Yield Variability Research Articles

Water stress combining weather condition shapes wheat yield and inter-annual yield variability: Field observations from a six-year study

Irrigation has played a pivotal role in Chinese wheat production and is becoming increasingly crucial in adapting to the changing climate. However, the benefits of water-saving wheat production in the long-term period and its response to climate change have received limited attention. In this study, a 6-year field experiment was conducted to investigate the grain yield and water use with three treatments such as Irrigation three times (I3), Irrigation two times (I2), and disposable pre-sowing irrigation (I0), and their sensitivity to weather conditions. The average yield over six years for the I2 treatment is 8.3 Mg ha−1, similar to I3 treatment while using 10.2 % less irrigation water and improving 8.0 % water use efficiency. In contrast, the grain yield in I0 treatment is 28.4 % lower than I3 treatment while consuming 36.9 % less irrigation water. Furthermore, 90.2 % of the yield decrease in I0 treatment results from the lower ear number. Water stress from jointing to flowering accounts for 58.8 % of ear number decrease. Although interannual yield variation is similar among the three treatments, the source of the variation is very different. Kernel weight explains the yield variation by 92.3 % in I3 treatment and 66.1 % in I2 treatment, while ear number accounts for 60.7 % of the variation in I0 treatment. Minimum temperature for kernel weight in both I3 and I2 treatments and rainfall for ear number in I0 treatment is the most important weather factor, respectively. In summary, this study provides valuable insights into the delicate balance between water conservation and food security while adapting to varying weather conditions.

Natural variability in yield and properties of Balanites aegyptiaca (L.) Delile kernel oil from different locations in Nigeria

Balanites aegyptiaca known as the desert date is an underutilized and neglected wild fruit tree/shrub of the arid and semi-arid zones with high economic potential. This study, therefore, was conducted to investigate the yield, physico-chemical and phytochemical properties, proximate and mineral composition of desert date kernel oil from different locations. Oil was extracted using the soxhlet apparatus, while hexane served as solvent. Standard procedures were used to determine the yield and properties of desert date kernel oil. The data obtained were subjected to the analysis of variance (p< 0.05). Oil yield (%) was found to range from 21.09±1.04-43.95±1.85 and varied significantly among the locations. Acid and saponification values (mg KOH/g) increased significantly from 2.11±0.07 to 1.36±0.07 and 76.35±0.22 to 179.85±0.90, respectively. The refractive index ranged from 1.36±0.15 to 1.48±0.06, while the iodine value (mg KOH/g) varied significantly from 67.07±1.53 to 85.33±2.52. Saponins, tannins, and phenol were discovered to vary significantly among the locations, and range from 3.82±0.30 -5.83±0.45 (mg/100 g), 1.31±0.10 - 1.68±0.03 (mg/100 g) and 1.51±0.05 - 2.09±0.06 (mg/100 g), respectively. The broad variability observed in the properties of desert date kernel oil among locations indicates the feasibility of improving kernel oil yield and properties through selection

Identification of climatic and management factors influencing wheat’s yield variability using AgMERRA dataset and DSSAT model across a temperate region

One of the main challenges of today’s agriculture to ensure food security is developing strategies to deal with potential negative impacts of adaptation to climate variability. This study was conducted to determine climatic and management factors influencing wheat yield variability throughout a temperate region in Northeastern Iran in the period of 1980–2010. The growth stages and yield of wheat crop were simulated via DSSAT model, using AgMERRA gridded weather dataset. Also, the effect of climatic variables on yield was identified using Panel Data Regression (PDA). According to the results, 63% of the changes in irrigated wheat yield are explained by climatic factors (temperature and precipitation) and 37% by management factors. PDA revealed that among the climatic variables, the number of days with temperatures above 30 °C during the growing season, mean temperature, as well as amount and frequency of precipitation have a significant effect on irrigated wheat yield (p ≤ 0.05). The management practices, including provision of inputs such as chemical fertilizers, modified seeds, tillage machinery and equipment, information transfer and the penetration of knowledge in the field, would increase yields by 5 kg on average per year in study region. In general, employing effective management methods, in particular selecting the appropriate planting date that could result in better adaptation of the phenological stages of wheat to climatic conditions, thus improving the wheat yield. The results of this research suggest that use of valid AgMERRA meteorological dataset as input for DSSAT crop model could produce reliable simulations which in turn could be employed by food policy and decision makers, farmers, and managers in a temperate region.

Biochar effects on crop yield variability

Context or ProblemNumerous studies have demonstrated that biochar application can increase crop yield by improving soil properties and health. Yet, these studies, however, neglected how biochar alters yield variability across years – reflecting the yield stability. Objective or Research questionThis study aimed to investigate the effects of biochar application on crop yield variability. MethodsPublished data from 38 experimental sites were collected from the Web-of-Science. Two-thirds of the data were originated from three main staple crops: maize, wheat, and rice. The remaining were from rapeseed, soybean, sweet potato, and peppermint. Biochar effects on crop yield and its variability as well as their driving factors were analyzed by linear mixed models depending on soil conditions, field management practices, and climate types. ResultsBiochar increased the crop yield globally by 14 %, especially in soils with low pH (< 5.5), low nitrogen (N) and phosphorus (P) inputs (≤ 120 kg N ha−1, < 35 kg P ha−1), with high biochar inputs (≥ 20 Mg ha−1), and under crop rotation. Biochar increased crop yields by 12 % in short-term (≤ 5 years) and 21 % in long-term (> 5 years) experiments. Biochar increased yield variability by 42 % in acidic soils (pH < 5.5) and by 24 % with low N inputs (≤ 120 kg N ha−1), mainly because its liming and fertilization effects were short-lasting within the first few years. The yield variability after biochar application decreased with the increase in mean annual temperature, inter-annual variabilities of temperature and precipitation, but with the decrease in mean annual precipitation in the growing season. Yield variability under biochar increased in short-term experiments by 27 %, but there was no change (0 %) in long-term experiments because of the higher yield gains and resistance to fluctuating weather conditions. Therefore, crop yield variability decreased with increasing yield in long-term experiments. ConclusionsBiochar application increased short-term variability of crop yields, but its long-term variability remained unaffected. Implications or SignificanceThis study highlights that biochar can support steadily future crop production in the long run.

Crop yield estimation at different growing stages using a synergy of SAR and optical remote sensing data

Crop yield forecasting is an essential component of crop production assessment, impacting people at the global scale down to the level of individual farms. Until now, yield forecasting has predominantly relied on optical data, particularly the maximum value of vegetation indexes. However, this approach only presents a short forecasting window, and it is essential to obtain yield estimates as early as possible in the growing season and then further improve forecasting even after the vegetation index has reached its peak. So far, optical satellite data at high-temporal resolution (1–3 days) has been actively used for real time crop yield monitoring, whereas fewer operational models make a use of synthetic aperture radar (SAR). In this study, we explore whether SAR data can capture distinct aspects of crop dynamics, providing new insights for yield estimation depending on the crop's phenological stage. We assess the efficiency of dual- (Sentinel-1) and quad-polarimetric (UAVSAR, RADARSAT-2) data to explain inter-field crop yield variability for corn, soybean, and rice over a test area in Arkansas, US (258 fields, 2019). We used optical imagery acquired by Planet/Dove-Classic, Sentinel-2, and Landsat 8, to establish a baseline performance of satellite-based indicators to explain yield variability and assess dual- and quad-polarimetric SAR data for crop yield assessment. In terms of polarimetric indexes, the results showed that in general the results for rice were mostly stable and better than the other crops (R2adj ∼ 0.4 on average). The best results were obtained for the Sentinel-1 VHasc with R2adj = 0.47 and RADARSAT-2 phase difference with R2adj = 0.45. The results for corn performed the least with an R2adj <0.35 for all the indexes. The results for soybeans were more variable and were highly correlated with certain indicators such as RADARSAT-2 HV, RADARSAT-2 Volume, and RADARSAT-2 Pauli HV with R2adj>0.4. We also investigated the day of year (DOY) with the maximum correlation between optical and SAR-derived features and the final yields for corn, soybean, and rice. The maximum correlation for optical features occurs over a short time between DOY 155 (June 4) and 185 (July 5) for corn and rice, and DOY 190 (July 9) and DOY 211 (July 30) for soybean, with these results being consistent across various optical-based sensors. On the contrary, the maximum correlation for SAR-derived features varied significantly and was between DOY 120 (April 30) to DOY 225 (August 13). A study of the time series parameters cross-correlation showed that the optical parameters were highly correlated, but the SAR parameters showed strong temporal decorrelation. We conducted a comparison between C-band and L-band to assess their sensitivity at each stage of growth. In this experiment, we determined that for low vegetation, the C band will be more useful at the beginning of the growth cycle, while the L band provides more information in later stages of growth. Using a random forest regression model combining SAR parameters with common difference vegetation index (DVI), we improved the error by 50% in comparison to the error using the (DVI) for corn, soybean, and rice.

Effects of Surface Area and Water Level Variability on Fisheries Production in Lake Naivasha, Kenya

Lake Naivasha is a freshwater, a Ramsar site and an Important Bird Area. It provides multiple services to the surrounding communities such as Fishing, Tourism and Agriculture. Lake Naivasha has been experiencing a water level variation cycle over the years as a result of it being shallow and experiencing high evaporation. Variation in water levels leads to flooding, and receding which disrupts the Lake ecosystem thus causing direct and indirect effects on fishing. The study was conducted to establish the relationship between Surface area and water level variability and fish yield and revenue in Lake Naivasha. Primary data was collected using a semi-structured questionnaire, and Key informant interviews. Secondary data was collected using Landsat images that provided the temporal and spatial water level variability and documentary analysis between 1989 to 2022. Both descriptive statistics and inferential statistics were used for data analysis. The results show a positive correlation between Surface area Variability and fish yield (r= 0.6261, R2= 0.3920) and fish revenue (r= 0.5219, R2= 0.2724). Surface area variability accounts for 39% of the variation in total fish yield, and 27% of the fish revenue. The study recommends continuous monitoring and surveillance of the lake level, and water quality by the relevant government agencies, as well as watershed management and conservation measures for the sustainability of the Lake’s fishery.

Multi-model ensembles for regional and national wheat yield forecasts in Argentina

Abstract While multi-model ensembles (MMEs) of seasonal climate models (SCMs) have been used for crop yield forecasting, there has not been a systematic attempt to select the most skillful SCMs to optimize the performance of a MME and improve in-season yield forecasts. Here, we propose a statistical model to forecast regional and national wheat yield variability from 1993–2016 over the main wheat production area in Argentina. Monthly mean temperature and precipitation from the four months (August–November) before harvest were used as features. The model was validated for end-of-season estimation in December using reanalysis data (ERA) from the European Centre for Medium-Range Weather Forecasts (ECMWF) as well as for in-season forecasts from June to November using a MME of three SCMs from 10 SCMs analyzed. A benchmark model for end-of-season yield estimation using ERA data achieved a R 2 of 0.33, a root-mean-square error (RMSE) of 9.8% and a receiver operating characteristic (ROC) score of 0.8 on national level. On regional level, the model demonstrated the best estimation accuracy in the northern sub-humid Pampas with a R 2 of 0.5, a RMSE of 12.6% and a ROC score of 0.9. Across all months of initialization, SCMs from the National Centers for Environmental Prediction, the National Center for Atmospheric Research and the Geophysical Fluid Dynamics Laboratory had the highest mean absolute error of forecasted features compared to ERA data. The most skillful in-season wheat yield forecasts were possible with a 3-member-MME, combining data from the SCMs of the ECMWF, the National Aeronautics and Space Administration and the French national meteorological service. This MME forecasted wheat yield on national level at the beginning of November, one month before harvest, with a R 2 of 0.32, a RMSE of 9.9% and a ROC score of 0.7. This approach can be applied to other crops and regions.

Versatile crop yield estimator

Accurate production estimates, months before the harvest, are crucial for all parts of the food supply chain, from farmers to governments. While methods have been developed to use satellite data to monitor crop development and production, they typically rely on official crop statistics or ground-based data, limiting their application to the regions where they were calibrated. To address this issue, a new method called VeRsatile Crop Yield Estimator (VeRCYe) has been developed to estimate wheat yield at the pixel and field levels using satellite data and process-based crop models. The method uses the Leaf Area Index (LAI) as the linking variable between remotely sensed data and APSIM crop model simulations. In this process, the sowing dates of each field were detected (RMSE = 2.6 days) using PlanetScope imagery, with PlanetScope and Sentinel-2 data fused into a daily 3 m LAI dataset, enabling VeRCYe to overcome the traditional trade-off between satellite data that has either high temporal or high spatial resolution. The method was evaluated using 27 wheat fields across the Australian wheatbelt, covering a wide range of pedo-climatic conditions and farm management practices across three growing seasons. VeRCYe accurately estimated field-scale yield (R2 = 0.88, RMSE = 757 kg/ha) and produced 3 m pixel size yield maps (R2 = 0.32, RMSE = 1213 kg/ha). The method can potentially forecast the final yield (R2 = 0.78–0.88) about 2 months before the harvest. Finally, the harvest dates of each field were detected from space (RMSE = 2.7 days), indicating when and where the estimated yield would be available to be traded in the market. VeRCYe can estimate yield without ground calibration, be applied to other crop types, and used with any remotely sensed LAI information. This model provides insights into yield variability from pixel to regional scales, enriching our understanding of agricultural productivity.

Cereal–Legume Intercropping: Which Partners Are Preferred in Northwestern Europe?

To increase Europe’s self-sufficiency for protein sources, boosting plant protein production is a prerequisite. Yield variability is one of the main problems regarding the cultivation of protein crops. In this light, cereal–legume intercropping can offer a solution, as well-balanced intercropping systems are less prone to yield variations. Therefore, in this study the effects of (i) species/genotype combination, (ii) intercropping sowing densities and (iii) fertilizer regime were evaluated under Belgian (Northwestern European) conditions over three years (i.e., the 2020–2021, 2021–2022 and 2022–2023 seasons). Regarding the species combinations, winter barley x winter pea, winter wheat x winter faba bean and winter triticale x winter faba bean, it was observed that the best-performing combination varied from year to year depending on the prevailing weather conditions. A reduced sowing density (i.e., 130 seeds/m2 for the cereal partner and 20 seeds/m2 in the case of faba bean or 40 seeds/m2 in the case of pea) was sufficient to achieve competitive yields under the prevailing conditions. Inoculation with commercial Rhizobium strains did not result in an increased yield. Fertilization with one or two nitrogen fractions significantly increased the total yield thanks to a yield increase in the cereal partner; however, as a consequence, the proportion of legumes in the mixture decreased. In conclusion, it can be stated that with the investigated cereal–legume combinations, a competitive yield and qualitative protein yield can be achieved with a reduced fertilizer input.

Spatially-explicit land use change emissions and carbon payback times of biofuels under the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA)

The Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) requires airlines to offset their greenhouse gas (GHG) emissions above 2019 levels by either buying carbon offsets or using Sustainable Aviation Fuels (SAFs). These are drop-in jet fuels made from biomass or other renewable resources that reduce GHG emissions by at least 10 % compared to kerosene and meet certain sustainability criteria. This study assesses the direct land use change (DLUC) emissions of SAF, i.e., GHG emissions from on-site land conversion from previous uses (excluding primary forests, peatlands, wetlands, and protected and biodiversity-rich areas) into alternative feedstocks, considering spatial variability in global yields and land carbon stocks. The results provide DLUC values and carbon payback times at 0.5-degree resolution for six SAF pathways, with and without irrigation and a medium-input intensity, according to CORSIA sustainability criteria. When excluding CORSIA non-compliant areas, soybean SAF shows the highest mean DLUC factor (31.9 ± 20.7 gCO2/MJ), followed by reed canary grass and maize. Jatropha SAF shows the lowest mean DLUC factor (3.6 ± 31.4 gCO2/MJ), followed by miscanthus and switchgrass. The latter feedstocks show potential for reducing GHG emissions over large areas but with relatively greater variability. Country-average DLUC values are higher than accepted ILUC ones for all pathways except for maize. To ensure the GHG benefits of CORSIA, feedstocks must be produced in areas where not only carbon stocks are relatively low but also where attainable yields are sufficiently high. The results help identify locations where the combination of these two factors may be favourable for low-DLUC SAF production. Irrigated miscanthus offers the highest SAF production potential (2.75 EJ globally) if grown on CORSIA-compliant cropland and grassland areas, accounting for ∼1/5 of the total kerosene used in 2019. Quantifying other environmental impacts of SAFs is desirable to understand sustainability trade-offs and financial constraints that may further limit production potentials.

Enhancing climate resilience in northern Ghana: A stochastic dominance analysis of risk-efficient climate-smart technologies for smallholder farmers

Northern Ghana is a semi-arid region characterised by a unimodal rainfall pattern, and hot and dry weather conditions. Heavy reliance on rain-fed agriculture and the lack of resources for irrigation, makes smallholder farmers in the region increasingly vulnerable to climate-related crop failures. In recent years, climate-smart technologies (CSTs) such as changing planting dates (PD), compartmental bunding (CB), mulching (M), and transplanting (TP) have been recommended to minimise yield losses. However, there is limited information on the most risk-efficient CSTs for crops cultivated in the region. This study used a stochastic dominance approach to identify the most risk-efficient CSTs for maize, rice, and sorghum. The stochastic modelling process employed the Aqua-crop model to simulate climate-related yield variability using Ghana climate data, and gross margin variability with crop budgets from literature sources. From the study's findings, changing planting date from April to May was the most risk-efficient choice for maize and sorghum under farmers' and recommended practices. In contrast, transplanting was the most risk-efficient technology for rice farming in the study area. The study also highlights the importance of considering the risk-averse nature of smallholder farmers when selecting CSTs. By identifying the most risk-efficient CSTs, the study can help improve the resilience of smallholder farmers. These findings have important implications for the development and adoption of CSTs in northern Ghana.

Genetic variability, association and diversity analysis of yield and its component traits in rice (Oryza sativa) germplasm

An experiment was conducted during winter (rabi) season of 2022 at ICAR-Indian Institute of Rice Research, Hyderabad, Telangana to evaluate extent of genetic variability, heritability, diversity and the relationship between yield and its component traits in a collection of 60 rice germplasm accessions. Results showed that germplasm accessions exhibited ample genetic variability and diversity for yield traits. Based on per se performance, inter crosses between the genotypes IC458483 with high grain yield, IC210775 with more panicle length, IC75919 with high 100-grain weight, IC75980 with more productive tillers and IC75965 with high protein content could produce positive transgressive segregants for improving respective traits. Plant height, total and effective tillers, grain yield and 100-grain weight traits which exhibited the highest heritability and genetic advance could be vital for selection in hybridization programmes. Crossing among genotypes of diverse clusters specifically VI, VII and VIII is suggested as this could create a broad variation useful for selecting genotypes with both high yield and long panicles.

Soil Moisture Information Improves Drought Risk Protection Provided by the USDA Livestock Forage Disaster Program

Abstract The USDA Livestock Forage Disaster Program (LFP) offers financial assistance to farmers and ranchers with grazed forage losses caused by fire or drought. Payments for drought losses are based on the U.S. Drought Monitor (USDM), which is designed to integrate meteorological, agricultural, hydrological, ecological, and socioeconomic droughts. Because soil moisture deficit is a more specific measure of agricultural drought, we hypothesized that basing LFP payments on soil moisture observations could better reduce producers’ risk. Therefore, our objectives were to 1) quantify relationships of forage yield with USDM-based LFP payment multipliers and with in situ soil moisture, 2) develop an alternative LFP payment multiplier structure based on in situ soil moisture, and 3) quantify risk reduction using the current and alternative payment structures. We focused on Oklahoma, United States, which has led the nation in LFP payments received and has &gt;25 years of in situ soil moisture observations statewide. Using non-alfalfa hay yield as a surrogate for forage production, we found that LFP payment multiplier values and soil moisture anomaly were each related to yield, and soil moisture anomaly explained 54% of yield variability. However, the USDM-based LFP payment structure sometimes resulted in payments for above-average yield, and higher payments did not always correspond with greater yield losses. We developed an alternative soil-moisture-based payment structure that reduced financial risk by &gt;20% compared with the current USDM-based structure. Our study identifies an improved LFP payment structure for Oklahoma that can be evaluated and refined in other states or nationwide using other soil moisture data sources.

STATISTICAL OPTIMIZATION OF ANTIOXIDANT AND EXTRACTION YIELD FROM CENTELLA ASIATICA LEAVES

This study focuses on the statistical optimization of antioxidant and extraction yield from Centella asiatica leaves using Central Composite Designs (CCD) and response surface methodology. The primary objective was to identify influential variables affecting the extraction process, such as extraction time and temperature, using ultrasonic-assisted extraction. The optimized extraction conditions yielded an antioxidant activity of 52.4420%, Total Phenolic Content (TPC) of 0.5000 mg/g, and Total Flavonoid Content (TFC) of 15.3125 mg/g. Statistical analysis revealed significant model terms with an R² value of 0.930, indicating that 93% of the variability in antioxidant yield could be explained by the model. The 'Adeq Precision' ratio of 15.479 confirmed an adequate signal, and the Model F-value of 15.16 (p = 0.0043) highlighted the significance of the model. The findings demonstrate a viable and efficient technique for maximizing the extraction yield of bioactive components from C. asiatica, providing a robust framework for enhancing the efficiency of extracting valuable antioxidants from medicinal plants, which is crucial for natural product research and herbal medicine.

Intraspecific Chemical Variability and Antioxidant Capacity of Siparuna guianensis Aubl. Essential Oil from Brazil

Siparuna guianensis Aubl. is an essential-oil-producing plant with diverse ethnopharmacological uses and bioactive potential. This study aims to evaluate the intraspecific variation in the yield, chemical composition, and antioxidant capacity of S. guianensis essential oil (EO). The specimens (SG-1 to SG-6) were collected in June, five in the district of Outeiro and one in the Salvaterra municipality (Brazil). EOs were obtained by hydrodistillation. The chemical compositions were analyzed by gas chromatography coupled to a mass spectrometer (GC-MS). The DPPH radical scavenging tests and inhibition of β-carotene oxidation by linoleic acid were carried out to evaluate the antioxidant capacity of EOs. Principal components analyses were performed to verify the interrelationships between the studied specimens’ oil yields, chemical composition, and antioxidant capacity. Regarding chemical constituents, all studied samples showed the occurrence of spathulenol with an average concentration of 25.6 ± 15.6%. The samples that presented the highest amounts of this constituent were SG-5 (43.3%) and SG-1 (41.8%); the spathulenol amounts in other samples were 33.2% (SG-4); 13.8% (SG-2); 11.5% (SG-6) and 9.8% (SG-3). Moreover, there was no significant variability in yield and antioxidant capacity using DPPH and β-carotene/linoleic acid; both tests found insignificant values. This species presents a notable intraspecific chemical variability. Despite notable antitumor activities, the plant presents intraspecific chemical variability in composition, which suggests new studies to evaluate the impacts on bioactive compounds.

Machine learning based on functional principal component analysis to quantify the effects of the main drivers of wheat yields

Assessing the response of crop yield to year-to-year climate variability at the field scale is often done using process-based models and regression techniques. Although powerful, these tools rely on strong assumptions and can lead to substantial prediction errors. In this study, we investigate the use of a flexible machine learning algorithm combining Functional Principal Component Analysis and Random Forest, to relate field scale wheat yield to local daily climate variables. Instead of computing seasonal, monthly or any other arbitrary time-frame climate averages, climate time series are decomposed by Functional Principal Component Analysis into a few data-driven basis functions, called Principal Curves, in order to summarize the dynamic of key climate variables by a limited number of interpretable components. Scores associated to these components are then used as inputs of a Random Forest algorithm for yield prediction and for analysing important factors responsible for yield variability. To evaluate our approach, we use a French national database including wheat yield data as well as climate and management practice data for 298 farm fields from 2011 to 2016 in four main producing regions. Depending on the regions, our approach can explain from 62 % to 81 % of the yield variability when both agronomic and climate variables are included, down to 56–81 % when ignoring agronomic variables and 51–74 % when ignoring climate variables. Based on a year-by-year cross-validation, RMSE ranges from 0.5 t ha−1 to 2.1 t ha−1 in non-extreme years (2012–2015). However, prediction error can reach 3.6 t ha−1 in case of exceptional weather conditions, such as those experienced in 2016 in Northern France. We find that this new approach performs in most cases better than the same machine learning algorithm using the usual time averages of climate variables, without the need to choose an arbitrary time-frame. We then show how important patterns in weather time series can be identified and how their effects on yield can be interpreted using the proposed modelling framework.

Assessing Maize Yield Spatiotemporal Variability Using Unmanned Aerial Vehicles and Machine Learning

Optimizing the prediction of maize (Zea mays L.) yields in smallholder farming systems enhances crop management and thus contributes to reducing hunger and achieving one of the Sustainable Development Goals (SDG 2—zero hunger). This research investigated the capability of unmanned aerial vehicle (UAV)-derived data and machine learning algorithms to estimate maize yield and evaluate its spatiotemporal variability through the phenological cycle of the crop in Bronkhorstspruit, South Africa, where UAV data collection took over four dates (pre-flowering, flowering, grain filling, and maturity). The five spectral bands (red, green, blue, near-infrared, and red-edge) of the UAV data, vegetation indices, and grey-level co-occurrence matrix textural features were computed from the bands. Feature selection relied on the correlation between these features and the measured maize yield to estimate maize yield at each growth period. Crop yield prediction was then conducted using our machine learning (ML) regression models, including Random Forest, Gradient Boosting (GradBoost), Categorical Boosting, and Extreme Gradient Boosting. The GradBoost regression showed the best overall model accuracy with R2 ranging from 0.05 to 0.67 and root mean square error from 1.93 to 2.9 t/ha. The yield variability across the growing season indicated that overall higher yield values were predicted in the grain-filling and mature growth stages for both maize fields. An analysis of variance using Welch’s test indicated statistically significant differences in maize yields from the pre-flowering to mature growing stages of the crop (p-value &lt; 0.01). These findings show the utility of UAV data and advanced modelling in detecting yield variations across space and time within smallholder farming environments. Assessing the spatiotemporal variability of maize yields in such environments accurately and timely improves decision-making, essential for ensuring sustainable crop production.

Drivers of cocoa yield and growth in young monoculture and agroforestry systems

CONTEXTThe expansion of cocoa farming has been linked to deforestation and biodiversity loss in West Africa. Agroforestry systems could potentially increase the sustainability of cocoa production. However, despite the long history of cocoa cultivation in agroforestry systems, the exact mechanism or the combination of factors that drive cocoa growth and yield in these agroecosystems, especially at the cocoa establishment stage, is unclear. OBJECTIVEThe present study aims to analyse how resource availability, stand characteristics and cocoa tree morphology interact to determine the performance of cocoa during the establishment phase among different cocoa cultivation systems. METHODSWe studied seven different agroforestry systems and one monoculture system in 53 plots located in a 12-ha cocoa agroforestry trial recently established in Côte d'Ivoire. We characterized each system with 16 variables that described five system components: 1) cocoa yield (number of pods, pods weight, number of productive trees) and cocoa growth (basal diameter), 2) cocoa tree morphology (crown depth, diameter, area, and volume), 3) stand characteristics (number and size of shade trees), 4) light (leaf area index) and 5) water (soil volumetric water content). We used a Structural Equation Modelling (SEM) approach to understand and quantify interactions between the five components. RESULTS AND CONCLUSIONSThe models were able to largely explain cocoa early yield and juvenile growth variability (respective determination coefficients: 0.45 and 0.92). We observed improved cocoa establishment in certain designs of agroforestry systems compared with monoculture. However, SEM revealed that stand characteristics (i.e. increase in the number and height of shade trees) had a negative effect on cocoa yield and growth via changes in cocoa tree morphology, having path coefficients of −0.48 and − 0.72, respectively. Conversely, we found a positive relationship between the cocoa tree morphology and cocoa early yield, for which the path coefficient was 0.74, the strongest relation. Cocoa tree morphology was more important than stand characteristics, leaf area index, and soil volumetric water content in controlling cocoa yield and growth. Taken together, our results suggest a competition for space between shade trees and cocoa plants. Finding the optimal design of agroforestry systems can help enhance cocoa establishment in the first years, improving the long-term performance of the system. SIGNIFICANCEThis finding suggests that the competition effect depends on spatial planting pattern and species composition, offering an opportunity to meet the goals of a sustainable intensification of cocoa plantations through the design and management of optimal agroforestry systems.

RELATIONSHIP BETWEEN RAINFALL VARIABILITY AND MAIZE YIELD IN APA LOCAL GOVERNMENT AREA OF BENUE STATE, NIGERIA

This study investigated the relationship between rainfall variability and maize yield in Apa Local Government Area of Benue State. The specific objectives were to analyse the trend in rainfall distribution and its impact on maize yield and to provide recommendations to improve maize productivity in the study area. Rainfall data was sourced from the archives of the Nigerian Meteorological station, Oshodi, Lagos; the maize yield data was collected from the archives of Benue State Agricultural and Rural Development Agency (BNARDA). The rainfall and maize yield data for 33 years (1988-2021) were used to characterise the maize yield response to rainfall pattern (increase or decrease). Trend-line equation was used to show the trend of rainfall, while Pearson's Correlation Coefficient (r) showed the degree of relationship between rainfall and maize yield. The results of the trend-line regressions showed an increase in total annual rainfall (y = 7.1873x + 1106.4) and an increase in maize yield (y = 0.0319x + 2.3239). The correlational trend chart showed a positive relationship between total annual rainfall and maize yield (r = 0.19), indicating that rainfall positively influenced yield. The study recommended that early warning weather information and climate-resilient varieties are needed to improve maize productivity. Extension services should also be provided to enhance farmers' knowledge on rainfall variability and its effects on maize yield.

Nitrogen application increased yield sensitivity of indica hybrid rice to climate resource

Rice yield variability can be attributed to the range of sowing dates across extensive cropping areas and varying nitrogen application rates. Nonetheless, the precise quantitative relationship between sowing dates, nitrogen application rates, and rice yield development remains elusive. In this study, we evaluated differences in dry matter and yield components of three indica hybrid varieties for five sowing dates at four ecological sites in the middle and lower reaches of the Yangtze River, China in 2019 and 2020, with three nitrogen application rates. Results indicated that yield initially increased and then decreased with delayed sowing dates. Photosynthetically active radiation accumulation after anthesis (PARAA) was identified as the key positive climate resource affecting rice yield, decreasing by an average of 19.7 MJ m−2 for every 15-day delay in sowing. Nitrogen agronomic efficiency decreased linearly with the delay in sowing dates. Early sowing of rice with high PARAA and effective accumulated temperature after anthesis (EATAA) showed yield increases due to nitrogen application, whereas late-sown rice with insufficient photothermal resources did not exhibit marked yield improvement and may experience yield reduction. Ensuring adequate dry matter accumulation after anthesis and high seed setting rate was the main way to increase yield, influenced by sowing dates and nitrogen application rates. Intriguingly, our study revealed that nitrogen application markedly enhanced yield sensitivity to PARAA and EATAA. This finding underscores the pivotal role of nitrogen in modulating crop responsiveness to climate resources, and contributes to a deeper understanding of agronomic practices for optimizing yield under varying climatic conditions.