Maize Yield Gaps Research Articles

Agro-technology for climate-smart agriculture and resilience to climate extremes in sub-Saharan Africa

Agro-technologies such as irrigation and new crop varieties can reduce climate risk for agricultural production in sub-Sahara Africa (SSA). SSA has the highest maize yield gaps globally, despite its importance as a staple crop in the region. Reducing maize yield gaps is key to tackling food insecurity; however, closing yield gaps might imply an increased greenhouse gas (GHG) emission cost. Climate smart agriculture (CSA) seeks to minimise this cost whilst maximising productivity and resilience. One key element of CSA is resilience to extreme events, although this is rarely examined. Accordingly, we assess the climate smartness of contrasting agro-technology and climate scenarios to assess both resilience to extremes and the overall climate smartness of the scenarios. We use simulations from an existing integrated modelling framework for Malawi, Tanzania, and Zambia, centred on 2050. Four scenarios were examined, defined by combinations of high vs. low agro-technology adoption and high vs. low climate risk (RCP2.6 and RCP8.5). We calculated a climate smartness index (CSI) to the model outputs that quantify the trade-offs between greenhouse gas emissions and agricultural productivity. CSI scores showed that the increase in GHG emissions from improved agro-technology is compensated for the yield benefits. Agro-technology in SSA can therefore benefit the pillars of climate-smart agriculture, namely increased mitigation, adaptation, and productivity. Further, we show that improved maize varieties and irrigation can substantially reduce future yield shocks and enhance resilience to climate change extremes in SSA, pointing to best-bets for agro-technology adoption. Irrigation reduces mid-century yield shocks by 64% (RCP2.6) or 42% (RCP8.5). When combined with improved maize varieties, irrigation removes the majority of yield shocks (90%) in RCP8.5. We therefore conclude that: (i) irrigation has significant potential to increase resilience in SSA; and (ii) investment in strategies to improve crop varieties is critical if the benefits or irrigation are to be fully realized under an RCP8.5 future.

The Formal Seed System of Maize in Ethiopia: Implications for Reducing the Yield Gap

Smallholder farmers’ access to and use of quality seeds of improved varieties and hybrids is an integral component of sustainable maize production and the reduction of the yield gap in Ethiopia. Formal seed supply systems play a pivotal role in ensuring farmers’ access to good quality seed and increasing the productivity of maize. However, the contribution of the formal seed system to the increase in the average national maize yield in the country is not well documented. This paper specifically analyzes the formal seed supply systems and contributions of improved seed to maize productivity increase in Ethiopia. Secondary sources of information, including published and unpublished data, were used. Analysis of the seed value chain from research to commercial seed producers indicated that public and private maize hybrids dominate the formal seed system. Increased use of improved seed and associated management practices has contributed to the yield increase at the national level. For every 10,000 MT of additional maize-improved seed used by smallholder farmers, the national average maize yield increased by 400 kg per hectare (R sq. = 0.59). Hence, in addition to other yield-increasing technologies and extension support, ensuring the availability of improved seed to farmers can significantly contribute to reducing the maize yield gap between the national average yield and the average yield from on-farm demonstrations already attained by farmers in Ethiopia. Various policy interventions are suggested to improve the maize seed system performance and increase the supply of maize improved seeds.

Impact of integrated soil fertility management on maize yield, yield gap and income in northern Ghana

This paper analyzes the impact of integrated soil fertility management (ISFM) on maize yield, yield gap and net income in northern Ghana using an inverse-probability-weighted regression adjustment (IPWRA). In this study, ISFM is restricted to the adoption of crop rotation (CR), inorganic fertilizer (FT), and farmyard manure (MN) either in isolation or in combination. We find a synergy in yield gain (86.52 % increase in yield) and a decrease in yield gap (of 10.22 %) with the adoption of all three technologies as a package (CR+FT+MN). The joint adoption of all three technologies is also associated with a 51.29 % increase in net income from maize production and has a Benefit-Cost Ratio of 3.23.

Assessing Growth, Productivity, Profitability and Yield Gaps of Spring Maize Using Site Specific Nutrient Management Approaches in Kailali

A field experiment was conducted during spring season of 2023 at the sub-tropical climate of Tikapur, Kailali to evaluate the impact of site-specific nutrient management on growth, productivity, profitability and yield gaps of spring maize. The experiment followed a Randomized Complete Block Design (RCBD) with six treatment combinations and four replications. Maize seeds were sown in four different farms on 27 February, 2023 with different site-specific nutrient management (SSNM) approaches viz. with Nutrient Expert®-Maize recommended dose (T1), three omission plots i.e., Omission of Nitrogen (T2), Omission of Phosphorus (T3), Omission of Potassium (T4), Government Recommended Dose (T5), and Farmer’s Fertilizer Practices (T6). Using the Nutrient Expert® Maize model Computer Software, the SSNM+NE dose was recommended via a household survey in the respective farmer's field before the start-up of the experimentation. Various growth parameters, yield, yield attributing traits, and total cost of production of spring maize were recorded and subjected to analysis using Duncan's Multiple Range Test (DMRT) in R-studio. The results revealed that the highest grain yield (3.71 t ha-1) was achieved with SSNM+NE, which was statistically at par with the Government Recommendation Dose (3.12 t ha-1). Moreover, SSNM+NE showed significantly higher thousand grains weight (442.75 g) and straw yield (6.81 t ha-1), respectively. Notably, the straw yield, grain yield, and thousand grain weight of SSNM+NE were statistically comparable to those obtained with the government recommendation. Economic analysis showed higher benefit-cost ratio as well as increased net and gross returns in SSNM+NE as compared to the other nutrient management practices. The SSNM+NE and Governmental Recommended Fertilizer based treatments were about 231% and 178% advantageous over the Farmer’s Fertilizer Practices. It could be suggested that SSNM+NE would the best approach for increasing the growth, productivity and profitability of spring maize in the sub-tropical terai of Far Western Nepal.

Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources

Improved agricultural production systems, together with increased grain yield, are essential to feed the growing global population in the 21st century. Global gridded crop models (GGCMs) have been extensively used to assess crop production and yield simulation on a large geographical scale. However, GGCMs are less effective when they are used on a finer scale, significantly limiting the precision in capturing the yearly maize yield. To address this issue, we propose a relatively more advanced approach that downsizes GGCMs by combining machine learning and crop modeling to enhance the accuracy of maize yield simulations on a regional scale. In this study, we combined the random forest algorithm with multiple data sources, trained the algorithm on low-resolution maize yield simulations from GGCMs, and applied it to a finer spatial resolution on a regional scale in China. We evaluated the performance of the eight GGCMs by utilizing a total of 1046 county-level maize yield data available over a 30-year period (1980–2010). Our findings reveal that the downscaled models created for maize yield simulations exhibited a remarkable level of accuracy (R2 ≥ 0.9, MAE < 0.5 t/ha, RMSE < 0.75 t/ha). The original GGCMs performed poorly in simulating county-level maize yields in China, and the improved GGCMs in our study captured an additional 17% variability in the county-level maize yields in China. Additionally, by optimizing nitrogen management strategies, we identified an average maize yield gap at the county level in China ranging from 0.47 to 1.82 t/ha, with the south maize region exhibiting the highest yield gap. Our study demonstrates the high effectiveness of machine learning methods for the spatial downscaling of crop models, significantly improving GGCMs’ performance in county-level maize yield simulations.

Potential contribution of agronomic practices and conservation agriculture towards narrowing smallholders’ yield gaps in Southern Africa: lessons from the field

Summary Smallholders in Southern Africa continue to grapple with low maize productivity despite this being the staple food crop. This study sought to analyze and isolate the relative contribution of agronomic practices to maize yields obtained by smallholders in Malawi and Mozambique using data generated from on-farm trials testing the performance of conservation agriculture cropping systems. The trials were implemented in two communities, namely Kasungu district in Malawi and Sussundenga district in Mozambique, and ran for seven consecutive growing seasons starting in 2010–2011. Maize yield was measured annually in the on-farm trials, which included a ‘control treatment’ representing an improved farm practice, and in neighboring fields managed by the same farmers on their own, hence representing a ‘true farm practice’. Results indicated that maize yield increased linearly with increasing plant population at harvest at both sites. On average, an increase in plant population at harvest by 1000 plants ha–1 resulted in an increase in maize yield of 90 and 63 kg ha–1 at Kasungu and Sussundenga, respectively. The greatest maize yields were obtained when plant population at harvest exceeded 40 000 plants ha–1. Yet, the plant population at harvest was below the generally recommended optimum for most of the cropping systems studied and in most growing seasons. Furthermore, the use of agronomic practices alone without conservation agriculture (i.e., improved varieties, fertilizer management, and timely weed control) resulted in maize yield gains of as much as 54% and 43% relative to the ‘true farm practice’ at Kasungu and Sussundenga, respectively. Overall, the proportion of these yield increases relative to the ‘true farm practice’ accounted for by agronomic practices amounted to 53–70% and 57–85% at Kasungu and Sussundenga for the highest to the lowest-yielding cropping system. Although conservation agriculture significantly improved maize yield at both sites, such increases were smaller in magnitude compared to the yield gains derived from improved agronomic practices. The study suggests that considerable strides toward narrowing maize yield gaps in Southern Africa can be achieved through improvement of current crop management practices, let alone adhering to the conservation agriculture principles of minimum tillage, residue retention, and crop diversification.

A decade of maize yield gap studies in sub-Saharan Africa: how are farm-level factors considered?

ABSTRACT The study of yield gaps has become more complex, prompting the use of varied approaches to measure yields and a wider range of factors to explain these gaps. In the Global North, the focus is on precision farming, whereas in sub-Saharan Africa (SSA), a broader perspective is necessary due to pronounced variability in farmland conditions. While biogeophysical and management factors have been traditional focal points in yield gap analyses, socio-economic and institutional factors are increasingly recognized as significant, especially in SSA. This review synthesizes research from the past decade in SSA that integrates biogeophysical, management, farm characteristics, and institutional factors in yield gap discussions. The findings indicate a slow shift in including socio-economic factors, with management, particularly nutrient supply and crop management, remaining predominant. However, there is a growing trend towards methodological diversity, such as the adoption of remote sensing and GIS in recent years. Case studies from Kenya and Ghana, utilizing field surveys, interviews, panel data, and spatial analysis, highlight how a multifaceted approach can enhance our understanding of the various elements influencing maize yield gaps in SSA.

Agronomic assessment of the yield variability and yield gap of maize in Bhutan

Maize is the staple food crop in Bhutan, which has not achieved national food self-sufficiency. On-farm assessment of yield variability would provide insights into the priorities for Bhutan's maize development program. Here, we conducted three studies in Bhutan: a household survey, on-station experiment, and on-farm monitoring. First, we interviewed 100 households and collected information on maize crop management options and farming characteristics. Second, we evaluated maize growth at two research stations in different elevation zones (640 and 1,700 ​m a.s.l.). Third, we harvested maize from 25 farm fields at low and high elevations. The gaps between potential yield (with the best management practices at the research stations) and average farm yield and between the best and average farm yields were 53% and 23%, respectively, at the low elevation, and 23% and 20%, respectively, at the high elevation. The classification and regression tree (CART) model showed that field location (distance from the farmer's home), seed source (certified vs. self-produced), and the number of household members involved in farming were the key farming characteristics that affected yield variability, and the manure application regime, urea application, sowing method, and weeding frequency were key management practices. Our results suggest that future research should clarify the most suitable sowing methods and nutrient and weed management regimes, and identify optimal cultivars for each elevation zone, with the goal of developing crop management guidelines for smallholder farmers in Bhutan.

Assessing changes in potential yields and yield gaps of summer maize in the North China Plain

AbstractIn recent years, China's crop production growth rate has decreased. This has provoked questions on whether the grain yields have reached their potential. Taking the North China Plain (NCP) as the study area, this study first simulated the potential yield of an irrigated maize crop with the WOFOST model, using daily weather data for 1960–2017. The yield gap was quantified for 1998–2017, and finally, the total potential maize production was estimated. The results indicate that during 1960–2017, the average potential yield for the region varied annually from 9.5 to 13.5 t ha−1, showing an obvious downward trend at a rate of 37.6 kg ha−1 per year. The long‐term mean potential yield at all 327 counties ranged from 9.9 to 13.7 t ha−1, increasing from south to north. The annual maize yield gap was between an average of 2.7–6.0 t ha−1 (29.0%–51.3%) of the potential yield for the NCP during 1998–2017. At county‐level, the yield gap varied between 1.9 and 7.8 t ha−1 and exceeded 5.0 t ha−1 for 99 counties (30.3% of all counties). Attainable yields can be increased by 0.1–5.2 t ha−1 at county‐level, and by 2.2 t ha−1 for the whole NCP, implying that the maize production has the potential to increase with 20.8 Mt. To increase maize production, the focus areas should be the southcentral NCP, which have a larger yield gap during 2012–2017.

Filling the maize yield gap based on precision agriculture – A MaxEnt approach

Precision agriculture (PA) and yield gap (Yg) analysis are promising strategies to achieve the desired sustainable intensification of agricultural production systems. Current crop Yg approaches do not consider the internal field yield variability caused by soil properties. Topographic and edaphic characteristics causing consistent high and low yield patterns in time and space can be interpreted as an ecological niche and used as proxies for potential yield (Yp) and Yg. Ecological niche models (ENMs) are statistical models originally developed to forecast a species’ niche. However, its application to analyse crop yield spatio-temporal variability has never been made. This study aimed to fill this void by developing a novel approach: i) to quantify the magnitude and spatio-temporal distribution of Yp and Yg, ii) to identify the main factors that cause the Yg, and iii) to provide statistical and agronomical interpretation of the data to reduce the Yg. We performed this work using high-resolution maize yield maps from three seasons, with an ancillary dataset composed of soil electrical conductivity, soil properties and digital elevation models provided by “Quinta da Cholda”, Portugal. The yield maps were averaged, resulting in a standardised multiyear yield map. The 90th and 10th yield percentiles were interpreted as proxies for Yp and Yg, and analysed by an ENM machine learning algorithm – maximum entropy (MaxEnt). The average Yg and Yp were quantified as 1.5 and 19.1 ton/ha. Yp was characterised by having silty, richer soils and lower elevations, with several nutritional factors above the critical limits to maintain higher yields. Yg had loam soils coupled with higher relative elevations and lower nutrition content. This innovative modelling approach can efficiently manage high-dimensional spatio-temporal data to support advanced PA solutions, allowing detailed support for narrowing the Yg.

Adverse Weather Impacts on Winter Wheat, Maize and Potato Yield Gaps in northern Belgium

Adverse weather conditions greatly reduce crop yields, leading to economic losses and lower food availability. The characterization of adverse weather and the quantification of their potential impact on arable farming is necessary to advise farmers on feasible and effective adaptation strategies and to support decision making in the agriculture sector. This research aims to analyze the impact of adverse weather on the yield of winter wheat, grain maize and late potato using a yield gap approach. A time-series analysis was performed to identify the relationship between (agro-)meteorological indicators and crop yields and yield gaps in Flanders (northern Belgium) based on 10 years of field trial and weather data. Indicators were calculated for different crop growth stages and multiple soils. Indicators related to high temperature, water deficit and water excess were analyzed, as the occurrence frequency and intensity of these weather events will most likely increase by 2030–2050. The concept of “yield gap” was used to analyze the effects of adverse weather in relation to other yield-reducing factors such as suboptimal management practices. Winter wheat preferred higher temperatures during grain filling and was negatively affected by wet conditions throughout the growing season. Maize was especially vulnerable to drought throughout the growing season. Potato was more affected by heat and drought stress during tuber bulking and by waterlogging during the early growth stages. The impact of adverse weather on crop yield was influenced by soil type, and optimal management practices mitigated the impact of adverse weather.

Narrowing maize yield gaps across smallholder farming systems in Zambia: what interventions, where, and for whom?

Maize production in Zambia must increase with a view towards improved food security and reduced food imports whilst avoiding cropland expansion. To achieve this, it is important to understand the causes behind the large maize yield gaps observed in smallholder farming systems across the country. This is the first study providing a yield gap decomposition for maize in Zambia, and combining it with farm typology delineation, to identify the key limiting factors to maize yield gaps across the diversity of farms in the country. The analysis builds upon a nationally representative household survey covering three growing seasons and crop model simulations to benchmark on-farm maize yields and N application rates. Three farm types were delineated, including households for which maize is a marginal crop, households who are net buyers of maize, and households who are market-oriented maize producers. Yield gap closure was about 20% of the water-limited yield, corresponding to an actual yield of 2.4 t ha− 1. Market-oriented maize farms yielded slightly more than the other farm types, yet the drivers of yield variability were largely consistent across farm types. The large yield gap was mostly attributed to the technology yield gap indicating that more efficient production methods are needed to raise maize yields beyond the levels observed in highest yielding fields. Yet, narrowing efficiency and resource yield gaps through improved crop management (i.e., sowing time, plant population, fertilizer inputs, and weed control) could more than double current yields. Creating a conducive environment to increase maize production should focus on the dissemination of technologies that conserve soil moisture in semi-arid areas and improve soil health in humid areas. Recommendations of sustainable intensification practices need to consider profitability, risk, and other non-information constraints to improved crop management and must be geographically targeted to the diversity of farming systems across the country.

An integrative approach based on crop modeling and geospatial and statistical analysis to quantify and explain the maize (Zea mays) yield gap in Ghana

In Ghana, maize (Zea mays) is a crop crucial to achieving food and nutrition security. Maize consumption has increased exponentially over the past decades and contributes to 25% of the caloric consumption in the country. In order to assist in decision-making and guide investment in sustainable intensification of maize production, this study set out to identify the determinants of yield and to arrive at potential interventions for closing the maize yield gap. These were quantified using analytical approaches that combine a light use efficiency crop model (LINTUL-1) with statistical and geospatial analyses. Legacy data, auxiliary covariables, and maize fertilizer trials on eight experimental stations in Ghana were used in this study. Overall, the maize yield gap across the stations and trial treatments ranged from 17% to 98%. The variation in yield gap within a single station indicates a significant scope for closing the yield gap through site-specific nutrient management. Multiple linear regression models that explained 81% of the variability in maize yield gap identified soil organic matter, soil water-holding capacity, root zone depth, rainfall, sulfur fertilizer, and nitrogen fertilizer, in that order of importance, as the major determinants for closing the yield gap in the major agroecological zones of Ghana. The yield gap decreased by 1.4 t ha-1 with a 1% increase in soil organic matter. A 1 mm increase of the soil water-holding capacity reduced the yield gap by 1.06 t ha-1, while an increase in pH and in the application of potassium fertilizer widened the gap. These results suggest that both soil physical and chemical properties, together with weather data, should be taken into consideration to arrive at site-specific fertilizer recommendation and other agronomic practices.

Closing yield gap of maize in Southeast Asia by intercropping systems: A review

Maize (Zea may L.) is a cereal crop grown extensively throughout Southeast Asia, particularly in Thailand, Indonesia, Vietnam, and the Philippines. However, maize yield in this region varies yearly due to several factors such as soil infertility, poor management practices, unpredictable weather conditions, and geography. This review aims (i) to demonstrate constraints and mitigation strategies for closing yield gaps and optimizing land use efficiency through the integration of companion crops into maize cropping, based on existing research in Southeast Asia between 1980 and 2021, and (ii) to discuss the benefits and drawbacks of growing maize alongside with other companion crops in Southeast Asia. Based on existing 50 articles of maize-based intercropping conducted in Southeast Asia founded from Google Scholar, Scopus and Web of science, cassava, legumes, potato, rice, grasses, and perennial crops such as rubber and coconut are the most frequently grown as companion crops in this region. Much emphasis of maize-based intercropping is paid to erosion control. Intercropping maize with other crops can successfully minimize soil erosion, runoff, and maintain the topsoil fertility. Therefore, less fertilizer input is required in intercropping system, which can also have a positive influence on the off-site environment. In addition, we discovered that maize-based intercropping improves the Land Equivalent Ratio (LER) (LER > 1) when planting time, crop spacing, and management practices are appropriately implemented. On the other hand, there are just a few studies that indicate the restrictions associated with maize-based intercropping (LER < 1). While it is theoretically beneficial to grow maize alongside with legumes due to biological nitrogen fixation, research undertaken in this region has not confirmed this statement when maize is intercropped with rice bean. Although, maize-based intercropping in Southeast Asia had been more thoroughly investigated in a variety of ways, further research is still needed to determine how maize-based intercropping mitigates the impact of climate change

A sustainable approach to narrowing the summer maize yield gap experienced by smallholders in the North China Plain

CONTEXTThe maize production sector in the North China Plain (NCP) is facing the dual pressures of having to increase its grain yield and improve its environmental sustainability. Evaluating the yield gap and ascertaining the factors that limit the grain yield might suggest new approaches to address these challenges. OBJECTIVEThe objective of this study was to estimate the yield potential and the yield gap associated with smallholdings in the NCP using a hybrid method; to determine the factors driving the grain yield and corresponding sustainability indicators; and to evaluate to what degree the introduction of scientifically developed high-yield and high-efficiency agronomic practices (HH) to smallholders might help to sustainably narrow the exploitable yield gap. METHODSCombined with diagnostic smallholder survey and crop modeling exercise to characterize and decompose yield gaps. With survey data, we assessed the existing variation with respect to various key performance indicators and carried out a number of controlled comparative experiments at farms (HH vs traditional farming practices (FP)) in multiple locations. RESULTS AND CONCLUSIONSOur work revealed the total and exploitable yield gap were 4.3 t ha−1 and 1.8 t ha−1 for rainfed summer maize, and 4.4 t ha−1 and 1.9 t ha−1 for irrigated summer maize across the entire NCP, indicating a large potential for yield improvements. The main factors driving the yield from the entire region were the planting density, the fertilization frequency, and the irrigation frequency. It should be noted that farmers whose yield was in the top 25% bracket of the yield distribution also achieved relatively high utilization efficiencies with respect to N, P, and K fertilizers (15%, 16%, and 16% above the average values, respectively) while their profit was 41% above average. Comparative experiments conducted over several seasons support insights from surveys by demonstrating that major gains in grain yield (+30%), the fertilizer utilization efficiency (+37% in N partial factor productivity, +48% in P partial factor productivity, and + 32% in K partial factor productivity) and economic profit (+62%) are achievable through the adoption of HH. SIGNIFICANCEThrough a hybrid method, our results suggest that agricultural intensification can be achieved in the NCP without having to trade off yield against environmental sustainability. Evidently, smallholders can simultaneously improve their maize grain yield, fertilizer utilization efficiency, and profit by adopting straightforward non-disruptive optimizations of their agronomic practices.

Estimation of productivity yield gap contributions of climate change variability in selected horticultural crops (fresh maize and okra) in Anambra State, Nigeria

Most horticultural crops are susceptible to climate extremes that affect vegetative growth and yield. Climate variability poses a great threat to food security and the sustainability of the agricultural sector in Nigeria. However, the study estimated the productivity yield gap contributions of climate variability among maize and okra farmers. Data was collected from a cross-section of 160 farmers. Statistical tools such as descriptive statistics, second-order quadratic and linear regression models and Chi-square were utilized to operationalize the study objectives. The study established that high temperature was the highest climate variability (okra - 80.0%, and maize - 78.7%); whereas fug was lowest with 38.3% (okra) and 30.4% (maize). Again, flood contributed to the highest (27.5% - okra, and 36.5% - maize) yield gap. All the climate variability cumulatively explained 6.49 ton/ha (maize) and 3.47 ton/ha (okra) yield gap. However, the farmers are producing 2.26 ton/ha (maize) and 1.70 ton/ha (okra) below their optimal level. For better management, the study revealed that sex, age, education, household size, and cooperative membership are responsible for okra farmers; while age, marital status, and farming experience are responsible for maize farmers’ managerial ability. The study, therefore, concludes that climate variability significantly reduced horticultural crop yield. Howbeit, the study recommends that farmers should be taught to adopt climate-smart agricultural practices.

Assessment of maize yield losses due to the effect of fall armyworm (Spodoptera frugiperda) infestation: the case of Trans-Nzoia County, Kenya

Invasive alien species have emerged as a production constraint, the most recent in Kenya being fall armyworm (FAW) (Spodoptera frugiperda). The pest incidence has resulted in farmer welfare damage by exacerbating the maize yield gap, a principal staple crop in Kenya. Through a field survey, 257 farmers were interviewed. Farmer perceived direct estimates and an iterative bidding procedure were used to assess maize yield losses. The majority of farmers (99%) were aware of fall armyworm and could correctly identify it. Moreover, most of the farmers (94%) ranked fall armyworm as the major pest for the 2018 cropping season, with more than 43% of the farmers describing the resulting yield loss as high. Yield loss estimates ranged between 0.18 to 2.7 tonnes per acre. Farmers adopted a combination of control strategies, to mitigate the effect of FAW. We conclude that FAW is a problematic pest in Kenya, based on the observed significant losses due to this pest. To inform policy on appropriate and prioritization of resources to manage FAW, yield loss estimates are key.

Spatial and temporal variations of maize and wheat yield gaps and their relationships with climate in China

With the growth in population over the past decades, China’s food demand has been increasing. However, crops are facing greater water stress with the rising temperature and increasing drought in future. Therefore, identifying potential crop yield and its driving factors is necessary to ensure continued food production. In the present study, we estimated the potential yields of maize and wheat in China, with and without water limits, from 1985 to 2015 using a simple generic crop model (SIMPLE). We then investigated how climate factors (temperature, precipitation, and solar radiation) drove the spatial and temporal variation of yield gaps (quantified as the difference between potential yields with and without water limits). We found an increasing trend in the water limited yield gap of maize in northern China (3.9 ± 4.3 kg ha-1 yr-2) and wheat in central China (2.6 ± 2.4 kg ha-1 yr-2). The temporal trends of maize and wheat yield gaps in China were mainly related to precipitation and temperature. However, the spatial variation in maize yield gap trends was mainly related to the variations in precipitation, whereas for wheat it was attributed to changes in temperature or solar radiation. Upgrading irrigation infrastructure and management techniques or spatially adjusting crop types could help closing the yield gap of wheat in some zones.

Optimized agronomic management practices narrow the yield gap of summer maize through regulating canopy light interception and nitrogen distribution

Optimizing the canopy nitrogen (N) distribution by matching the available light resources to construct a light-N matching canopy structure of summer maize is important to improve yield and narrow the yield gap (YG). A field experiment with four integrated agronomic management practice systems [a control treatment (T1), an improved management system (T2), a super high-yield management system (T3), and an optimized management system (T4)] was conducted at Da Wenkou Town, Tai'an, China, from June to October in -2018–2020 to study the canopy light and N distribution and photosynthetic N use efficiency of summer maize. The results showed that optimized integrated agronomic management practices could coordinate canopy light and N distributions to increase the yield, thus narrowing the YG between T1 and yield potential by 1.9–5.2 Mg ha-1. In addition, the T4 treatment intercepted more radiation by adjusting the leaf area. Furthermore, the leaf N content of T4 in the middle canopy was significantly increased. The coordination of canopy light and N distributions of T4 was increased significantly, which was conducive to maximizing the use of N and solar radiation and increasing photosynthetic N use efficiency significantly by 9.4–41.4%, compared to that of T1. As a result, the yield of T4 was increased by 37.0% compared to that of T1. And T4 narrowed the YG by 3.4 Mg ha-1. Therefore, the T4 was the best choice for achieving a high-yield and high-efficiency production of summer maize in this study.

The Yield Gap Maize under Intensive Cropping System in Central Java

The study aimed to estimate the level of the yield gaps of maize in major producing areas, point out the causes of yield gaps in farmers’ maize fields, and identify opportunities to the existing yield gaps through management practices of maize production in Central Java. This is the strategy for closing the existing yield gaps to achieve food self-sufficiency in agricultural land. Methods to estimates the yield gaps should cover data sources on physical conditions (weather and soil), management practices, and smallholder shapes. The relevant methods for estimating actual yields (Ya), potential (Yp), and water-limited (Yw) were compared. The yield gaps of maize under intensive cropping systems in rainfed ecosystems resulted in significant differences in all cultivation situations. The lowland rainfed maize showed Ya, Yp, and Yw values of 5.57, 12.83, and 12.47 ton/ha, respectively. The major causes of the yield gaps include variety, land preparation, and water issues concerned with the limited water inputs.