Coincidence of variation in potato yield and climate in northern China

Balancing potato yield, soil nutrient supply, and nitrous oxide emissions: An analysis of nitrogen application trade-offs

Potato has been promoted as a national key staple food to alleviate pressure on food security in China. Appropriate nitrogen (N) application rate is prerequisite and is crucial for increasing yield, improving fertilizer efficiency, and reducing N losses. In the present study, we determined the optimum N application rates by analyzing field trial data from the main potato producing areas of China between 2004 and 2020. We considered the equilibrium relationships between potato yield, N uptake, partial N balance (PNB), and N2O emission under different soil indigenous N supply (INS) scenarios. The results showed that N rate, INS, and their interactions all significantly affect potato yield and nutrient uptake increment. On average, N application increased potato yield and N uptake by 29.5 % and 56.7 %, respectively. The relationship between N rate and yield increment was linear-plateau, while the relationship between N rate and N uptake increment was linear-linear. Soil INS accounted for 63.5 % of total potato N requirement. Potato yield increment and nutrient uptake increment were exponentially negatively correlated with INS and had a significant parabolic-nonlinear relationship with the interaction of N fertilizer application rate and INS. PNB was negatively correlated with fertilizer N supply intensity as a power function. Based on our analysis, a N application rate of 166 kg N ha-1 was found to be sufficient when the target yield was <34 t ha-1. However, when the target yield reached 40, 50 and 60 t ha-1, the recommended N application rate increased to 182, 211, and 254 kg N ha-1, respectively, while ensuring N2O emissions low with an emission factor of 0.2 %. Our findings will help guide potato farming toward cleaner production without compromising environmental benefit.

Measuring the impact of climate change on potato production in Bangladesh using Bayesian Hierarchical Spatial-temporal modeling

BACKGROUND

Potato is a staple food and a main crop of Bangladesh. Climate plays an important role in different crop production all over the world. Potato production is influenced by climate change, which is occurring at a rapid pace according to time and space.

OBJECTIVE

The main objective of this research is to observe the variation in potato production based on the discrepancy of the variability in the spatial and temporal domains. The research is based on secondary data on potato production from different parts of Bangladesh and five major climate variables for the last 17 years ending with 2020.

METHODS

Bayesian Spatial-temporal modelling for linear, analysis of variance (ANOVA), and auto-Regressive models were used to find the best-fitted model compared with the independent Error Bayesian model. The Watanabe-Akaike information criterion (WAIC) and Deviance Information Criterion (DIC) were used as the model choice criteria and the Markov Chain Monte Carlo (MCMC) method was implemented to generate information about the prior and posterior realizations.

RESULTS

Findings revealed that the ANOVA model under the Spatial-temporal framework was the best model for all model choice and validation criteria. Results depict that there is a significant impact of spatial and temporal variation on potato yield rate. Besides, the windspeed does not show any influence on potato production, however, temperature, humidity, rainfall, and sunshine are important components of potato yield rate in Bangladesh.

CONCLUSION

It is evident that there is a potential impact of climate change on potato production in Bangladesh. Therefore, the authors believed that the findings will be helpful to the policymakers or farmers in developing potato varieties that are resilient to climate change to ensure the United Nations Sustainable Development Goal of zero hunger.

Assessment of Seasonal Drought Impact on Potato in the Northern Single Cropping Area of China

Drought is one of the key limiting factors for potato yield in the northern single cropping area (NSCA) in China. To analyze the impact of drought on potato yield in the NSCA, this study first analyzed the variation of dry/wet conditions in the plantable areas on a seasonal scale using the standardized precipitation evapotranspiration index (SPEI). Secondly, the changes in yield structure in the last 36 years were systematically analyzed and divided the total yield change into planting area contribution and climate yield contribution. Finally, a regression model of the seasonal drought index and contributing factors of total yield change in different administrative regions was constructed. The results showed that the main factors affecting the total potato yield of the NSCA began to change from yield to planting area in the 1990s, while the barycenter of the output structure and population moved to the southwest, with grassland being the main source; dry/wet conditions (year i) had varying degrees of effect on contributing factors (year i, year i + 1) of total yield change in different administrative regions that were not limited to the growing season; the non-overlap of high-yield area, high-adaptability area and planting area was the urgent problem to be solved for the NSCA. The results of this study can provide a scientific basis for NSCA crop management and communication with farmers, providing new ideas for sustainable production in other agricultural regions in the world.

Differences in Reference Evapotranspiration Variation and Climate-Driven Patterns in Different Altitudes of the Qinghai–Tibet Plateau (1961–2017)

Reference evapotranspiration (ET0) in the hydrological cycle is one of the processes that is significantly affected by climate change. The Qinghai–Tibet Plateau (QTP) is universally recognized as a region that is sensitive to climate change. In this study, an area elevation curve is used to divide the study area into three elevation zones: low (below 2800 m), medium (2800–3800 m) and high (3800–5000 m). The cumulative anomaly curve, Mann–Kendall test, moving t-test and Yamamoto test results show that a descending mutation occurred in the 1980s, and an ascending mutation occurred in 2005. Moreover, a delay effect on the descending mutation in addition to an enhancement effect on the ascending mutation of the annual ET0 were coincident with the increasing altitude below 5000 m. The annual ET0 series for the QTP and different elevation zones showed an increasing trend from 1961 to 2017 and increased more significantly with the increase in elevation. Path analysis showed that the climate-driven patterns in different elevation zones are quite different. However, after the ascending mutations occurred in 2005, the maximum air temperature (Tmax) became the common dominant driving factor for the whole region and the three elevation zones.

Staple food and health: a comparative study of physiology and gut microbiota of mice fed with potato and traditional staple foods (corn, wheat and rice)

The effects of potato and traditional staple foods (corn, wheat and rice) on physiology and gut microbiota were investigated by feeding ICR mice for 12 months. Compared with traditional staple foods, potato significantly improved the food and water intake and survival rate, and inhibited the swelling of viscera of mice, accompanied by a decreased white blood cell count and urine bilirubin content. Furthermore, potato significantly increased the relative abundance of Bacteroides and Faecalibacterium, which are short-chain fatty acid producing bacteria and play very important roles in the maintenance of human health. Meanwhile, potato significantly decreased the relative abundance of spoilage bacteria Pseudomonas and Thiobacillus. Analysis of putative metagenomes indicated that the potato diet upregulated the gene abundance of glycan biosynthesis and metabolism, digestive system and immune system. These findings indicated that potato has the potential to be an excellent substitute for traditional staple foods owing to its good physiological function and favorable gut microbiota modulation.

Estimation of Potato Yield Using Satellite Data at a Municipal Level: A Machine Learning Approach

Crop growth modeling and yield forecasting are essential to improve food security policies worldwide. To estimate potato (Solanum tubersum L.) yield over Mexico at a municipal level, we used meteorological data provided by the ERA5 (ECMWF Re-Analysis) dataset developed by the Copernicus Climate Change Service, satellite imagery from the TERRA platform, and field information. Five different machine learning algorithms were used to build the models: random forest (rf), support vector machine linear (svmL), support vector machine polynomial (svmP), support vector machine radial (svmR), and general linear model (glm). The optimized models were tested using independent data (2017 and 2018) not used in the training and optimization phase (2004–2016). In terms of percent root mean squared error (%RMSE), the best results were obtained by the rf algorithm in the winter cycle using variables from the first three months of the cycle (R2 = 0.757 and %RMSE = 18.9). For the summer cycle, the best performing model was the svmP which used the first five months of the cycle as variables (R2 = 0.858 and %RMSE = 14.9). Our results indicated that adding predictor variables of the last two months before the harvest did not significantly improved model performances. These results demonstrate that our models can predict potato yield by analyzing the yield of the previous year, the general conditions of NDVI, meteorology, and information related to the irrigation system at a municipal level.

Effects of soil- and climate data aggregation on simulated potato yield and irrigation water requirement

Input data aggregation affects crop model estimates at the regional level. Previous studies have focused on the impact of aggregating climate data used to compute crop yields. However, little is known about the combined data aggregation effect of climate (DAE_c) and soil (DAE_s) on irrigation water requirement (IWR) in cool-temperate and spatially heterogeneous environments. The aims of this study were to quantify DAE_c and DAE_s of model input data and their combined impacts for simulated irrigated and rainfed yield and IWR. The Agricultural Production Systems sIMulator Next Generation model was applied for the period 1998-2017 across areas suitable for potato (Solanum tuberosum L.) in Tasmania, Australia, using data at 5, 15, 25 and 40 km resolution. Spatial variances of inputs and outputs were evaluated by the relative absolute difference (rAD¯) between the aggregated grids and the 5 km grids. Climate data aggregation resulted in a rAD¯ of 0.7-12.1%, with high values especially for areas with pronounced differences in elevation. The rAD¯ of soil data was higher (5.6-26.3%) than rAD¯ of climate data and was mainly affected by aggregation of organic carbon and maximum plant available water capacity (i.e. the difference between field capacity and wilting point in the effective root zone). For yield estimates, the difference among resolutions (5 km vs. 40 km) was more pronounced for rainfed (rAD¯ = 14.5%) than irrigated conditions (rAD¯ = 3.0%). The rAD¯ of IWR was 15.7% when using input data at 40 km resolution. Therefore, reliable simulations of rainfed yield require a higher spatial resolution than simulation of irrigated yields. This needs to be considered when conducting regional modelling studies across Tasmania. This study also highlights the need to separately quantify the impact of input data aggregation on model outputs to inform about data aggregation errors and identify those variables that explain these errors.

Observation of irrigation-induced climate change in the Midwest United States

Irrigated agriculture alters near-surface temperature and humidity, which may mask global climate change at the regional scale. However, observational studies of irrigation-induced climate change are lacking in temperate, humid regions throughout North America and Europe. Despite unknown climate impacts, irrigated agriculture is expanding in the Midwest United States, where unconfined aquifers provide groundwater to support crop production on coarse soils. This is the first study in the Midwest United States to observe and quantify differences in regional climate associated with irrigated agricultural conversion from forests and rainfed agriculture. To this end, we established a 60 km transect consisting of 28 stations across varying land uses and monitored surface air temperature and relative humidity for 31 months in the Wisconsin Central Sands region. We used a novel approach to quantify irrigated land use in both space and time with a database containing monthly groundwater withdrawal estimates by parcel for the state of Wisconsin. Irrigated agriculture decreased maximum temperatures and increased minimum temperatures, thus shrinking the diurnal temperature range (DTR) by an average of 3°C. Irrigated agriculture also decreased the vapor pressure deficit (VPD) by an average of 0.10 kPa. Irrigated agriculture significantly decreased evaporative demand for 25% and 66% of study days compared to rainfed agriculture and forest, respectively. Differences in VPD across the land-use gradient were highest (0.21 kPa) during the peak of the growing season, while differences in DTR were comparable year-round. Interannual variability in temperature had greater impacts on differences in DTR and VPD across the land-use gradient than interannual variability in precipitation. These regional climate changes must be considered together with increased greenhouse gas emissions, changes to groundwater quality, and surface water degradation when evaluating the costs and benefits of groundwater-sourced irrigation expansion in the Midwest United States and similar regions around the world.

Variations in climatic suitability and planting regionalization for potato in northern China under climate change

Investigating the variations in crop climatic suitability and planting regionalization can provide scientific evidence for ensuring food security under climate change. In this study, variations in climatic suitability and planting regionalization for the potato in northern China were investigated based on daily data from 1965 to 2014 collected at 321 agro-meteorological observation stations located throughout the region. Northern China was divided into three areas, including Northwest China, North China and Northeast China. The agricultural climatic suitability theory and the fuzzy mathematics method were applied. The potato growth seasons were divided into threestages:from sowing to emergence, from emergence to flowering and from flowering to maturity. The comprehensive climatic suitabilityindex (C), which varied from 0 to 1, was established to evaluate the effects of climate change on potato planting. The results showed that, from 1965 to 2014, the C value in the study area increased 0.002 every ten years over the past 50 years with an average of 0.706, benefitting potato growth in the vast area of northern China. Nonetheless, precipitation was found to be the main climatic factor restricting potato growth in northern China. For spatial distribution, the C value showed a gradually declining trend from east to west, decreasing westward and southward over the past 50 years. For the growth season, the C value varied during different potato growth stages over the past 50 years. The C value increased during the sowing-emergence stage and decreased during the emergence-flowering stage and the flowering-maturity stage. The decreased C during the later growth stages would directly affect the quality and yield of the potato, mainly because the flowering-maturity stage was associated with potato tuber enlargement and starch accumulation. Variations in potato planting regionalization in northern China over the past 50 years were evident. Climate change was more beneficial to potato cultivation in northeast China where the highly suitable areas had clearly expanded. However, potato cultivation was most negatively affected in northwest China where the middle suitable areas had receded. Our findings have important implications for improving climate change impact studies and agricultural production to cope with ongoing climate change.

Assessing the combined effects of climatic factors on spring wheat phenophase and grain yield in Inner Mongolia, China

Understanding the regional relationships between climate change and crop production will benefit strategic decisions for future agricultural adaptation in China. In this study, the combined effects of climatic factors on spring wheat phenophase and grain yield over the past three decades in Inner Mongolia, China, were explored based on the daily climate variables from 1981-2014 and detailed observed data of spring wheat from 1981-2014. Inner Mongolia was divided into three different climate type regions, the eastern, central and western regions. The data were gathered from 10 representative agricultural meteorological experimental stations in Inner Mongolia and analysed with the Agricultural Production Systems Simulator (APSIM) model. First, the performance of the APSIM model in the spring wheat planting areas of Inner Mongolia was tested. Then, the key climatic factors limiting the phenophases and yield of spring wheat were identified. Finally, the responses of spring wheat phenophases and yield to climate change were further explored regionally. Our results revealed a general yield reduction of spring wheat in response to the pronounced climate warming from 1981 to 2014, with an average of 3564 kg·ha-1. The regional differences in yields were significant. The maximum potential yield of spring wheat was found in the western region. However, the minimum potential yield was found in the middle region. The air temperature and soil surface temperature were the optimum climatic factors that affected the key phenophases of spring wheat in Inner Mongolia. The influence of the average maximum temperature on the key phenophases of spring wheat was greater than the average minimum temperature, followed by the relative humidity and solar radiation. The most insensitive climatic factors were precipitation, wind speed and reference crop evapotranspiration. As for the yield of spring wheat, temperature, solar radiation and air relative humidity were major meteorological factors that affected in the eastern and western Inner Mongolia. Furthermore, the effect of the average minimum temperature on yield was greater than that of the average maximum temperature. The increase of temperature in the western and middle regions would reduce the spring wheat yield, while in the eastern region due to the rising temperature, the spring wheat yield increased. The increase of solar radiation in the eastern and central regions would increase the yield of spring wheat. The increased air relative humidity would make the western spring wheat yield increased and the eastern spring wheat yield decreased. Finally, the models describing combined effects of these dominant climatic factors on the maturity and yield in different regions of Inner Mongolia were used to establish geographical differences. Our findings have important implications for improving climate change impact studies and for local agricultural production to cope with ongoing climate change.