Inter-annual Yield Variability Research Articles

Future climate change significantly alters interannual wheat yield variability over half of harvested areas

Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation (CV)) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). About 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies.

ENSO influence on corn and soybean yields as a base of an early warning system for agriculture in Córdoba, Argentina

Reducing uncertainty and productive risk increase the resilience of agricultural systems and food security of a region. While crop productivity strongly depends on weather conditions during the growing season, El Niño Southern Oscillation (ENSO) phenomenon is the main driver of global interannual climate variability and affects crop production in many regions. The ENSO, Pacific Decadal Oscillation (PDO) and Indian Ocean Dipole (IOD) impact on corn and soybean yields between 1973 and 2017 was compared in Córdoba Province, Argentina, looking for an early signal to support agricultural activity. The analysis suggests that not only ENSO signal (SOI and SST El Niño 3.4) but also PDO anomaly (PDOA) link better than IOD with productivity for both crops, as well as the Southern Oscillation Index of August and September (SOIas) is a proper yield predictor. An inverse relationship was determined between SOIas and yields anomaly, more spread territorially for soybeans since the linear functions are significant (p < 0.05) in 9 of the 12 administrative areas and, with a slightly lower significance (p < 0.1), in the remaining too. On the other hand, the low R2 values indicate little capacity to explain the yield interannual variability from the SOIas signal that for corn, has a departmental range between 2 % and 19 % and, for soybeans, slightly higher between 7 % and 24 %. While the mean difference test of yield anomaly between different ENSO phases (El Niño, La Niña and Neutral) supports the more general indifference of both extreme phases with the neutral cases, it reaches a significant character in 8 departments when the cold phase is compared with the warm. The seasonal rainfall regime has also a reduced capacity to explain productive variability. However, this information is useful to realize that both the wettest (El Niño) and the driest years (La Niña) fail to differentiate from those with normal rainfall. This confusion was verified by a combinatory frequency analysis of rainfall and productivity data related to the ENSO phases. Because ENSO allows anticipating the rainfall condition and the productive potential of the main rain-fed crops in Córdoba, Argentina, it constitutes appropriate information to reduce uncertainty and the risk level in agricultural production. Therefore, although the ENSO predictive capacity is limited and more knowledge about the influence of other climatic variability sources like PDO is needed, its incorporation into a climate monitoring protocol must conforms to the basis of an early warning system for a sustainable agriculture in the region.

Night Temperature Determines the Interannual Yield Variation in Hybrid and Inbred Rice Widely Used in Central China Through Different Effects on Reproductive Growth.

Interannual variation in grain yield of rice has been observed at both farm and regional scales, which is related to the climate variability. Previous studies focus on predicting the trend of climate change in the future and its potential effects on rice production using climate models; however, field studies are lacking to examine the climatic causes underlying the interannual yield variability for different rice cultivars. Here a 6-year field experiment from 2012 to 2017 was conducted using one hybrid (Yangliangyou6, YLY6) cultivar and one inbred (Huanghuazhan, HHZ) cultivar to determine the climate factors responsible for the interannual yield variation. A significant variation in grain yield was observed for both the inbred and hybrid cultivars across six planting years, and the coefficient of variation for grain yield was 7.3–10.5%. The night temperature (average daily minimum temperature, Tmin) contributed to the yield variability in both cultivars. However, the two cultivars showed different responses to the change in Tmin. The yield variation in HHZ was mainly explained by the effects of Tmin on grain filling percentage and grain weight, while the change in spikelets m−2 in response to Tmin accounted for the yield variability in YLY6. Further analysis found that spikelets m−2 of YLY6 significantly and negatively correlated with Tmin from transplanting to heading. For HHZ, the grain filling percentage and grain weight were significantly affected by Tmin of the week prior to heading and from heading to maturity, respectively. Overall, there were differences in the response mechanism between hybrid and inbred cultivars to high night temperature. These will facilitate the development of climate-resilient cultivars and appropriate management practices to achieve a stable grain yield.

Machine Learning-Based Modeling of Spatio-Temporally Varying Responses of Rainfed Corn Yield to Climate, Soil, and Management in the U.S. Corn Belt

Better understanding the variabilities in crop yield and production is critical to assessing the vulnerability and resilience of food production systems. Both environmental (climatic and edaphic) conditions and management factors affect the variabilities of crop yield. In this study, we conducted a comprehensive data-driven analysis in the U.S. Corn Belt to understand and model how rainfed corn yield is affected by climate variability and extremes, soil properties (soil available water capacity, soil organic matter), and management practices (planting date and fertilizer applications). Exploratory data analyses revealed that corn yield responds non-linearly to temperature, while the negative vapor pressure deficit (VPD) effect on corn yield is monotonic and more prominent. Higher mean yield and inter-annual yield variability are found associated with high soil available water capacity, while lower inter-annual yield variability is associated with high soil organic matter (SOM). We also identified region-dependent relationships between planting date and yield and a strong correlation between planting date and the April weather condition (temperature and rainfall). Next, we built machine learning models using the random forest and LASSO algorithms, respectively, to predict corn yield with all climatic, soil properties, and management factors. The random forest model achieved a high prediction accuracy for annual yield at county level as early as in July (R2 = 0.781) and outperformed LASSO. The gained insights from this study lead to improved understanding of how corn yield responds to climate variability and projected change in the U.S. Corn Belt and globally.

Assessing the impact of climate change on wheat and sugarcane with the AquaCrop model along the Indus River Basin, Pakistan

Pakistan is among the most vulnerable regions to climate change impacts, in particular the agricultural areas found in the worlds’ largest contiguous irrigation system, the Indus River Basin (IRB). This study assesses the impacts of two climate change scenarios (Representative Concentration Pathways-RCPs 4.5 and 8.5) on soil evaporation and transpiration rates, crop water productivity (CWP) and wheat and sugarcane yields over the 21st century, under two irrigation schedules (less/more frequent irrigation and higher/lower volume) for six locations along the Sindh and Punjab provinces. Maximum and minimum temperatures are projected to increase across the study area over the course of the 21st century. Additionally, precipitation is projected to increase (decrease) along the southernmost (northernmost) areas during the summer rainy season from June to September. To evaluate the crop-water productivity of wheat and sugarcane, we used the AquaCrop model in the six selected locations. For assessing the goodness of model validation and calibration, different statistical indicators are considered for comparing simulated and observed inter-annual yield variability (e.g. NRMSE of 12.4% and 12.1% for wheat and sugarcane, average values of the calibration and validation process). Our results show that wheat yields are likely to remain constant over time across the study areas, whereas sugarcane yields are expected to experience a decline in the Sindh province and an exponential increase in the Punjab province up to 2080, then yields will start to decline. In addition, our results reveal that both crops perform better, in terms of CWP, under low frequent irrigation and higher volumes of water. Overall, the findings of this work also support policymakers and project developers to implement adaptation strategies to cope with changing environmental conditions in a country where pressure on water resources is expected to continue to grow.

Alternative cropping systems for groundwater irrigation sustainability in the North China Plain

Over-exploitation of groundwater for agricultural irrigation has attracted worldwide attention because of the rapid groundwater depletion and environmental consequences induced by the current intensified cropping system. The combination of cropping system modeling with field experimentation is a powerful tool to evaluate the long-term consequences of changing water management and cropping systems under inter-annual climate variability. Here, we conducted two field experiments for early maize (EM, 2012–2018) single cropping system and winter wheat – summer maize (WWSM, 2015–2018) double cropping system in the North China Plain (NCP). Field observations were carried out for crop growth, yield, and soil volumetric moisture content (VMC) calibration with the Agricultural Policy/Environmental eXtender (APEX) model. The calibrated APEX model was used to assess the long-term (35 years, 1985–2019) impacts of EM and WWSM cropping systems under four irrigation scenarios on sustainable water and crop productivity using historical weather data. Results showed that the APEX replicated growth, yield, and VMC all reasonably well with R2 > 0.5, percent bias < 25%, and index of agreement > 0.8. The model realistically simulated VMC under full irrigation (FI), while model simulations under reduced irrigation were not that accurate. Neglectable yield losses of EM were observed by changing EM-FI to rainfed (EM-RF) (7.0 vs. 6.8 Mg ha−1) and lower inter-annual yield variability. Shifting WWSM-FI to EM-RF saved 47% evapotranspiration (ET) and 115% net water use (NWU) regardless of the precipitation category years, but caused 54% reduction in yield. Critical irrigation (CI) significantly reduced ET (7%) and NWU (28%) of WWSM, without yield loss, compared to FI, indicating great potentials of water-saving through optimized irrigation strategy. Minimum irrigation (MI) saved ET by 17% and NWU of WWSM by 62%, but led to 15% EY reduction compared with FI. In conclusion, the WWSM cropping system under CI has the potential to maintain yield with less water consumption, while rainfed EM is the best alternative option for mitigating groundwater over-exploitation with a certain extent risk in crop yield losses.

Global Within-Season Yield Anomaly Prediction for Major Crops Derived Using Seasonal Forecasts of Large-Scale Climate Indices and Regional Temperature and Precipitation

AbstractWeather and climate variability associated with major climate modes is a main driver of interannual yield variability of commodity crops in global cropland areas. A global crop forecasting service that is currently in the test operation phase is based on temperature and precipitation forecasts, while recent literature suggests that crop forecasting services may benefit from the use of climate index forecasts. However, no consistent comparison is available on prediction skill between yield models relying on forecasts from temperature and precipitation and from climate indices. Here, we present a global assessment of 26-yr (1983–2008) within-season yield anomaly hindcasts for maize, rice, wheat, and soybean derived using different types of statistical yield models. One type of model utilizes temperature and precipitation for individual cropping areas (the TP model type) to represent the current service, whereas the other type relies on large-scale climate indices (the CI model). For the TP models, three specifications with different model complexities are compared. The results show that the CI model is characterized by a small reduction in the skillful area from the reanalysis model to the hindcast model and shows the largest skillful areas for rice and soybean. In the TP models, the skill of the simple model is comparable to that of the more complex models. Our findings suggest that the use of climate index forecasts for global crop forecasting services in addition to temperature and precipitation forecasts likely increases the total number of crops and countries where skillful yield anomaly prediction is feasible.

Performance of a process-based model for predicting robusta coffee yield at the regional scale in Vietnam

Reliable and timely prediction of robusta coffee (Coffea canephora Pierre ex A. Froehner) yield is pivotal to the profitability of the coffee industry worldwide. In this study we assess the performance of a simple process-based model for simulating and predicting robusta coffee yield at the regional scale in Vietnam. The model includes the key processes of coffee growth and development and simulates its response to variation in climate and potential water requirements throughout the growing season. The model was built and evaluated for the major Vietnamese robusta coffee-producing provinces Dak Lak, Dak Nong, Gia Lai, Kon Tum, and Lam Dong, using official provincial coffee yield data and climate station data for the 2001–2014 period, and field data collected during a 10-year (2008–2017) survey. Overall, good agreements were found between the observed and predicted coffee yields. Root mean square error (RMSE) and mean absolute percentage error (MAPE) values ranged from 0.24 to 0.33 t ha−1, and 9% to 14%, respectively. Willmott's index of agreement (WI) was greater than or equal to 0.710 in model evaluation steps for three out of five provinces. The relatively low values of WI were found for provinces with relatively low inter-annual yield variability (i.e. Dak Lak and Dak Nong). Moreover, the model was successfully tested using remote sensing satellite and model-based gridded climate data: MAPE values were ≤ 12% and RMSE were ≤ 0.29 t ha−1. Such evaluation is important for long-term coffee productivity studies in these regions where long-term climate stations data are not readily available. The simple process-based model presented in this study could serve as a basis for developing an integrated seasonal climate-robusta coffee yield forecasting system, which would offer substantial benefits to coffee growers and industry through better supply chain management and preparedness for extreme climate events, and increased profitability.

Regional estimation of garlic yield using crop, satellite and climate data in Mexico

Garlic (Allivium sativum L.) has been consumed by humans since the beginning of recorded history. Despite its culinary and economical importance, little efforts are reported in the literature to forecast garlic yield. In this study, we developed the first attempt to predict garlic yield at large scale using crop, satellite (TERRA-MODIS) and climate data (ERA-5). The study area was located in Mexico (2004 – 2018). We compared the predictive capacity of three Machine Learning (ML) methods: generelised linear model (glm), support vector machine radial (svmR) and random forest (rf); as well as different combinations of predictors. The best performance was obtained by the svmR model (R2 = 0.68 and %RMSE = 20.08%) which combined YieldBaseLine, NDVI and climate data under Feature Selection scenario (FS) = 0.90. The rf method performed similarly well (R2 = 0.66), while glm achieved lower performance (R2 = 0.61). To test the predictive capacity of the models in practice, we additionally used a leave-one-year-out method in a time-wise manner. Models gradually improved over the years as more data was incorporated for training. The svmR and rf methods presented an overall performance of R2 ~0.60 (%RMSE ~20.5%), reaching the highest accuracy (R2 = 0.78, %RMSE ~16.6%) in 2014. Both approaches reveal the potentiality of the proposed models to account for the inter-annual and spatial garlic yield variability in Mexico. This procedure can be adjusted and used for other crop types or locations.

Climate-related inter-annual variability and long-term influence on wheat yield across canal-irrigated areas of Punjab, Pakistan

The association of climatic variables and climatic oscillations with wheat yield across 26 canal command areas (CCAs) in Punjab, Pakistan, is studied, for recent 34 years (1982–2015). Climatic variables include precipitation, temperature, dry/wet spell lengths, hot/cold spell lengths, wet/dry spell ratio along with climatic oscillations, and satellite-based vegetation index for the months of January to April (Rabi season). During Rabi season, temperature increase rate was 0.14 °C decade−1, whereas precipitation trends were spatially inhomogeneous and varied between – 2.7 mm decade−1 and + 5.4 mm decade−1. The precipitation, temperature, cold spell length, smoothed NDVI (ND), and standard deviation of ND (SDND) displayed significant correlations (0.31–0.96) with wheat yield at varying time scales. The inter-annual wheat yield variability displayed a gain of 415.7 kg ha−1 under present climatic conditions for all CCAs. Worse to extreme climatic conditions based on percentiles obtained from present climate displayed a reduction of wheat yield by 252.0 to 954.9 kg ha−1, respectively. The good to best climate in comparison to present climatic conditions added up 108.1 to 251.9 kg ha−1, respectively. The 11-year period of 1993–2003 was the most drought prone period with consecutive five years of drought (1998–2002), having decreased precipitation, small wet/dry spell ratio and increased temperature, and long dry/hot spell length during wheat growing season. The wheat yield is predicted based on multiple regression models with sizable skill (having an explained variance of 0.79 and a root mean squared error of 121.3 kg ha−1).

Analysis of Spatiotemporal Variability of Corn Yields Using Empirical Orthogonal Functions

We used empirical orthogonal functions (EOF) to analyze the spatial and temporal patterns of corn (Zea mays L.) yields at three hydrologically-bounded fields with shallow subsurface preferential lateral flow pathways. One field received uniform application of manure, the second field received the uniform applications of the chemical nitrogen fertilizer, and the third field received variable rate applications of the chemical fertilizer. The preferential subsurface flow and storage pathway locations were inferred from the ground penetration radar survey. Six-year monitoring data were analyzed. Statistical distributions of EOFs across fields were approximately symmetrical. Semivariograms of the first EOF differed between fields receiving manure and chemical fertilizer. This EOF accounted for 52 to 56% of the interannual variability of yields, and its values reflected the distance to the subsurface flow and storage pathways. The second and third EOF explained 17 to 20% and 10 to 13% of the interannual variability of yields, respectively. The precision applications of the nitrogen fertilizer minimized corn yield variability associated with subsurface preferential flow patterns. Investigating spatial patterns of yield variability under shallow groundwater flow control can be beneficial for the within-field crop management resource allocation.

Decreasing wheat yield stability on the North China Plain: Relative contributions from climate change in mean and variability

AbstractThere has been increasing interest in understanding climate change impacts on crop yield stability, including interannual yield variability and lower yield extremes, in addition to mean yield. In this study, we evaluated these impacts on wheat yield and investigated the contribution of changes in climate mean and variability, and their interaction, on the North China Plain (NCP). Wheat yield simulation experiments with control groups were conducted using the Crop Environment Resource Synthesis (CERES) model, with multiple general circulation model ensembles under two representative concentration pathways (RCPs), namely 4.5 and 8.5. Climate change was projected to reduce mean yield by 15 and 17%, increase yield interannual variability by 5 and 11%, and reduce 10‐year return period lower yield extremes by 31 and 34% under RCPs 4.5 and 8.5, respectively. When analysed, changes in climate mean proved the main cause for changes in mean yield (62–71%), followed by the interactive changes in climate mean and variability (26–33%). As for the impact on yield variability, the interaction of the changes in climate mean and variability proved the main cause (48–54%), followed by changes in climate mean (33–41%). Surprisingly, climate change in variability contributed the least in both cases. Our results pertaining to the decrease in both availability and stability of wheat yield on the NCP presents a greater challenge for building a resilient food system for local areas than before. They also highlighted the importance of separating the impacts of changes in climate mean and variability on crop yield stability in a holistic framework, with particular attention paid to the tangible and interactive effects.

Contrasted response to climate change of winter and spring grain legumes in southwestern France

Abstract Climate change could undermine grain legumes ability to fix atmospheric nitrogen and their contribution to increase cropping systems sustainability. Pea (Pisum sativum L.) and faba bean (Vicia faba L.) are the two most widely grown grain legumes in Europe, yet the potential impact of climate change on their performances has not been quantified. We calibrated and evaluated the STICS soil-crop model for spring pea, winter pea and winter faba bean using experimental data from southwestern France and explored the effect of contrasting climate change scenarios. After calibration, STICS accurately simulated grain yield and amount of N2 fixed for the experimental growing seasons. Assuming no change in crop management, mean and inter-annual variability of grain yield and fixed N2 were assessed for historical (1995−2015), mid-term (2020−2040) and long-term (2060−2080) periods in one location in southwestern France. We considered projections from three climate models and two Representative [CO2] Pathways (RCP 4.5 and RCP 8.5). The climate models spanned a wide range of changes in temperature (+0.3 to +4.1 °C) and rainfall (−15% to +8%) depending on time horizon and RCP. Simulated grain yield increased over the long term in most scenarios (+1 to +25%), and spring pea tended to benefit less than winter pea and winter faba bean. Nevertheless, for the climate scenario with a decrease in rainfall and the strongest increase in temperature, simulated spring pea grain yield decreased by 28% while winter legumes yields were less affected (−14% for pea and no decrease for faba bean). Simulated changes in the amount of N2 fixed followed the grain yield response. Temperature rise caused a shortening in crop cycle duration. Simulated temperature stress significantly increased for spring and winter pea in most climate change scenarios while winter faba bean was rather unaffected due to greater upper temperature thresholds. N2 fixation of spring pea was reduced by above-optimal temperature during its vegetative growth in spring while N2 fixation of winter legumes was enhanced by the increase in temperature during their vegetative growth in winter. Simulated drought stress only increased in the climate scenario predicting a decrease in rainfall. Overall, [CO2] increase would allow offsetting negative effects of temperature and drought on grain yield and N2 fixation, except for climate scenarios involving a decrease in rainfall and the strong increase in temperature. The contrasted simulated response of winter and spring grain legumes to climate change in southwestern France points to the opportunity to tap grain legume diversity and cultivar choice as an adaptation strategy.

Legacy of soil health improvement with carbon increase following one time amendment of biochar in a paddy soil – A rice farm trial

“4 per 1000 Initiative” launched at COP21 has strongly recommended that increasing soil organic carbon (SOC) is a global imperative. However, the strategies to improve agricultural soil functioning and health by an increase in soil C have been poorly addressed. In a rice farm trail, topsoil samples were collected in 2015 respectively in a field amended with biochar (BA) (equivalent to 10 t ha−1) in 2009 and an adjacent field with no biochar (NB). Soil aggregation, biochemical activities and microbial communities of the samples were analyzed with microscopic, micro-biochemical and molecular assays respectively. Compared to NB field, SOC storage increased by 45%, total and available nitrogen pool enhanced by approximately 30% while the bulk density decreased and soil pH unchanged in BA field. A 25% increase in mean weight diameter of water stable aggregates was observed in the BA compared to NB field. Correspondingly, microbial biomass nitrogen and enzyme activities in BA field was enhanced both by approximately 30% compared to NB field. Furthermore, with community structure altered, a moderate (32%) increase in total bacterial abundance and a significant decrease in microbial abundance of amino acid metabolism and fungal pathotrophs were observed in BA field compared to NB field. This could link to the higher (10%) grain yield with lower yield inter-annual variability in BA field compared to NB field, reported previously. All these changes clearly demonstrated a legacy of paddy soil health improvement over years following one time biochar amendment. Overall, addition of biochar to the clayey nutrient rich paddy soil could sustain an increase in SOC, soil aggregation and soil health functioning, with positive changes in microbial community over years. Thus, carbon increase as per required by the “4 per 1000 Initiative” could be a mechanistic driver to enhance soil fertility and improve soil-(plant) health for ensuring food production in world rice agriculture facing land degradation and climate change impacts, which could be assisted by biochar from crop residue in an approach of circular economy.

Water rights shape crop yield and revenue volatility tradeoffs for adaptation in snow dependent systems

Irrigated agriculture in snow-dependent regions contributes significantly to global food production. This study quantifies the impacts of climate change on irrigated agriculture in the snow-dependent Yakima River Basin (YRB) in the Pacific Northwest United States. Here we show that increasingly severe droughts and temperature driven reductions in growing season significantly reduces expected annual agricultural productivity. The overall reduction in mean annual productivity also dampens interannual yield variability, limiting yield-driven revenue fluctuations. Our findings show that farmers who adapt to climate change by planting improved crop varieties may potentially increase their expected mean annaul productivity in an altered climate, but remain strongly vulnerable to irrigation water shortages that substantially increase interannual yield variability (i.e., increasing revenue volatility). Our results underscore the importance for crop adaptation strategies to simultaneously capture the biophysical effects of warming as well as the institutional controls on water availability.

Increased extreme hourly precipitation over China\u2019s rice paddies from 1961 to 2012

Rice yield have been affected by the increased extreme precipitation events in recent decades. Yet, the spatio-temporal patterns of extreme precipitation by rice type and phenology remain elusive. Here, we investigate the characteristics of four extreme precipitation indices across China’s rice paddy and their potential association with crop yields, by using hourly precipitation data from 1,215 stations and rice phenology observations from 45 sub-regions. The data indicate that hourly extreme precipitation have significantly increased in 1961–2012 for single rice and early rice in China but not for late rice. Rice were mainly exposed to extreme precipitation from transplantation to flowering stages. The frequency and proportion of extreme precipitation were significantly increased by 2.0–4.7% and 2.3–2.9% per decade, respectively, mainly in south China and Yangtze River Basin. The precipitation intensity and maximum hourly precipitation were increased by 0.7–1.1% and 0.9–2.8% per decade, respectively, mainly in central China and southeast coastal area. These extreme precipitation indices played a role as important as accumulated precipitation and mean temperature on the interannual variability of rice yields, regardless of rice types. Our results also highlight the urgencies to uncover the underlying mechanisms of extreme precipitation on rice growth, which in turn strengthens the predictability of crop models.

Improving crop yield forecasts with satellite-based soil moisture estimates: An example for township level canola yield forecasts over the Canadian Prairies

Satellite-derived vegetation indices are widely utilized in yield forecasting models; however, they can be heavily impacted by atmospheric conditions due to their reliance on visible and near-infrared portions of the electromagnetic spectrum. Given the importance of soil moisture (SM) for crop development, the objective of this study was to investigate the use of passive microwave-derived estimates of surface SM obtained by the SM Ocean Salinity Mission (SMOS) satellite for forecasting canola yields across the Canadian Prairies within Agriculture and Agri-Food Canada’s (AAFC) Canadian Crop Yield Forecaster (CCYF) model. Weekly SMOS SM observations were combined with climate variables and normalized difference vegetation index (NDVI) data derived from the Advanced Very High Resolution Radiometer (AVHRR) platform and used as an input for forecasting canola yields at the township-scale across the Canadian Prairies from 2010 to 2016. Top predictors were identified, and regression models were built using a robust least angle regression (RLARS) and leave-one-out cross-validation (LOOCV) scheme. SM was found to provide a better descriptor of canola stress than the more widely utilized NDVI, being selected as a predictor in 74.2 % of developed ecodistrict models over the 7-year period, compared to just 41.2 % for NDVI. The difference between model R2 values (i.e. R2diff) when SMOS SM predictors were included and excluded from the forecast, respectively, revealed varying degrees of model improvements; however, the majority of ecodistricts under study (53.3 %) showed improved model fit (i.e. R2diff > 0) with observed canola yields when SMOS SM indices were included as potential predictors within the CCYF. Overall, greater improvements in the CCYF performance were observed in Manitoba and Saskatchewan where meteorological stations are more sparsely distributed. However, performance for both sets of model inputs was relatively low with R2 values ranging from 0 to 0.74 (mean = 0.13) and from 0 to 0.52 (mean = 0.12) across the study area both when SM was included and excluded from the model, respectively. These findings suggest that while SMOS SM observations may provide a more effective indicator of canola yields, the CCYF’s performance at the township-scale, where interannual yield variability is often quite high, is limited by the short temporal satellite record.

The effects of cropping sequence, fertilization and straw management on the yield stability of winter wheat (1986–2017) in the Broadbalk Wheat Experiment, Rothamsted, UK

AbstractThe development of resilient cropping systems with high yield stability is becoming increasingly important due to future climatic and agronomic challenges. Consequently, it is essential to compare the effects of different agronomic management practices, such as cropping sequences and nutrient supply, on the stability of crop yields. Long-term experiments are a valuable resource for investigating these effects, as they provide enough time to accurately estimate stability parameters. The objective of the current study was to compare the effects of different cropping sequencing (#1: continuous v. rotational), fertilization (#2: mineral v. organic) and straw management techniques (in the case of continuous wheat; #3: removal v. incorporation) on the yield stability of winter wheat; yield risk (the probability of yield falling below a threshold yield level) and inter-annual yield variability were used as stability indicators of the effects. Long-term yield data from the Broadbalk Wheat Experiment (Rothamsted, UK) were analysed using a mixed model. Overall, the results showed that rotational cropping combined with sufficient mineral N fertilizer, with or without organic manure, ensured stable wheat yields while reducing yield risk. In contrast, higher yield risks and inter-annual yield variabilities were found in continuous wheat sections with less mineral N fertilizer or with organic manure only.

Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China

Wheat is a leading cereal grain throughout the world. Timely and reliable wheat yield prediction at a large scale is essential for the agricultural supply chain and global food security, especially in China as an important wheat producing and consuming country. The conventional approach using either climate or satellite data or both to build empirical and crop models has prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, socio-economic (SC) factors may also improve crop yield prediction, but their contributions need in-depth investigation, especially in regions with good irrigation conditions, sufficient fertilization, and pesticide application. Here, we performed the first attempt to predict wheat yield across China from 2001 to 2015 at the county-level by integrating multi-source data, including monthly climate data, satellite data (i.e., Vegetation indices (VIs)), and SC factors. The results show that incorporating all the datasets by using three machine learning methods (Ridge Regression (RR), Random Forest (RF), and Light Gradient Boosting (LightGBM)) can achieve the best performance in yield prediction (R2: 0.68~0.75), with the most individual contributions from climate (~0.53), followed by VIs (~0.45), and SC factors (~0.30). In addition, the combinations of VIs and climate data can capture inter-annual yield variability more effectively than other combinations (e.g., combinations of climate and SC, and combinations of VIs and SC), while combining SC with climate data can better capture spatial yield variability than others. Climate data can provide extra and unique information across the entire growing season, while the peak stage of VIs (Mar.~Apr.) do so. Furthermore, incorporating spatial information and soil proprieties into the benchmark models can improve wheat yield prediction by 0.06 and 0.12, respectively. The optimal wheat prediction can be achieved with approximately a two-month leading time before maturity. Our study develops timely and robust methods for winter wheat yield prediction at a large scale in China, which can be applied to other crops and regions.

Inter-annual variability of Pinus pinea L. cone productivity in a non-native habitat

Pinus pinea produces the most expensive pine nuts worldwide, with a masting habit (variable cone productivity over time) in its native habitat. This study assessed the presence of masting habit in a non-native area, the correlation of annual cone production with climatic variables, and the impact of resource depletion on cone productivity 3 and 4 years after a bumper crop. During 10 years, all cones tree−1 were harvested and counted in three plantations. Cone yield stability was measured via coefficient of variation of cones tree−1. Climatic factors were correlated with stone pine masting. Results indicated that inter-annual variability of cone yield and annual rainfall during 3 years prior to seed maturity, and thermal oscillation 2 years before harvest, were significantly correlated. A correlation was found between cone productivity when reproductive shoot differentiation or when male and female primordia induction occurred, and cone productivity 3 and 4 years later. Individual tree fruit load did not reduce flower induction. The most productive trees will also be the most productive ones in the following years, a finding with practical implications for breeding programs and plant production.