumer price index and 1.8% by temperature changes. Notably, the study observes a significant increase in the area harvested by 16.8 million hectares and average yield levels varied between 5.7 and 9.6 tonnes per hectare. The analysis also reveals a disparity in translating gains from disease eradication and introducing high-yield, disease-resistant varieties into smallholder cassava farming. In conclusion, the study highlights the potential for sustainable intensification of cassava production as a viable pathway to enhance absolute and per-hectare yields while promoting farmers’ income and mitigating cassava cultivation-related deforestation. Understanding and addressing the low yield per hectare problem in cassava production are crucial steps toward ensuring food security and achieving sustainable agricultural practices in Africa.

Effect of foliar applied zinc sulphate on phenotypic variability, association and heritability of yield and zinc biofortification related traits of wheat genotypes

Wheat is an important food crop worldwide, providing substantial calories and nourishment. Genetic variability in wheat germplasm is crucial for the development of cultivars with desirable features. This two years study (2020–21 and 2021–22) was conducted to evaluate 13 diverse wheat genotypes factorially combined with foliar-applied zinc sulphate (0, 0.4, 0.6%) arranged in a triplicate randomized complete block design. Boxplot analysis revealed the significant (P < 0.01) phenotypic variation of wheat germplasm for all the studied traits, but maximum variation was observed for yield and Zn biofortification-related traits. Correlation and path analysis revealed a significant (P < 0.01) association among yield and biofortification-related traits. Zinc uptake showed maximum strength of association (r = 0.96, p < 0.01) with grain Zn concentration. The Biplot analysis showed the graphical representation of wheat accessions based on similar characteristics and then assort into distinct groups. Broadsense heritability (Hbs) was calculated to determine the proportion of variation transmitted to future generations. The high value of Hbs for yield and Zn biofortification-related traits indicates that these traits are governed by the additive type of gene action and can be fixed in early segregating generations. In crux, this study validated the genetic variability in existing wheat genotypes for yield and Zn biofortification-related traits and may be helpful to devise an efficient breeding program for wheat Zn biofortification.

A weakly supervised framework for high-resolution crop yield forecasts

Predictor inputs and labels (e.g. yield data) for crop yield forecasting are not always available at the same spatial resolution. Common statistical and machine learning methods require inputs and labels at the same resolution. Therefore, they cannot produce high resolution (HR) yield forecasts in the absence of HR yield data. We propose a weakly supervised (WS) deep learning framework that uses HR inputs and low resolution (LR) labels (crop areas and yields) to produce HR forecasts. The forecasting model was calibrated by aggregating HR forecasts and comparing with LR crop area and yield statistics. The framework was evaluated by disaggregating yields from parent statistical regions to sub-regions for five countries and two crops in Europe. Similarly, corn yields were disaggregated from counties to 10 km grids in the US. The performance of WS models was compared with naive disaggregation (ND) models, which assigned LR forecasts for a region or county to all HR sub-units, and strongly supervised models trained with HR yield labels. In Europe, all models (ND, WS and strongly supervised) were statistically similar, mainly due to the effect of yield trend. In the US, the WS models performed even better than the strongly supervised models. Based on Kendall’s rank correlation coefficient, the WS model forecasts captured significant amounts of HR yield variability. Combining information from WS with Trend model (using LR yield trend) and WS No Trend model (not using yield trend) provided good estimates of yields as well as spatial variability among sub-regions or grids. High resolution crop yield forecasts are useful to policymakers and other stakeholders for local analysis and monitoring. Our weakly supervised framework produces such forecasts even in the absence of high resolution yield data.